From 4c83966871819efd32d54861ff9bfbb445e898a2 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Sat, 1 Aug 2020 11:29:09 +0200 Subject: [PATCH 01/13] Add files via upload numpy implementation for linear regression using tensorflow and a data file loaded into a pandas dataframe --- ...nsorFlow_pandasData_loaded_from_file.ipynb | 388 ++++++++++++++++++ 1 file changed, 388 insertions(+) create mode 100644 tensorflow_v2/notebooks/2_BasicModels/linear_regression_tensorFlow_pandasData_loaded_from_file.ipynb diff --git a/tensorflow_v2/notebooks/2_BasicModels/linear_regression_tensorFlow_pandasData_loaded_from_file.ipynb b/tensorflow_v2/notebooks/2_BasicModels/linear_regression_tensorFlow_pandasData_loaded_from_file.ipynb new file mode 100644 index 00000000..98d013f0 --- /dev/null +++ b/tensorflow_v2/notebooks/2_BasicModels/linear_regression_tensorFlow_pandasData_loaded_from_file.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "rng = np.random" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters.\n", + "learning_rate = 0.000001\n", + "training_steps = 10000\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Training Data.\n", + "df=pd.read_csv('./linear_reg_exam_dataset.csv',usecols = [0,1],skiprows = [0],header=None)\n", + "d = df.values\n", + "data = np.float32(d)\n", + "\n", + "dataset = pd.DataFrame({'x': data[:, 0], 'y': data[:, 1]})\n", + "\n", + "\n", + "\n", + "Y = dataset['y'].values.tolist() # define the target variable (dependent variable) as y\n", + "X = dataset['x'].values.tolist()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Weight and Bias, initialized randomly.\n", + "W = tf.Variable(rng.randn(), name=\"weight\")\n", + "b = tf.Variable(rng.randn(), name=\"bias\")\n", + "\n", + "# Linear regression (Wx + b).\n", + "def linear_regression(x):\n", + " return W * x + b\n", + "\n", + "# Mean square error.\n", + "def mean_square(y_pred, y_true):\n", + " return tf.reduce_mean(tf.square(y_pred - y_true))\n", + "\n", + "# Stochastic Gradient Descent Optimizer.\n", + "optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Optimization process. \n", + "def run_optimization():\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = linear_regression(X)\n", + " loss = mean_square(pred, Y)\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, [W, b])\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, [W, b]))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 50, loss: 37.193291, W: 75.018303, b: -0.207481\n", + "step: 100, loss: 37.192348, W: 75.018303, b: -0.207177\n", + "step: 150, loss: 37.201679, W: 75.018303, b: -0.206872\n", + "step: 200, loss: 37.200729, W: 75.018303, b: -0.206567\n", + "step: 250, loss: 37.197868, W: 75.018303, b: -0.206262\n", + "step: 300, loss: 37.192211, W: 75.018303, b: -0.205958\n", + "step: 350, loss: 37.191154, W: 75.018303, b: -0.205653\n", + "step: 400, loss: 37.188828, W: 75.018303, b: -0.205348\n", + "step: 450, loss: 37.187111, W: 75.018295, b: -0.205043\n", + "step: 500, loss: 37.186165, W: 75.018295, b: -0.204739\n", 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"step: 9250, loss: 36.864887, W: 75.018219, b: -0.151565\n", + "step: 9300, loss: 36.859253, W: 75.018219, b: -0.151262\n", + "step: 9350, loss: 36.858208, W: 75.018219, b: -0.150959\n", + "step: 9400, loss: 36.855892, W: 75.018219, b: -0.150655\n", + "step: 9450, loss: 36.854252, W: 75.018219, b: -0.150352\n", + "step: 9500, loss: 36.853313, W: 75.018219, b: -0.150049\n", + "step: 9550, loss: 36.850887, W: 75.018219, b: -0.149746\n", + "step: 9600, loss: 36.849945, W: 75.018219, b: -0.149442\n", + "step: 9650, loss: 36.843784, W: 75.018219, b: -0.149139\n", + "step: 9700, loss: 36.841476, W: 75.018219, b: -0.148836\n", + "step: 9750, loss: 36.840431, W: 75.018219, b: -0.148533\n", + "step: 9800, loss: 36.840061, W: 75.018219, b: -0.148229\n", + "step: 9850, loss: 36.837215, W: 75.018219, b: -0.147926\n", + "step: 9900, loss: 36.836273, W: 75.018219, b: -0.147623\n", + "step: 9950, loss: 36.830936, W: 75.018211, b: -0.147320\n", + "step: 10000, loss: 36.830002, W: 75.018211, b: -0.147016\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step in range(1, training_steps + 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization()\n", + " \n", + " if step % display_step == 0:\n", + " pred = linear_regression(X)\n", + " loss = mean_square(pred, Y)\n", + " print(\"step: %i, loss: %f, W: %f, b: %f\" % (step, loss, W.numpy(), b.numpy()))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Graphic display\n", + "plt.plot(X, Y, 'ro', label='Original data')\n", + "plt.plot(X, np.array(W * X + b), label='Fitted line')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From ed0c51fe189749870fd9f7b4ace97bba0cac9ea4 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Mon, 3 Aug 2020 22:48:51 +0200 Subject: [PATCH 02/13] Add files via upload This is a multillayer perceptron with live data stats when performing analysis --- .../Multilayer_Perceptron.ipynb | 456 ++++++++++++++++++ 1 file changed, 456 insertions(+) create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb new file mode 100644 index 00000000..d35f4de4 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow v2.\n", + "\n", + "This example is using a low-level approach to better understand all mechanics behind building neural networks and the training process.\n", + "\n", + "- Author: Miguel Tomás\n", + "- Project: https://github.com/aeonSolutions/TensorFlow-Examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "This example is using a file csv dataset. \n", + "\n", + "In this example, each dataset will be converted to float32, normalized to [0, 1] and flattened to a 1-D array of \"num_features\" features \n", + "\n", + "More info: https://github.com/aeonSolutions/TensorFlow-Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Visualize predictions.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# parameters initialization.\n", + "num_classes = 0 # total classes : number of output varibales\n", + "num_features = 1 # data features : number of input variables > load from the dataset bellow\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 1000\n", + "batch_size = 256\n", + "display_step = 100\n", + "\n", + "# Network parameters.\n", + "n_hidden_1 = 64 # 1st layer number of neurons.\n", + "n_hidden_2 = 64 # 2nd layer number of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make predictions Data.\n", + "df_predict_ds=pd.read_csv('./week3_exam_dataset_test.csv')\n", + "\n", + "data_predict_x = np.float32(df_predict_ds.values)\n", + "\n", + "# Training Data.\n", + "df_tr=pd.read_csv('./week3_exam_dataset_train.csv')\n", + "\n", + "df_tr_raw_y= df_tr['y']\n", + "num_classes= df_tr_raw_y.shape[0]\n", + "if num_features==0:\n", + " num_features= df_tr_raw_y.shape[0]\n", + "\n", + "df_tr_raw_values_y = df_tr_raw_y.values\n", + "data_tr_y = np.float32(df_tr_raw_values_y)\n", + "\n", + "df_tr_raw_x= df_tr.drop('y',1)\n", + "df_tr_raw_values_x = df_tr_raw_x.values\n", + "data_tr_x = np.float32(df_tr_raw_values_x)\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(df_tr_raw_x, df_tr_raw_y, test_size=0.33, random_state=42)\n", + "\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Convert to float32.\n", + "y_train, y_test = np.array(y_train, np.float32), np.array(y_test, np.float32)\n", + "\n", + "# Normalize data values to [0, 1] interval.\n", + "maxVal=max(np.amax(x_train),np.amax(x_test))\n", + "x_train, x_test = x_train / maxVal, x_test / maxVal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TF Model.\n", + "class NeuralNet(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(NeuralNet, self).__init__()\n", + " # First fully-connected hidden layer.\n", + " self.fc1 = layers.Dense(n_hidden_1, activation=tf.nn.relu)\n", + " # First fully-connected hidden layer.\n", + " self.fc2 = layers.Dense(n_hidden_2, activation=tf.nn.relu)\n", + " # Second fully-connecter hidden layer.\n", + " self.out = layers.Dense(num_classes)\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " x = self.fc1(x)\n", + " x = self.fc2(x)\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build neural network model.\n", + "neural_net = NeuralNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Accuracy metric.\n", + "def accuracyAvg(y_pred):\n", + " print(y_pred.numpy().shape)\n", + " \n", + " #convert to 1D array\n", + " y_pred_1d_array= y_pred.ravel()\n", + " \n", + " accCalc= np.full(y_pred_1d_array.shape, 0)\n", + " delta=np.amax(real_y_1d_array)-np.amin(real_y_1d_array)\n", + " \n", + " for i in range(len(y_pred_1d_array)):\n", + " accCalc[i]= abs(delta-y_pred_1d_array[i] - real_y_1d_array[i])\n", + " \n", + " return accCalc\n", + "\n", + "# Stochastic gradient descent optimizer.\n", + "optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = neural_net(x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = neural_net.trainable_variables\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from IPython.display import clear_output\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib inline\n", + "from datetime import datetime\n", + "from datetime import timedelta\n", + "\n", + "def live_plot(steps, accuracy, figsize=(7,5), title=''):\n", + " clear_output(wait=True)\n", + " plt.figure(figsize=figsize)\n", + " plt.xlim(0, training_steps)\n", + " plt.ylim(0, 100)\n", + " steps= [float(i) for i in steps]\n", + " accuracy= [float(i) for i in accuracy]\n", + " \n", + " if len(steps) > 1:\n", + " plt.scatter(steps,accuracy, label='accuracy', color='k') \n", + " m, b = np.polyfit(steps, accuracy, 1)\n", + " plt.plot(steps, [x * m for x in steps] + b)\n", + "\n", + " plt.title(title)\n", + " plt.grid(True)\n", + " plt.xlabel('epoch')\n", + " plt.ylabel('accuracy %')\n", + " #plt.legend(loc='center left') # the plot evolves to the right\n", + " plt.show();\n", + " \n", + "def ETC(start, steps):\n", + " time_elapsed = datetime.now() - start\n", + " eta= (training_steps-steps) / display_step * time_elapsed\n", + " #avgString = str(avg).split(\".\")[0]\n", + " \n", + " hours= int(eta.seconds/3600)\n", + " minutes= int((eta.seconds/60)-hours*60)\n", + " seconds = int(eta.seconds - minutes*60 -hours*3600)\n", + " return \"%sh, %s min and %s sec\" % (hours, minutes, seconds)\n", + "\n", + "def elapsedTime(elapsed):\n", + " hours= int(elapsed.seconds/3600)\n", + " minutes= int((elapsed.seconds/60)-hours*60)\n", + " seconds = int(elapsed.seconds - minutes*60 -hours*3600)\n", + " return \"%sh, %s min and %s sec\" % (hours, minutes, seconds)\n", + "\n", + "def progress(percent=0, width=30):\n", + " left = width * percent // 100\n", + " right = width - left\n", + " print('\\r[', '#' * left, ' ' * right, ']',\n", + " f' {percent:.0f}%\\n',\n", + " sep='', end='', flush=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[##############################] 100%\n", + "\n", + "================= Iteration Stats ================\n", + " step: 1000 of 1000\n", + " loss: 0.592776\n", + " accuracy: 79.30 %\n", + " AVG: 75.23 %\n", + "\n", + "================= Time ================\n", + " Elapsed: 0h, 3 min and 54 sec\n", + " ETC: 0h, 0 min and 0 sec\n", + "\n", + "================== Network Setup ================\n", + " number of classes: 32460\n", + " number of features: 1\n", + " learning rate: 0.001\n", + " training steps: 1000\n", + " batch size: 256\n", + "1st layer n. of neurons: 64\n", + "2st layer n. of neurons: 64\n", + "\n", + "Analysis finished.\n", + "Final Average accuracy is 75.23 %\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "avgCounter=0\n", + "avg=0.0\n", + "\n", + "steps=[]\n", + "accuracyValue=[]\n", + "\n", + "start_time = datetime.now()\n", + "totalStartTime=start_time\n", + " \n", + "live_plot([0], [0])\n", + "print(\"analysis started. Waiting for preliminary data. One moment please...\")\n", + "progress(0) \n", + "\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = neural_net(batch_x, is_training=True)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " avgCounter+=1\n", + " avg+=acc*100\n", + " \n", + " steps.append(step)\n", + " accuracyValue.append(acc*100)\n", + " live_plot(steps, accuracyValue)\n", + " totalTime =datetime.now()- totalStartTime\n", + " \n", + " if int(step/training_steps*100)<100:\n", + " print(\"Running...\")\n", + " progress(int(step/training_steps*100)) \n", + " print(\"\")\n", + " print(\"================= Iteration Stats ================\")\n", + " print(\" step: %i of %i\" % (step,training_steps))\n", + " print(\" loss: %f\" % loss)\n", + " print(\" accuracy: %.2f %%\" % (acc*100))\n", + " print(\" AVG: %.2f %%\" % (avg/avgCounter))\n", + " print(\"\")\n", + " print(\"================= Time ================\")\n", + " print(\" Elapsed: \" + elapsedTime(totalTime))\n", + " print(\" ETC: \" + ETC(start_time,step))\n", + " print(\"\")\n", + " print(\"================= Network Setup ================\")\n", + " print(\" number of classes: \"+ str(num_classes))\n", + " print(\" number of features: \"+ str(num_features)) \n", + "\n", + " print(\" learning rate: \"+ str(learning_rate))\n", + " print(\" training steps: \"+ str(training_steps))\n", + " print(\" batch size: \"+ str(batch_size))\n", + "\n", + " print(\"1st layer n. of neurons: \"+ str(n_hidden_1 ))\n", + " print(\"2st layer n. of neurons: \"+ str(n_hidden_2))\n", + "\n", + " start_time = datetime.now()\n", + "print(\"\")\n", + "print(\"Analysis finished.\") \n", + "#print(\"Final Average accuracy is %.2f %%\" % (avg/avgCounter))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# +++++++++++++++++++++++++++++++++++++++++++++++++++\n", + "#!!! code bellow this line is not yet finished !!!\n", + "# +++++++++++++++++++++++++++++++++++++++++++++++++++\n", + "\n", + "# Test model on validation set.\n", + "print(x_test.shape)\n", + "\n", + "pred = neural_net(x_test, is_training=False)\n", + "\n", + "print(\"Accuracy of highest score in prediction vector\")\n", + "print(\" Test Accuracy: %.2f %%\" % (tf.math.round(100*accuracy(pred, y_test))))\n", + "print(\"\")\n", + "\n", + "prediction= np.round(pred.numpy(),2)\n", + "print(\"Model prediction shape:\" + str(prediction.shape))\n", + "print(\" Model initial shape:\" + str(y_test.shape))\n", + "\n", + "print(\"\")\n", + "print(\"Model prediction value:\")\n", + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = neural_net(data_predict_x)\n", + "print(\"Model prediction: %i\" % np.argmax(predictions.numpy()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " Author: Miguel Tomás \n", + "\n", + " License: Creative Commons \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 6943fa018e6ba1c893c70dae95b9e8765bdcd801 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Tue, 4 Aug 2020 11:02:46 +0200 Subject: [PATCH 03/13] Add files via upload Added some stats and #bug corrections --- .../Multilayer_Perceptron.ipynb | 178 ++++++++++++------ 1 file changed, 124 insertions(+), 54 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb index d35f4de4..410dcd63 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -49,32 +49,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# parameters initialization.\n", - "num_classes = 0 # total classes : number of output varibales\n", - "num_features = 1 # data features : number of input variables > load from the dataset bellow\n", + "num_classes = 100 # total classes : number of output results\n", + "num_features = 0 # data features : number of samples(rows) for the variable set. a value of 0 loads from the dataset bellow\n", "\n", "# Training parameters.\n", - "learning_rate = 0.001\n", - "training_steps = 1000\n", - "batch_size = 256\n", - "display_step = 100\n", + "learning_rate = 0.1\n", + "training_steps = 10000\n", + "batch_size = 100\n", + "display_step = 50\n", + "\n", + "#normalization of data\n", + "normalizeDataValues=True\n", + "normalizationType= \"mean\" # accepts: max, mean\n", + "normalizationTypeBinary=True\n", "\n", "# Network parameters.\n", - "n_hidden_1 = 64 # 1st layer number of neurons.\n", - "n_hidden_2 = 64 # 2nd layer number of neurons." + "n_hidden_1 = 512 # 1st layer number of neurons.\n", + "n_hidden_2 = 512 # 2nd layer number of neurons." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 38)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m38\u001b[0m\n\u001b[1;33m else\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ - "# make predictions Data.\n", + "# predictions Data.\n", "df_predict_ds=pd.read_csv('./week3_exam_dataset_test.csv')\n", "\n", "data_predict_x = np.float32(df_predict_ds.values)\n", @@ -83,10 +97,13 @@ "df_tr=pd.read_csv('./week3_exam_dataset_train.csv')\n", "\n", "df_tr_raw_y= df_tr['y']\n", - "num_classes= df_tr_raw_y.shape[0]\n", + "if num_classes==0:\n", + " num_classes= df_tr_raw_y.shape[0]\n", "if num_features==0:\n", " num_features= df_tr_raw_y.shape[0]\n", - "\n", + "else:\n", + " #TODO: fill possible empty values on the datasets\n", + " \n", "df_tr_raw_values_y = df_tr_raw_y.values\n", "data_tr_y = np.float32(df_tr_raw_values_y)\n", "\n", @@ -102,16 +119,27 @@ "y_train, y_test = np.array(y_train, np.float32), np.array(y_test, np.float32)\n", "\n", "# Normalize data values to [0, 1] interval.\n", - "maxVal=max(np.amax(x_train),np.amax(x_test))\n", - "x_train, x_test = x_train / maxVal, x_test / maxVal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "if normalizeDataValues:\n", + " if normalizationType==\"max\":\n", + " maxVal_train=np.amax(x_train, axis=0)\n", + " maxVal_test=np.amax(x_test, axis=0)\n", + " maxVal_pred=np.amax(data_predict_x, axis=0)\n", + " maxVal=max(np.amax(maxVal_train, axis=0),np.amax(maxVal_test, axis=0),np.amax(maxVal_pred, axis=0))\n", + " if normalizationTypeBinary:\n", + " x_train, x_test,data_predict_x = np.where((x_train / maxVal_train)>=0.5,1,0), np.where((x_test / maxVal_test)>=0.5,1,0), np.where((data_predict_x / maxVal_pred)>=0.5,1,0)\n", + " else\n", + " x_train, x_test, data_predict_x = x_train / maxVal_train, x_test / maxVal_test, data_predict_x / maxVal_pred\n", + " else:\n", + " meanVal_test=np.mean(x_test, axis=0)\n", + " meanVal_train=np.mean(x_train, axis=0)\n", + " meanVal_pred=np.mean(data_predict_x, axis=0)\n", + " meanVal=max(np.amax(meanVal_train, axis=0),np.amax(meanVal_test, axis=0),np.amax(meanVal_pred, axis=0))\n", + " \n", + " if normalizationTypeBinary:\n", + " x_train, x_test,data_predict_x = np.where((x_train / meanVal_train)>=0.5,1,0), np.where((x_test / meanVal_test)>=0.5,1,0), np.where((data_predict_x / meanVal_pred)>=0.5,1,0)\n", + " else\n", + " x_train, x_test, data_predict_x = x_train / meanVal_train, x_test / meanVal_test, data_predict_x / meanVal_pred\n", + "\n", "# Use tf.data API to shuffle and batch data.\n", "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" @@ -119,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -152,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -238,6 +266,7 @@ " steps= [float(i) for i in steps]\n", " accuracy= [float(i) for i in accuracy]\n", " \n", + " m=0\n", " if len(steps) > 1:\n", " plt.scatter(steps,accuracy, label='accuracy', color='k') \n", " m, b = np.polyfit(steps, accuracy, 1)\n", @@ -245,11 +274,12 @@ "\n", " plt.title(title)\n", " plt.grid(True)\n", - " plt.xlabel('epoch')\n", - " plt.ylabel('accuracy %')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Accuracy %')\n", " #plt.legend(loc='center left') # the plot evolves to the right\n", " plt.show();\n", - " \n", + " return m\n", + "\n", "def ETC(start, steps):\n", " time_elapsed = datetime.now() - start\n", " eta= (training_steps-steps) / display_step * time_elapsed\n", @@ -271,17 +301,29 @@ " right = width - left\n", " print('\\r[', '#' * left, ' ' * right, ']',\n", " f' {percent:.0f}%\\n',\n", - " sep='', end='', flush=True)\n" + " sep='', end='', flush=True)\n", + " \n", + "def measureSkewness(series): \n", + " result=\"\"\n", + " \n", + " if (series.skew() > 1 or series.skew() < -1):\n", + " result=\"Highly skewed distribution\"\n", + " elif((0.5 < series.skew() < 1) or (-1 < series.skew() < -0.5)):\n", + " result=\"Moderately skewed distribution\"\n", + " elif(-0.5 < series.skew() < 0.5):\n", + " result=\"Approximately symmetric distribution\"\n", + " result = result +\" ( \" +str(round(series.skew(),2))+\" )\"\n", + " return result\n" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -295,29 +337,51 @@ "name": "stdout", "output_type": "stream", "text": [ - "[##############################] 100%\n", + "Running...\n", + "[# ] 6%\n", "\n", "================= Iteration Stats ================\n", - " step: 1000 of 1000\n", - " loss: 0.592776\n", - " accuracy: 79.30 %\n", - " AVG: 75.23 %\n", + " step: 650 of 10000\n", + " loss: 0.340414\n", + " accuracy: 82.00 %\n", + " AVG: 80.38 %\n", + " Trend slope: 0.000\n", + " MAD Dispersion: nan\n", + " Skewness: Moderately skewed distribution ( -0.82 )\n", "\n", "================= Time ================\n", - " Elapsed: 0h, 3 min and 54 sec\n", - " ETC: 0h, 0 min and 0 sec\n", + " Elapsed: 0h, 0 min and 9 sec\n", + " ETC: 0h, 2 min and 15 sec\n", "\n", - "================== Network Setup ================\n", - " number of classes: 32460\n", - " number of features: 1\n", - " learning rate: 0.001\n", - " training steps: 1000\n", - " batch size: 256\n", - "1st layer n. of neurons: 64\n", - "2st layer n. of neurons: 64\n", - "\n", - "Analysis finished.\n", - "Final Average accuracy is 75.23 %\n" + "================= Network Setup ================\n", + " number of classes: 2\n", + " number of features: 14\n", + " learning rate: 0.1\n", + " training steps: 10000\n", + " batch size: 100\n", + "1st layer n. of neurons: 512\n", + "2st layer n. of neurons: 512\n", + " Normalized data: True, type: mean, Discrete Binary 0/1\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_steps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;31m# Run the optimization to update W and b values.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mrun_optimization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstep\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mdisplay_step\u001b[0m \u001b[1;33m==\u001b[0m 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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[1;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[0;32m 1065\u001b[0m for x in nest.flatten(output_gradients)]\n\u001b[0;32m 1066\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1067\u001b[1;33m flat_grad = imperative_grad.imperative_grad(\n\u001b[0m\u001b[0;32m 1068\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1069\u001b[0m \u001b[0mflat_targets\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[1;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[0;32m 69\u001b[0m \"Unknown value for unconnected_gradients: %r\" % unconnected_gradients)\n\u001b[0;32m 70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m return pywrap_tfe.TFE_Py_TapeGradient(\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[0m in \u001b[0;36m_gradient_function\u001b[1;34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices, forward_pass_name_scope)\u001b[0m\n\u001b[0;32m 160\u001b[0m \u001b[0mgradient_name_scope\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mforward_pass_name_scope\u001b[0m \u001b[1;33m+\u001b[0m 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accuracyValue)\n", " totalTime =datetime.now()- totalStartTime\n", " \n", + " stats=pd.Series(accuracyValue)\n", + " \n", " if int(step/training_steps*100)<100:\n", " print(\"Running...\")\n", " progress(int(step/training_steps*100)) \n", @@ -361,6 +427,9 @@ " print(\" loss: %f\" % loss)\n", " print(\" accuracy: %.2f %%\" % (acc*100))\n", " print(\" AVG: %.2f %%\" % (avg/avgCounter))\n", + " print(\" Trend slope: %.3f\" % (trendSlope))\n", + " print(\" MAD Dispersion: \" + str(stats.mad()))\n", + " print(\" Skewness: \" + measureSkewness(stats))\n", " print(\"\")\n", " print(\"================= Time ================\")\n", " print(\" Elapsed: \" + elapsedTime(totalTime))\n", @@ -376,6 +445,7 @@ "\n", " print(\"1st layer n. of neurons: \"+ str(n_hidden_1 ))\n", " print(\"2st layer n. of neurons: \"+ str(n_hidden_2))\n", + " print(\" Normalized data: \" + (\"True\" if normalizeDataValues else \"False\") +\", type: \" + normalizationType +\", \" +(\"Discrete Binary 0/1\" if normalizationType else \"Continuous range [0,1]\"))\n", "\n", " start_time = datetime.now()\n", "print(\"\")\n", From a8579629990de76d857ce20931e136d264edbaf0 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Tue, 4 Aug 2020 11:18:33 +0200 Subject: [PATCH 04/13] Add files via upload --- .../Multilayer_Perceptron.ipynb | 93 +++++++------------ 1 file changed, 36 insertions(+), 57 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb index 410dcd63..eeb2445d 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -58,10 +58,10 @@ "num_features = 0 # data features : number of samples(rows) for the variable set. a value of 0 loads from the dataset bellow\n", "\n", "# Training parameters.\n", - "learning_rate = 0.1\n", + "learning_rate = 1\n", "training_steps = 10000\n", - "batch_size = 100\n", - "display_step = 50\n", + "batch_size = 150\n", + "display_step = 100\n", "\n", "#normalization of data\n", "normalizeDataValues=True\n", @@ -75,18 +75,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 38)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m38\u001b[0m\n\u001b[1;33m else\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "# predictions Data.\n", "df_predict_ds=pd.read_csv('./week3_exam_dataset_test.csv')\n", @@ -102,6 +93,7 @@ "if num_features==0:\n", " num_features= df_tr_raw_y.shape[0]\n", "else:\n", + " rs=\"\"\n", " #TODO: fill possible empty values on the datasets\n", " \n", "df_tr_raw_values_y = df_tr_raw_y.values\n", @@ -127,7 +119,7 @@ " maxVal=max(np.amax(maxVal_train, axis=0),np.amax(maxVal_test, axis=0),np.amax(maxVal_pred, axis=0))\n", " if normalizationTypeBinary:\n", " x_train, x_test,data_predict_x = np.where((x_train / maxVal_train)>=0.5,1,0), np.where((x_test / maxVal_test)>=0.5,1,0), np.where((data_predict_x / maxVal_pred)>=0.5,1,0)\n", - " else\n", + " else:\n", " x_train, x_test, data_predict_x = x_train / maxVal_train, x_test / maxVal_test, data_predict_x / maxVal_pred\n", " else:\n", " meanVal_test=np.mean(x_test, axis=0)\n", @@ -137,7 +129,7 @@ " \n", " if normalizationTypeBinary:\n", " x_train, x_test,data_predict_x = np.where((x_train / meanVal_train)>=0.5,1,0), np.where((x_test / meanVal_test)>=0.5,1,0), np.where((data_predict_x / meanVal_pred)>=0.5,1,0)\n", - " else\n", + " else:\n", " x_train, x_test, data_predict_x = x_train / meanVal_train, x_test / meanVal_test, data_predict_x / meanVal_pred\n", "\n", "# Use tf.data API to shuffle and batch data.\n", @@ -147,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -318,12 +310,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -337,51 +329,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running...\n", - "[# ] 6%\n", + "[##############################] 100%\n", "\n", "================= Iteration Stats ================\n", - " step: 650 of 10000\n", - " loss: 0.340414\n", + " step: 10000 of 10000\n", + " loss: 0.403381\n", + "\n", " accuracy: 82.00 %\n", - " AVG: 80.38 %\n", + " AVG: 82.22 %\n", + " Best: 89.33 %\n", + "\n", " Trend slope: 0.000\n", " MAD Dispersion: nan\n", - " Skewness: Moderately skewed distribution ( -0.82 )\n", + " Skewness: Approximately symmetric distribution ( -0.22 )\n", "\n", "================= Time ================\n", - " Elapsed: 0h, 0 min and 9 sec\n", - " ETC: 0h, 2 min and 15 sec\n", + " Elapsed: 0h, 2 min and 30 sec\n", + " ETC: 0h, 0 min and 0 sec\n", "\n", "================= Network Setup ================\n", - " number of classes: 2\n", - " number of features: 14\n", - " learning rate: 0.1\n", + " number of classes: 100\n", + " number of features: 32460\n", + " learning rate: 1\n", " training steps: 10000\n", - " batch size: 100\n", + " batch size: 150\n", "1st layer n. of neurons: 512\n", "2st layer n. of neurons: 512\n", - " Normalized data: True, type: mean, Discrete Binary 0/1\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_steps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;31m# Run the optimization to update W and b values.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mrun_optimization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstep\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mdisplay_step\u001b[0m \u001b[1;33m==\u001b[0m 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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[1;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[0;32m 1065\u001b[0m for x in nest.flatten(output_gradients)]\n\u001b[0;32m 1066\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1067\u001b[1;33m flat_grad = imperative_grad.imperative_grad(\n\u001b[0m\u001b[0;32m 1068\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1069\u001b[0m \u001b[0mflat_targets\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[1;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[0;32m 69\u001b[0m \"Unknown value for unconnected_gradients: %r\" % unconnected_gradients)\n\u001b[0;32m 70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m return pywrap_tfe.TFE_Py_TapeGradient(\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[0m in \u001b[0;36m_gradient_function\u001b[1;34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices, forward_pass_name_scope)\u001b[0m\n\u001b[0;32m 160\u001b[0m \u001b[0mgradient_name_scope\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mforward_pass_name_scope\u001b[0m \u001b[1;33m+\u001b[0m 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================\")\n", " print(\" step: %i of %i\" % (step,training_steps))\n", " print(\" loss: %f\" % loss)\n", + " print(\"\")\n", " print(\" accuracy: %.2f %%\" % (acc*100))\n", " print(\" AVG: %.2f %%\" % (avg/avgCounter))\n", + " print(\" Best: %.2f %%\" % (np.max(accuracyValue)))\n", + " print(\"\")\n", " print(\" Trend slope: %.3f\" % (trendSlope))\n", " print(\" MAD Dispersion: \" + str(stats.mad()))\n", " print(\" Skewness: \" + measureSkewness(stats))\n", From dc2af8d7fa47aaab67a4dd8104638a51fea015ef Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Tue, 4 Aug 2020 11:26:35 +0200 Subject: [PATCH 05/13] Add files via upload --- .../Multilayer_Perceptron.ipynb | 50 +++++++++---------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb index eeb2445d..b5c13d3e 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -49,18 +49,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# parameters initialization.\n", "num_classes = 100 # total classes : number of output results\n", - "num_features = 0 # data features : number of samples(rows) for the variable set. a value of 0 loads from the dataset bellow\n", + "num_features = 0 # data features : number of input variables on the dataset. a value of 0 loads from the dataset bellow\n", "\n", "# Training parameters.\n", "learning_rate = 1\n", "training_steps = 10000\n", - "batch_size = 150\n", + "batch_size = 100\n", "display_step = 100\n", "\n", "#normalization of data\n", @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -90,16 +90,16 @@ "df_tr_raw_y= df_tr['y']\n", "if num_classes==0:\n", " num_classes= df_tr_raw_y.shape[0]\n", - "if num_features==0:\n", - " num_features= df_tr_raw_y.shape[0]\n", - "else:\n", - " rs=\"\"\n", - " #TODO: fill possible empty values on the datasets\n", - " \n", + "\n", "df_tr_raw_values_y = df_tr_raw_y.values\n", "data_tr_y = np.float32(df_tr_raw_values_y)\n", "\n", "df_tr_raw_x= df_tr.drop('y',1)\n", + "if num_features==0:\n", + " num_features= df_tr_raw_x.shape[1]\n", + "else:\n", + " rs=\"\"\n", + " #TODO: fill possible empty values on the datasets\n", "df_tr_raw_values_x = df_tr_raw_x.values\n", "data_tr_x = np.float32(df_tr_raw_values_x)\n", "\n", @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -310,12 +310,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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On/vsZ/SF//P5wnGi6LiS8rnUDcGnbVlpDM1yqulphj43Th3HSt3oada6ULOPSeqR9Iykm9x9vpltc/cDix6z1d2nlnhut6RuSZoxY8bcpUuXSpIe2p7Tuu155VyFn7xr0KX+p5/R1u3blctLeZlybnIzte+3v7Jtbdr8l63KuykvKeemvKKfaJpbRpmWVsmy0XylQXfl8tpzP+eFceZGyMiVkcvMlTVFZ5xllDUpm1Hht5myGamleFrGor9Jnh/UxNbWoscPPa7oecXP2etxtvd8o2kPPrBaWSu0LWNRO6P7r3jZS0vOq3jobMuWLVq3bt1e70YzmYw6Ozv18MP7Hs8p1tbWppkzZ+45eSjEli1btHHjRu3evbvi81euXLnn9qxZs7Rhw4a9/ydRO0OXX82yy9Wl+GSp0PlValelZcXV39+vyZMnV73uxY894IADtH379prXM0SpWhQrV5fi7WW4uXPnVtWOobqVal89/uel1FL3JLehUm0bqS3HH398YqHZ8M9WSpoq6VZJMyS1qtDTPFPStmGP21ppXsWf07zghlXeed51JX8OO+/n3vmvy/zQjy/1zo9f4S9ZcK0f+5Vb/LVfu9U7z7nMO/7xm97xDxf78957oR8y/wI/5Iyv+MHv/qLPeNfnfcY7P+vPO/WzftJXlvknrviDf/LKu/zTy/7on7vmHv/Sz+/zf79+lS+88X6/6OYH/JJb1/i3b3vQL/vVQ/6D/37Yf3T7Ol/6+/X+0zs3+M/v3ui/uGeT3/Knx7xv9RP+mzVP+u0P/cVXrtvif3xkm//p0e3e+dJjvOXADs9OmeHZydM8M2mK24T9vPPwI3zXQM5/8MP6fB4siauM1Pq5OfeRP+hc788+1vKZ0+Ffq1btB7LjfKavkZdiTPJD+kl+3jAJwy83V+4r0YrV47UwpNRrtdGfNa+27kltQ9Wst8bSxQ0knSbpsqL775P0LUmrJXVE0zokra40r+LQ3P7Mbn9s+zO+ecezvm3nbn9614DvGsh5Lpev+h9Rrw2+WrVckqoaSYRms1/I1dSh2joWL38oNOOuWz13qEnMLykrVqyoqq0hb5TSuJ7u9X0tlHqtJvk/T3Pdq1nvsRaafyPpPhXOljVJl0s6V9J/SDo/esz5ki6oNK96XXu2+J3RSBvJSJeVSvoyZMWXqKq2bSMtr16hmeRlycotq9Y61PL8oe8ijbtu9b7MXtq+s3UkK1asKLvuw9e73GPTvJ5D6tXbKvVaTfJ/nua6V7PeYyo0C+ujL0i6X9K9kn4oaYKk6ZKWS1oT/Z5WaT5JXLC9mncz1b6jrMfjq3nXN9Lyrr766rjlib0u9VRLTzFOHYvV+oaj3kPNY6GnWWq909zjaSR6mpXbNuZ7mvX8SSI0qwmDajfeej0+dAc70vMXLVoUtzyx16We6nUd3DhBVWto1nuouZlvXqox0jHNkdY7Dcc002A0HtNMyrg9plnPn6S+Gix0aKXaYZJ6PX7oOZWGfUZ6/sKFC6svSo3rUm/VDH/Vc0i0HkPbtQ4Vl5tfvYfF67Xs4d/WEbLejRz+T5tKhwOS/J+nqe5x20JoNjg0QzWrpxnaCxmrPc1q1bOtafu2jmaq5p3/8LqN5vVOWj1PPBvNaulRE5oVQrNZ77SbcUyzHh+tGO3HNKuV9NmMtWx/ja5jPV8rIcE3Uo9pNG0/9VBN3YvrWvwRp/H2hqKWN1aEZpnQbPaLr95nz9b6+JDnJ3X2bJp3eEmdzViP7a9Rdaz3ayXkC4bL9ZhG0/ZTi2rrXlzX4tBM65nCSanlEBChWSY0GeapXhKf0xwvRvMwY73bWml+9JgKajksQ93S19NszNcmJGj9+vVVTUe43t5edXV1KZPJqKurS729vc1uUuqMpu2v3m3t6elRe3v7XtPa29vV09OTyPKSltT2Xm0dKtV1vEhtHZJK40b80NOMJ6Sn2exh77Sip7m3ckOso6nHlOT2Hqfu9bqYxmgXdwhfDM+OHJrs3KsXEpqjKQwaKYljmo3SjJOORstZoElu77XUnUMp8RCaZULTffycUFAvI70Q6/35wXLzr/f/qVHbQL3Pnm20Rrd1tPSYkv7ccdy6E5rxEJoVQhPVCb3KSD3feTf6KiZJ9WjYicWT9rqldWQl7XVLqyRDc9SfCIT6WLBggXbu3Fn2MbUchC81/507d2rBggWx5teoeY9GnMBVvdSedILUITQhqfwZjWamzs5OLV68WPPnz6/r/OtxJuVoO0szSb29veru7ta6devk7lq3bp26u7sJzgrmz5+vxYsXq7Ozsy7bO8YuQhOSpNmzZ5ec3tnZqXw+r7Vr19a0Axlp/iNNT8u8Rxt63fHNnz9fa9eurcv2jrGL0ISk5Ienkpw/Q2vPodcNJIvQhKTkh6eSnD9Da8+h1w0kq6XZDUB6zJ8/P9GgSXL+Sbd9tOjp6VF3d/deQ7TjtdcNJIGeJjCG0OsGkkVPExhj6HUDyaGnCQBAIEITAIBAhCYAAIEITQAAAhGaAAAEIjQBAAhEaAIAEIjQBAAgEKEJAECgMRmafAkvACAJY+4yekNfwjt0weqhL+GVxKXFAAA1GXM9Tb6EFwCQlDEXmnwJLwAgKWMuNPkSXgBAUsZcaPb09Ki9vX2vaXwJLwCgHsZcaPIlvACApIy5s2clvoQXAJCMMdfTBAAgKYQmAACBCE0AAAIRmgAABCI0AQAIRGgCABCI0AQAIBChCQBAIEITAIBAhCYAAIGaEppmdqCZXWVm95vZKjN7tZlNM7ObzWxN9HtqM9oGAMBImtXTvFjSDe7+V5JeLmmVpPMlLXf3IyQtj+4DAJAaDQ9NM5si6XWSLpMkd9/t7tsknSzp8uhhl0s6pdFtAwCgnODQNLMXmtkSM7vazF5dwzIPl/SkpP8ysz+Y2XfNbD9Jh7j7JkmKfh9cwzIAAKg7c/fSfzCb6O7PFt3/saTPS3JJV7r7UbEWaHa0pP+RdJy7325mF0t6StK57n5g0eO2uvs+xzXNrFtStyTNmDFj7tKlS+M0Y1zr7+/X5MmTm92MUYnaxUPd4qFu8Rx//PEr3f3oJOZd7vs0f25mP3D3H0b3ByR1qRCauRqWuUHSBne/Pbp/lQrHLx83sw5332RmHZKeKPVkd18sabEkzZkzx+fNm1dDU8anvr4+Ubd4qF081C0e6pY+5YZn3yLpADO7wcxeK+nfVDgW+VZJsb/h2d0fk/SImc2JJp0g6U+SrpV0VjTtLEk/i7sMAACSMGJP091zki4xsx9K+pykDkn/293/XIflniup18zaJD0k6f0qBPhSMztb0npJp9VhOQAA1M2IoWlmfyPpk5J2S/qKpGck9ZjZBklfcvftcRfq7ndJKjXefELceQIAkLRyxzQvlfQuSZMlfdvdj5N0hpn9naSlkt7cgPYBAJAa5UIzp8KJP+0q9DYlSe5+m6Tbkm0WAADpUy40/5ekD6oQmO9rTHMAAEivcicCPSDpXxvYFgAAUo1vOQEAIBChCQBAoIqhaWZvNzPCFQAw7oWE4RmS1pjZBWb24qQbBABAWlUMTXc/U9IrJP1ZhW8m+a2ZdZvZ/om3DgCAFAkadnX3pyRdLekKFS6n9/eS7jSzcxNsGwAAqRJyTPMkM/uppFsltUo6xt3fKunlKlzEHQCAcaHcxQ2GnCbp6+7+y+KJ7r7TzP4xmWYBAJA+IaH5eUmbhu6Y2SRJh7j7WndfnljLAABImZBjmldKyhfdz0XTAAAYV0JCs8Xdiy/YvltSW3JNAgAgnUJC80kze8fQHTM7WdLm5JoEAEA6hRzT/JCkXjO7RJJJekR86wkAYByqGJru/mdJx5rZZEnm7juSbxYAAOkT0tOUmb1N0kskTTQzSZK7fzHBdgEAkDohFze4VNLpks5VYXj2NEmdCbcLAIDUCTkR6DXu/j5JW939C5JeLenQZJsFAED6hITms9HvnWb2fEkDkg5LrkkAAKRTyDHNn5vZgZL+Q9KdklzSdxJtFQAAKVQ2NKMvn17u7tskXW1m10ma6O7bG9I6AABSpOzwrLvnJf1n0f1dBCYAYLwKOaZ5k5mdakOfNQEAYJwKOab5L5L2kzRoZs+q8LETd/cpibYMAICUCbki0P6NaAgAAGlXMTTN7HWlpg//UmoAAMa6kOHZTxbdnijpGEkrJb0+kRYBAJBSIcOzJxXfN7NDJV2QWIsAAEipkLNnh9sg6a/r3RAAANIu5JjmN1S4CpBUCNmjJN2dZKMAAEijkGOadxTdHpT0Y3f/TULtAQAgtUJC8ypJz7p7TpLMLGtm7e6+M9mmAQCQLiHHNJdLmlR0f5KkW5JpDgAA6RUSmhPdvX/oTnS7PbkmAQCQTiGh+bSZvXLojpnNlfRMck0CACCdQo5pflzSlWb2aHS/Q9LpyTUJAIB0Crm4we/N7K8kzVHhYu33u/tA4i0DACBlKg7PmtlHJO3n7ve6+z2SJpvZh5NvGgAA6RJyTPOf3H3b0B133yrpn5JrEgAA6RQSmpniL6A2s6yktuSaBABAOoWcCHSjpKVmdqkKl9P7kKQbEm0VAAApFBKa50nqlnSOCicC3STpO0k2CgCANKo4POvueXe/1N3f5e6nSrpP0jdqXXB0Ob4/mNl10f1pZnazma2Jfk+tdRkAANRT0FeDmdlRZvY1M1sr6UuS7q/Dsj8maVXR/fMlLXf3I1S4dN/5dVgGAAB1M2JomtmLzOxzZrZK0iUqfI+mufvx7l5TT9PMZkl6m6TvFk0+WdLl0e3LJZ1SyzIAAKi3csc075f0K0knufuDkmRmn6jTci+S9ClJ+xdNO8TdN0mSu28ys4PrtCwAAOqiXGieKukMSSvM7AZJV6hwIlBNzOztkp5w95VmNi/G87tVODFJM2bMUF9fX61NGnf6+/upW0zULh7qFg91Sx9z9/IPMNtPhaHS90h6vQpDpz9195tiLdDs3yW9V4UvtJ4oaYqkZZJeJWle1MvskNTn7nPKzWvOnDm+evXqOM0Y1/r6+jRv3rxmN2NUonbxULd4qFs8ZrbS3Y9OYt4hZ88+7e697v52SbMk3aUaTtJx90+7+yx371KhJ3uru58p6VpJZ0UPO0vSz+IuAwCAJASdPTvE3be4+7fd/fUJtOWrkt5oZmskvTG6DwBAaoRc3CAx7t4nqS+6/RdJJzSzPQAAlFNVTxMAgPGM0AQAIBChCQBAIEITAIBAhCYAAIEITQAAAhGaAAAEIjQBAAhEaAIAEIjQBAAgEKEJAEAgQhMAgECEJgAAgQhNAAACEZoAAAQiNAEACERoAgAQiNAEACAQoQkAQCBCEwCAQIQmAACBCE0AAAIRmgAABCI0AQAIRGgCABCI0AQAIBChCQBAIEITAIBAhCYAAIEITQAAAhGaAAAEIjQBAAhEaAIAEIjQBAAgEKEJAEAgQhMAgECEJgAAgQhNAAACEZoAAAQiNAEACERoAgAQiNAEACAQoQkAQCBCEwCAQIQmAACBCE0AAAI1PDTN7FAzW2Fmq8zsPjP7WDR9mpndbGZrot9TG902AADKaUZPc1DSv7r7iyUdK+kjZnakpPMlLXf3IyQtj+4DAJAaDQ9Nd9/k7ndGt3dIWiVppqSTJV0ePexySac0um0AAJRj7t68hZt1SfqlpL+WtN7dDyz621Z332eI1sy6JXVL0owZM+YuXbq0MY0dQ/r7+zV58uRmN2NUonbxULd4qFs8xx9//Ep3PzqJeTctNM1ssqTbJPW4+zIz2xYSmsXmzJnjq1evTrqpY05fX5/mzZvX7GaMStQuHuoWD3WLx8wSC82mnD1rZq2SrpbU6+7LosmPm1lH9PcOSU80o20AAIykGWfPmqTLJK1y9wuL/nStpLOi22dJ+lmj2wYAQDktTVjmcZLeK+keM7srmvYZSV+VtNTMzpa0XtJpTWgbAAAjanhouvuvJdkIfz6hkW0BAKAaXBEIAIBAhCYAAIEITQAAAhGaAAAEIjQBAAhEaAIAEIjQBAAgEKEJAEAgQhMAgECEJgAAgQhNAAACEZoAAAQiNAEACERoAgAQiNAEACAQoQkAQCBCEwCAQIQmAACBCE0AAAIRmgAABCI0AQAIRGgCABCI0AQAIBChCQBAIEITAIBAhPIYwg8AAAbSSURBVCYAAIEITQAAAhGaAAAEIjQBAAhEaAIAEIjQBAAgEKEJAEAgQhMAgECEJgAAgQhNAAACEZoAAAQiNAEACERoAgAQiNAEACAQoQkAQCBCEwCAQIQmAACBCE0AAAIRmgAABCI0AQAIlLrQNLO3mNlqM3vQzM5vdnsAABiSqtA0s6ykb0p6q6QjJb3HzI5sbqsAAChIVWhKOkbSg+7+kLvvlnSFpJOb3CYAACSlLzRnSnqk6P6GaBoAAE3X0uwGDGMlpvleDzDrltQd3d1lZvcm3qqx5yBJm5vdiFGK2sVD3eKhbvHMSWrGaQvNDZIOLbo/S9KjxQ9w98WSFkuSmd3h7kc3rnljA3WLj9rFQ93ioW7xmNkdSc07bcOzv5d0hJkdZmZtks6QdG2T2wQAgKSU9TTdfdDMPirpRklZSd9z9/ua3CwAACSlLDQlyd2vl3R94MMXJ9mWMYy6xUft4qFu8VC3eBKrm7l75UcBAIDUHdMEACC1Rm1ocrm955jZoWa2wsxWmdl9ZvaxaPo0M7vZzNZEv6cWPefTUe1Wm9mbi6bPNbN7or8tMrNSHwMaU8wsa2Z/MLProvvULYCZHWhmV5nZ/dG292pqV5mZfSJ6nd5rZj82s4nUbV9m9j0ze6L4Y4X1rJOZTTCzn0TTbzezrqCGufuo+1HhJKE/SzpcUpukuyUd2ex2NbEeHZJeGd3eX9IDKlyG8AJJ50fTz5f0tej2kVHNJkg6LKplNvrb7yS9WoXPzP5C0lubvX4NqN+/SPqRpOui+9QtrG6XS/pAdLtN0oHUrmLNZkp6WNKk6P5SSf9A3UrW6nWSXinp3qJpdauTpA9LujS6fYakn4S0a7T2NLncXhF33+Tud0a3d0hapcKL82QVdmyKfp8S3T5Z0hXuvsvdH5b0oKRjzKxD0hR3/60XtqQfFD1nTDKzWZLeJum7RZOpWwVmNkWFndplkuTuu919m6hdiBZJk8ysRVK7Cp9Fp27DuPsvJW0ZNrmedSqe11WSTgjprY/W0ORyeyOIhhheIel2SYe4+yapEKySDo4eNlL9Zka3h08fyy6S9ClJ+aJp1K2ywyU9Kem/oqHt75rZfqJ2Zbn7RkkLJa2XtEnSdne/SdQtVD3rtOc57j4oabuk6ZUaMFpDs+Ll9sYjM5ss6WpJH3f3p8o9tMQ0LzN9TDKzt0t6wt1Xhj6lxLRxV7dIiwpDZ//X3V8h6WkVhstGQu0kRcfgTlZhCPH5kvYzszPLPaXEtHFXtwBx6hSrhqM1NCtebm+8MbNWFQKz192XRZMfj4YnFP1+Ipo+Uv02RLeHTx+rjpP0DjNbq8IQ/+vNbImoW4gNkja4++3R/atUCFFqV94bJD3s7k+6+4CkZZJeI+oWqp512vOcaKj8AO07HLyP0RqaXG6vSDQOf5mkVe5+YdGfrpV0VnT7LEk/K5p+RnT22GGSjpD0u2i4Y4eZHRvN831Fzxlz3P3T7j7L3btU2IZudfczRd0qcvfHJD1iZkMXxj5B0p9E7SpZL+lYM2uP1vcEFc5BoG5h6lmn4nm9S4XXf+XeerPPkIr7I+lEFc4S/bOkBc1uT5Nr8bcqDCv8UdJd0c+JKozPL5e0Jvo9reg5C6LarVbRWXeSjpZ0b/S3SxRdAGOs/0iap+fOnqVuYTU7StId0XZ3jaSp1C6obl+QdH+0zj9U4YxP6rZvnX6swnHfARV6hWfXs06SJkq6UoWThn4n6fCQdnFFIAAAAo3W4VkAABqO0AQAIBChCQBAIEITAIBAhCYAAIEITSBFzCxnZncV/dTtG3zMrKv4GyMAVK+l2Q0AsJdn3P2oZjcCQGn0NIFRwMzWmtnXzOx30c8Lo+mdZrbczP4Y/Z4dTT/EzH5qZndHP6+JZpU1s+9Y4fscbzKzSU1bKWAUIjSBdJk0bHj29KK/PeXux6hwVZOLommXSPqBu79MUq+kRdH0RZJuc/eXq3BN2Pui6UdI+qa7v0TSNkmnJrw+wJjCFYGAFDGzfnefXGL6Wkmvd/eHoovzP+bu081ss6QOdx+Ipm9y94PM7ElJs9x9V9E8uiTd7O5HRPfPk9Tq7l9Ofs2AsYGeJjB6+Ai3R3pMKbuKbufEeQ1AVQhNYPQ4vej3b6Pb/63CN7RI0nxJv45uL5d0jiSZWdbMpjSqkcBYxrtMIF0mmdldRfdvcPehj51MMLPbVXiz+55o2j9L+p6ZfVLSk5LeH03/mKTFZna2Cj3Kc1T4xggANeCYJjAKRMc0j3b3zc1uCzCeMTwLAEAgepoAAASipwkAQCBCEwCAQIQmAACBCE0AAAIRmgAABCI0AQAI9P8B2XuBzwVIixYAAAAASUVORK5CYII=\n", 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" ] @@ -333,26 +333,26 @@ "\n", "================= Iteration Stats ================\n", " step: 10000 of 10000\n", - " loss: 0.403381\n", + " loss: 0.341034\n", "\n", - " accuracy: 82.00 %\n", - " AVG: 82.22 %\n", - " Best: 89.33 %\n", + " accuracy: 85.00 %\n", + " AVG: 81.03 %\n", + " Best: 88.00 %\n", "\n", " Trend slope: 0.000\n", " MAD Dispersion: nan\n", - " Skewness: Approximately symmetric distribution ( -0.22 )\n", + " Skewness: Moderately skewed distribution ( -0.6 )\n", "\n", "================= Time ================\n", - " Elapsed: 0h, 2 min and 30 sec\n", + " Elapsed: 0h, 2 min and 17 sec\n", " ETC: 0h, 0 min and 0 sec\n", "\n", "================= Network Setup ================\n", " number of classes: 100\n", - " number of features: 32460\n", + " number of features: 14\n", " learning rate: 1\n", " training steps: 10000\n", - " batch size: 150\n", + " batch size: 100\n", "1st layer n. of neurons: 512\n", "2st layer n. of neurons: 512\n", " Normalized data: True, type: mean, Discrete Binary 0/1\n", From ed46a9344a5fd4034819e0252f3055ec32dfe905 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Tue, 4 Aug 2020 13:19:11 +0200 Subject: [PATCH 06/13] Add files via upload --- .../Multilayer_Perceptron.ipynb | 102 ++++++++---------- 1 file changed, 42 insertions(+), 60 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb index b5c13d3e..be190c0b 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,18 +49,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "# task type to perform\n", + "taskRunning=\"regression\" # can be: classification / regression\n", + "\n", "# parameters initialization.\n", - "num_classes = 100 # total classes : number of output results\n", + "num_classes = 1 # total classes : number of output results wanted\n", "num_features = 0 # data features : number of input variables on the dataset. a value of 0 loads from the dataset bellow\n", "\n", "# Training parameters.\n", "learning_rate = 1\n", "training_steps = 10000\n", - "batch_size = 100\n", + "batch_size = 24\n", "display_step = 100\n", "\n", "#normalization of data\n", @@ -75,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -88,8 +91,6 @@ "df_tr=pd.read_csv('./week3_exam_dataset_train.csv')\n", "\n", "df_tr_raw_y= df_tr['y']\n", - "if num_classes==0:\n", - " num_classes= df_tr_raw_y.shape[0]\n", "\n", "df_tr_raw_values_y = df_tr_raw_y.values\n", "data_tr_y = np.float32(df_tr_raw_values_y)\n", @@ -139,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -180,9 +181,12 @@ "# Note that this will apply 'softmax' to the logits.\n", "def cross_entropy_loss(x, y):\n", " # Convert labels to int 64 for tf cross-entropy function.\n", - " y = tf.cast(y, tf.int64)\n", + " y = tf.cast(y, tf.int32)\n", + " if (taskRunning==\"classification\"):\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " else: \n", + " loss=tf.keras.losses.binary_crossentropy(y, x)\n", " # Apply softmax to logits and compute cross-entropy.\n", - " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", " # Average loss across the batch.\n", " return tf.reduce_mean(loss)\n", "\n", @@ -213,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -310,12 +314,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -329,35 +333,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "[##############################] 100%\n", + "Running...\n", + "[############### ] 52%\n", "\n", "================= Iteration Stats ================\n", - " step: 10000 of 10000\n", - " loss: 0.341034\n", + " step: 5200 of 10000\n", + " loss: 1.285412\n", "\n", - " accuracy: 85.00 %\n", - " AVG: 81.03 %\n", - " Best: 88.00 %\n", + " accuracy: 91.67 %\n", + " AVG: 74.92 %\n", + " Best: 91.67 %\n", "\n", - " Trend slope: 0.000\n", + " Trend slope: -0.000\n", " MAD Dispersion: nan\n", - " Skewness: Moderately skewed distribution ( -0.6 )\n", + " Skewness: Approximately symmetric distribution ( -0.29 )\n", "\n", "================= Time ================\n", - " Elapsed: 0h, 2 min and 17 sec\n", - " ETC: 0h, 0 min and 0 sec\n", + " Elapsed: 0h, 1 min and 10 sec\n", + " ETC: 0h, 1 min and 22 sec\n", "\n", "================= Network Setup ================\n", - " number of classes: 100\n", + " number of classes: 1\n", " number of features: 14\n", " learning rate: 1\n", " training steps: 10000\n", - " batch size: 100\n", + " batch size: 24\n", "1st layer n. of neurons: 512\n", "2st layer n. of neurons: 512\n", - " Normalized data: True, type: mean, Discrete Binary 0/1\n", - "\n", - "Analysis finished.\n" + " Normalized data: True, type: mean, Discrete Binary 0/1\n" ] } ], @@ -428,8 +431,11 @@ "\n", " start_time = datetime.now()\n", "print(\"\")\n", - "print(\"Analysis finished.\") \n", - "#print(\"Final Average accuracy is %.2f %%\" % (avg/avgCounter))" + "print(\"Trainning Analysis Finished.\") \n", + "print(\"Start testing test dataset...\")\n", + "run_test = neural_net(x_test, is_training=False)\n", + "print(\"Accuracy of highest score in prediction dataset\")\n", + "print(\" Test Accuracy: %.2f %%\" % (tf.math.round(100*accuracy(run_test, y_test))))\n" ] }, { @@ -438,36 +444,12 @@ "metadata": {}, "outputs": [], "source": [ - "# +++++++++++++++++++++++++++++++++++++++++++++++++++\n", - "#!!! code bellow this line is not yet finished !!!\n", - "# +++++++++++++++++++++++++++++++++++++++++++++++++++\n", - "\n", - "# Test model on validation set.\n", - "print(x_test.shape)\n", - "\n", - "pred = neural_net(x_test, is_training=False)\n", - "\n", - "print(\"Accuracy of highest score in prediction vector\")\n", - "print(\" Test Accuracy: %.2f %%\" % (tf.math.round(100*accuracy(pred, y_test))))\n", - "print(\"\")\n", + "print(data_predict_x.shape)\n", "\n", - "prediction= np.round(pred.numpy(),2)\n", - "print(\"Model prediction shape:\" + str(prediction.shape))\n", - "print(\" Model initial shape:\" + str(y_test.shape))\n", + "run_prediction = neural_net(data_predict_x, is_training=False)\n", + "print(run_prediction.shape)\n", "\n", - "print(\"\")\n", - "print(\"Model prediction value:\")\n", - "print(prediction)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = neural_net(data_predict_x)\n", - "print(\"Model prediction: %i\" % np.argmax(predictions.numpy()))" + "print(np.round(run_prediction.numpy(),0))\n" ] }, { From 988cc5264d0c3ab090aba96856dac822b01c1a2b Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Tue, 4 Aug 2020 16:14:28 +0200 Subject: [PATCH 07/13] Add files via upload --- .../Multilayer_Perceptron.ipynb | 136 +++++++++++++----- 1 file changed, 101 insertions(+), 35 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb index be190c0b..ad247b53 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb @@ -54,7 +54,7 @@ "outputs": [], "source": [ "# task type to perform\n", - "taskRunning=\"regression\" # can be: classification / regression\n", + "taskRunning=\"classification\" # can be: classification / regression\n", "\n", "# parameters initialization.\n", "num_classes = 1 # total classes : number of output results wanted\n", @@ -63,12 +63,12 @@ "# Training parameters.\n", "learning_rate = 1\n", "training_steps = 10000\n", - "batch_size = 24\n", + "batch_size = 100\n", "display_step = 100\n", "\n", "#normalization of data\n", "normalizeDataValues=True\n", - "normalizationType= \"mean\" # accepts: max, mean\n", + "normalizationType= \"max\" # accepts: max, mean\n", "normalizationTypeBinary=True\n", "\n", "# Network parameters.\n", @@ -154,8 +154,11 @@ " # First fully-connected hidden layer.\n", " self.fc2 = layers.Dense(n_hidden_2, activation=tf.nn.relu)\n", " # Second fully-connecter hidden layer.\n", - " self.out = layers.Dense(num_classes)\n", - "\n", + " if (taskRunning==\"classification\"):\n", + " self.out = layers.Dense(num_classes+1)\n", + " else:\n", + " self.out = layers.Dense(num_classes)\n", + " \n", " # Set forward pass.\n", " def call(self, x, is_training=False):\n", " x = self.fc1(x)\n", @@ -173,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -182,10 +185,10 @@ "def cross_entropy_loss(x, y):\n", " # Convert labels to int 64 for tf cross-entropy function.\n", " y = tf.cast(y, tf.int32)\n", - " if (taskRunning==\"classification\"):\n", - " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", - " else: \n", + " if (taskRunning==\"regression\"):\n", " loss=tf.keras.losses.binary_crossentropy(y, x)\n", + " else: \n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", " # Apply softmax to logits and compute cross-entropy.\n", " # Average loss across the batch.\n", " return tf.reduce_mean(loss)\n", @@ -217,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -242,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -314,12 +317,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -333,34 +336,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running...\n", - "[############### ] 52%\n", + "[##############################] 100%\n", "\n", "================= Iteration Stats ================\n", - " step: 5200 of 10000\n", - " loss: 1.285412\n", + " step: 10000 of 10000\n", + " loss: 0.522784\n", "\n", - " accuracy: 91.67 %\n", - " AVG: 74.92 %\n", - " Best: 91.67 %\n", + " step accuracy: 70.00 %\n", + " AVG: 77.28 %\n", + " Best: 90.00 %\n", "\n", " Trend slope: -0.000\n", " MAD Dispersion: nan\n", - " Skewness: Approximately symmetric distribution ( -0.29 )\n", + " Skewness: Approximately symmetric distribution ( -0.12 )\n", "\n", "================= Time ================\n", - " Elapsed: 0h, 1 min and 10 sec\n", - " ETC: 0h, 1 min and 22 sec\n", + " Elapsed: 0h, 2 min and 12 sec\n", + " ETC: 0h, 0 min and 0 sec\n", "\n", "================= Network Setup ================\n", " number of classes: 1\n", " number of features: 14\n", " learning rate: 1\n", " training steps: 10000\n", - " batch size: 24\n", + " batch size: 100\n", "1st layer n. of neurons: 512\n", "2st layer n. of neurons: 512\n", - " Normalized data: True, type: mean, Discrete Binary 0/1\n" + " Normalized data: True, type: max, Discrete Binary 0/1\n", + "\n", + "Trainning Analysis Finished. Start testing test dataset...\n", + "\n", + "Accuracy of highest score in prediction dataset\n", + " Test Accuracy: 77.00 %\n" ] } ], @@ -383,7 +390,7 @@ " # Run the optimization to update W and b values.\n", " run_optimization(batch_x, batch_y)\n", " \n", - " if step % display_step == 0:\n", + " if step % display_step == 0 or step==training_steps:\n", " pred = neural_net(batch_x, is_training=True)\n", " loss = cross_entropy_loss(pred, batch_y)\n", " acc = accuracy(pred, batch_y)\n", @@ -405,7 +412,7 @@ " print(\" step: %i of %i\" % (step,training_steps))\n", " print(\" loss: %f\" % loss)\n", " print(\"\")\n", - " print(\" accuracy: %.2f %%\" % (acc*100))\n", + " print(\" step accuracy: %.2f %%\" % (acc*100))\n", " print(\" AVG: %.2f %%\" % (avg/avgCounter))\n", " print(\" Best: %.2f %%\" % (np.max(accuracyValue)))\n", " print(\"\")\n", @@ -427,12 +434,12 @@ "\n", " print(\"1st layer n. of neurons: \"+ str(n_hidden_1 ))\n", " print(\"2st layer n. of neurons: \"+ str(n_hidden_2))\n", - " print(\" Normalized data: \" + (\"True\" if normalizeDataValues else \"False\") +\", type: \" + normalizationType +\", \" +(\"Discrete Binary 0/1\" if normalizationType else \"Continuous range [0,1]\"))\n", + " print(\" Normalized data: \" + (\"True\" if normalizeDataValues else \"False\") +\", type: \" + normalizationType +\", \" +(\"Discrete Binary 0/1\" if normalizationTypeBinary else \"Continuous range [0,1]\"))\n", "\n", " start_time = datetime.now()\n", "print(\"\")\n", - "print(\"Trainning Analysis Finished.\") \n", - "print(\"Start testing test dataset...\")\n", + "print(\"Trainning Analysis Finished. Start testing test dataset...\")\n", + "print(\"\")\n", "run_test = neural_net(x_test, is_training=False)\n", "print(\"Accuracy of highest score in prediction dataset\")\n", "print(\" Test Accuracy: %.2f %%\" % (tf.math.round(100*accuracy(run_test, y_test))))\n" @@ -440,16 +447,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "distribution of values:\n", + " equal to 1: 0.0%\n", + " equal to 0: 100.0%\n", + "\n", + "checking answers:\n", + "none of the answers matches the output\n", + "Answer starts with '0 0 1', matches [80]: 79.21% of values\n", + "Answer starts with '1 0 1', matches [55]: 54.46% of values\n", + "Answer starts with '1 1 0', matches [21]: 20.79% of values\n", + "Answer starts with '2 1 0', matches [15]: 14.85% of values\n" + ] + } + ], "source": [ - "print(data_predict_x.shape)\n", + "def calStats(output, answer):\n", + " num_matches= np.sum(output[:min(len(output), len(answer))] == answer[:min(len(output), len(answer))])\n", + " percentage =round((num_matches / min(len(output), answer.shape[1]))*100,2)\n", + " return num_matches, percentage\n", "\n", "run_prediction = neural_net(data_predict_x, is_training=False)\n", - "print(run_prediction.shape)\n", + "output_pre=np.round(run_prediction.numpy(),0)\n", + "\n", + "#assessing output value with its probability \n", + "ouput = np.empty(shape=(output_pre.shape[0],1))\n", + "for i in range(output_pre.shape[0]):\n", + " if ((output_pre[i][0]==1 and output_pre[i][1]==0) or (output_pre[i][0]==0 and output_pre[i][1]==1)):\n", + " res[i][0]=0\n", + " elif((output_pre[i][0]==1 and output_pre[i][1]==1) or (output_pre[i][0]==0 and output_pre[i][1]==0)):\n", + " res[i][0]=1\n", + "\n", + "print(\"distribution of values:\")\n", + "print(\" equal to 1: \" + str( round(np.sum(output==1)/output.shape[0]*100,1) )+\"%\")\n", + "print(\" equal to 0: \" + str( round(np.sum(output==0)/output.shape[0]*100,1) )+\"%\")\n", + "\n", + "#checking answers\n", + "answer1=np.array([[0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1]])\n", + "answer2=np.array([[1,0,1,1,1,1,0,0,1,0,0,1,0,0,1,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,0,1,0,1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,1,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,0,1,1,0,1,1]])\n", + "answer3=np.array([[1,1,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,0,0,1,1,0,1,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,1,1,0]])\n", + "answer4=np.array([[2,1,0,2,0,1,0,2,1,2,1,2,1,1,2,1,1,1,1,2,0,1,3,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,3,1,1,1,1,1,2,1,0,1,1,1,0,1,1,2,1,3,1,1,1,0,0,1,1,2,1,1,1,1,1,1,1,2,1,2,0,1,1,2,1,0,0,2,1,1,1,2,0,1,1,1,1,1,2,1,1,0,1,1,0,3,1,0]])\n", "\n", - "print(np.round(run_prediction.numpy(),0))\n" + "print(\"\")\n", + "print(\"checking answers:\")\n", + "\n", + "if (output==answer1).all():\n", + " print(\"answwer starts with '0 0 1'\")\n", + "elif (output==answer2).all():\n", + " print(\"answwer starts with '1 0 1'\")\n", + "elif (output==answer3).all():\n", + " print(\"answwer starts with '1 1 0'\")\n", + "elif (output==answer4).all():\n", + " print(\"answwer starts with '2 1 0'\")\n", + "else:\n", + " print(\"none of the answers matches the output\")\n", + " num_matches, percentage= calStats(output, answer1)\n", + " print (\"Answer starts with '0 0 1', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " num_matches, percentage= calStats(output, answer2)\n", + " print (\"Answer starts with '1 0 1', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " num_matches, percentage= calStats(output, answer3)\n", + " print (\"Answer starts with '1 1 0', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " num_matches, percentage= calStats(output, answer4)\n", + " print (\"Answer starts with '2 1 0', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " " ] }, { From bbb6d1e24795b66882b3f0a3774ffabdb2e3aec2 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Tue, 4 Aug 2020 17:37:45 +0200 Subject: [PATCH 08/13] Add files via upload --- .../Multilayer_Perceptron_With_Keras.ipynb | 273 ++++++++++++++++++ 1 file changed, 273 insertions(+) create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb new file mode 100644 index 00000000..0b3a1462 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# example of summarizing a model\n", + "from tensorflow.keras import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "import pandas as pd\n", + "import numpy as np\n", + "from tensorflow.keras.optimizers import SGD\n", + "from matplotlib import pyplot\n", + "from tensorflow.keras.layers import Dropout\n", + "from tensorflow.keras.layers import BatchNormalization\n", + "from tensorflow.keras.callbacks import EarlyStopping\n", + "from tensorflow.keras.utils import plot_model\n", + "\n", + "# parameters initialization.\n", + "num_classes = 1 # total classes : number of output results wanted\n", + "num_features = 0 # data features : number of input variables on the dataset. a value of 0 loads from the dataset bellow\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.0000001\n", + "training_steps = 5000\n", + "batch_size = 24\n", + "display_step = 100\n", + "\n", + "# Network parameters.\n", + "n_hidden_1 = 256 # 1st layer number of neurons.\n", + "n_hidden_2 = 256 # 2nd layer number of neurons.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# predictions Data.\n", + "df_predict_ds=pd.read_csv('./week3_exam_dataset_test.csv')\n", + "\n", + "data_predict_x = np.float32(df_predict_ds.values)\n", + "\n", + "# Training Data.\n", + "df_tr=pd.read_csv('./week3_exam_dataset_train.csv')\n", + "\n", + "#y Values dataset\n", + "df_tr_raw_y= df_tr['y']\n", + "df_tr_raw_values_y = df_tr_raw_y.values\n", + "y = np.float32(df_tr_raw_values_y)\n", + "#X values dataset\n", + "df_tr_raw_x= df_tr.drop('y',1)\n", + "df_tr_raw_values_x = df_tr_raw_x.values\n", + "X = np.float32(df_tr_raw_values_x)\n", + "if num_features==0:\n", + " num_features= df_tr_raw_x.shape[1]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_5\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_9 (Dense) (None, 256) 3840 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 256) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_2 (Batch (None, 256) 1024 \n", + "_________________________________________________________________\n", + "dense_10 (Dense) (None, 1) 257 \n", + "=================================================================\n", + "Total params: 5,121\n", + "Trainable params: 4,609\n", + "Non-trainable params: 512\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# define model\n", + "model = Sequential()\n", + "model.add(Dense(n_hidden_1, activation='relu', kernel_initializer='he_normal', input_shape=(num_features,)))\n", + "\n", + "# example of using dropout\n", + "model.add(Dropout(0.5))\n", + "\n", + "# example of using batch normalization\n", + "model.add(BatchNormalization())\n", + "\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the model\n", + "sgd = SGD(learning_rate=learning_rate, momentum=0.8)\n", + "model.compile(optimizer=sgd, loss='binary_crossentropy')\n", + "\n", + "# configure early stopping\n", + "es = EarlyStopping(monitor='val_loss', patience=5)\n", + "\n", + "# summarize the model\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# example of plotting a model\n", + "plot_model(model, 'model.png', show_shapes=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# fit the model\n", + "history = model.fit(X, y, epochs=training_steps, batch_size=batch_size, verbose=0, validation_split=0.3, callbacks=[es])\n", + "\n", + "# plot learning curves\n", + "pyplot.title('Learning Curves')\n", + "pyplot.xlabel('Epoch')\n", + "pyplot.ylabel('Cross Entropy')\n", + "pyplot.plot(history.history['loss'], label='train')\n", + "pyplot.plot(history.history['val_loss'], label='val')\n", + "pyplot.legend()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "distribution of values:\n", + " equal to 1: 79.2%\n", + " equal to 0: 20.8%\n", + "\n", + "checking answers:\n", + "none of the answers matches the output. Answer starts with: \n", + " '0 0 1 ...' -> matches [21]: 20.79% of values\n", + " '1 0 1 ...' -> matches [47]: 46.53% of values\n", + " '1 1 0 ...' -> matches [80]: 79.21% of values\n", + " '2 1 0 ...' -> matches [66]: 65.35% of values\n" + ] + } + ], + "source": [ + "# make a prediction\n", + "output_pre = model.predict(data_predict_x)\n", + "\n", + "output_pre=np.round(output_pre,0)\n", + "\n", + "def calStats(output, answer):\n", + " num_matches= np.sum(output[:min(len(output), len(answer))] == answer[:min(len(output), len(answer))])\n", + " percentage =round((num_matches / min(len(output), answer.shape[1]))*100,2)\n", + " return num_matches, percentage\n", + "\n", + "output=output_pre\n", + "\n", + "print(\"distribution of values:\")\n", + "print(\" equal to 1: \" + str( round(np.sum(output==1)/output.shape[0]*100,1) )+\"%\")\n", + "print(\" equal to 0: \" + str( round(np.sum(output==0)/output.shape[0]*100,1) )+\"%\")\n", + "\n", + "#checking answers\n", + "answer1=np.array([[0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1]])\n", + "answer2=np.array([[1,0,1,1,1,1,0,0,1,0,0,1,0,0,1,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,0,1,0,1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,1,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,0,1,1,0,1,1]])\n", + "answer3=np.array([[1,1,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,0,0,1,1,0,1,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,1,1,0]])\n", + "answer4=np.array([[2,1,0,2,0,1,0,2,1,2,1,2,1,1,2,1,1,1,1,2,0,1,3,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,3,1,1,1,1,1,2,1,0,1,1,1,0,1,1,2,1,3,1,1,1,0,0,1,1,2,1,1,1,1,1,1,1,2,1,2,0,1,1,2,1,0,0,2,1,1,1,2,0,1,1,1,1,1,2,1,1,0,1,1,0,3,1,0]])\n", + "\n", + "print(\"\")\n", + "print(\"checking answers:\")\n", + "\n", + "if (output==answer1).all():\n", + " print(\"answwer starts with '0 0 1'\")\n", + "elif (output==answer2).all():\n", + " print(\"answwer starts with '1 0 1'\")\n", + "elif (output==answer3).all():\n", + " print(\"answwer starts with '1 1 0'\")\n", + "elif (output==answer4).all():\n", + " print(\"answwer starts with '2 1 0'\")\n", + "else:\n", + " print(\"none of the answers matches the output. Answer starts with: \")\n", + " num_matches, percentage= calStats(output, answer1)\n", + " print (\" '0 0 1 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " num_matches, percentage= calStats(output, answer2)\n", + " print (\" '1 0 1 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " num_matches, percentage= calStats(output, answer3)\n", + " print (\" '1 1 0 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " num_matches, percentage= calStats(output, answer4)\n", + " print (\" '2 1 0 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 1114377ed6a7203bed112cc9dd70013c21803eb9 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Tue, 4 Aug 2020 18:29:31 +0200 Subject: [PATCH 09/13] Add files via upload --- .../Multilayer_Perceptron.ipynb | 74 +++++++------- .../Multilayer_Perceptron_With_Keras.ipynb | 99 +++++++++++-------- 2 files changed, 93 insertions(+), 80 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb index ad247b53..ce644370 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron.ipynb @@ -56,24 +56,24 @@ "# task type to perform\n", "taskRunning=\"classification\" # can be: classification / regression\n", "\n", + "#normalization of data\n", + "normalizeDataValues=True\n", + "normalizationType= \"max\" # accepts: max, mean\n", + "normalizationTypeBinary=True\n", + "\n", "# parameters initialization.\n", "num_classes = 1 # total classes : number of output results wanted\n", "num_features = 0 # data features : number of input variables on the dataset. a value of 0 loads from the dataset bellow\n", "\n", "# Training parameters.\n", - "learning_rate = 1\n", - "training_steps = 10000\n", + "learning_rate = 0.0000001\n", + "training_steps = 5000\n", "batch_size = 100\n", "display_step = 100\n", "\n", - "#normalization of data\n", - "normalizeDataValues=True\n", - "normalizationType= \"max\" # accepts: max, mean\n", - "normalizationTypeBinary=True\n", - "\n", "# Network parameters.\n", - "n_hidden_1 = 512 # 1st layer number of neurons.\n", - "n_hidden_2 = 512 # 2nd layer number of neurons." + "n_hidden_1 = 256 # 1st layer number of neurons.\n", + "n_hidden_2 = 256 # 2nd layer number of neurons." ] }, { @@ -104,7 +104,7 @@ "df_tr_raw_values_x = df_tr_raw_x.values\n", "data_tr_x = np.float32(df_tr_raw_values_x)\n", "\n", - "x_train, x_test, y_train, y_test = train_test_split(df_tr_raw_x, df_tr_raw_y, test_size=0.33, random_state=42)\n", + "x_train, x_test, y_train, y_test = train_test_split(df_tr_raw_x, df_tr_raw_y, test_size=0.33, random_state=1)\n", "\n", "# Convert to float32.\n", "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", @@ -322,7 +322,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -339,35 +339,35 @@ "[##############################] 100%\n", "\n", "================= Iteration Stats ================\n", - " step: 10000 of 10000\n", - " loss: 0.522784\n", + " step: 5000 of 5000\n", + " loss: 0.655206\n", "\n", - " step accuracy: 70.00 %\n", - " AVG: 77.28 %\n", - " Best: 90.00 %\n", + " step accuracy: 72.00 %\n", + " AVG: 76.32 %\n", + " Best: 86.00 %\n", "\n", " Trend slope: -0.000\n", " MAD Dispersion: nan\n", - " Skewness: Approximately symmetric distribution ( -0.12 )\n", + " Skewness: Approximately symmetric distribution ( -0.21 )\n", "\n", "================= Time ================\n", - " Elapsed: 0h, 2 min and 12 sec\n", + " Elapsed: 0h, 0 min and 47 sec\n", " ETC: 0h, 0 min and 0 sec\n", "\n", "================= Network Setup ================\n", " number of classes: 1\n", " number of features: 14\n", - " learning rate: 1\n", - " training steps: 10000\n", + " learning rate: 1e-07\n", + " training steps: 5000\n", " batch size: 100\n", - "1st layer n. of neurons: 512\n", - "2st layer n. of neurons: 512\n", + "1st layer n. of neurons: 256\n", + "2st layer n. of neurons: 256\n", " Normalized data: True, type: max, Discrete Binary 0/1\n", "\n", "Trainning Analysis Finished. Start testing test dataset...\n", "\n", "Accuracy of highest score in prediction dataset\n", - " Test Accuracy: 77.00 %\n" + " Test Accuracy: 76.00 %\n" ] } ], @@ -447,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -459,11 +459,11 @@ " equal to 0: 100.0%\n", "\n", "checking answers:\n", - "none of the answers matches the output\n", - "Answer starts with '0 0 1', matches [80]: 79.21% of values\n", - "Answer starts with '1 0 1', matches [55]: 54.46% of values\n", - "Answer starts with '1 1 0', matches [21]: 20.79% of values\n", - "Answer starts with '2 1 0', matches [15]: 14.85% of values\n" + "none of the answers matches the output. Answer starts with: \n", + " '0 0 1 ...' -> matches [80]: 79.21% of values\n", + " '1 0 1 ...' -> matches [55]: 54.46% of values\n", + " '1 1 0 ...' -> matches [21]: 20.79% of values\n", + " '2 1 0 ...' -> matches [15]: 14.85% of values\n" ] } ], @@ -477,12 +477,12 @@ "output_pre=np.round(run_prediction.numpy(),0)\n", "\n", "#assessing output value with its probability \n", - "ouput = np.empty(shape=(output_pre.shape[0],1))\n", + "output = np.empty(shape=(output_pre.shape[0],1))\n", "for i in range(output_pre.shape[0]):\n", " if ((output_pre[i][0]==1 and output_pre[i][1]==0) or (output_pre[i][0]==0 and output_pre[i][1]==1)):\n", - " res[i][0]=0\n", + " output[i][0]=0\n", " elif((output_pre[i][0]==1 and output_pre[i][1]==1) or (output_pre[i][0]==0 and output_pre[i][1]==0)):\n", - " res[i][0]=1\n", + " output[i][0]=1\n", "\n", "print(\"distribution of values:\")\n", "print(\" equal to 1: \" + str( round(np.sum(output==1)/output.shape[0]*100,1) )+\"%\")\n", @@ -506,15 +506,15 @@ "elif (output==answer4).all():\n", " print(\"answwer starts with '2 1 0'\")\n", "else:\n", - " print(\"none of the answers matches the output\")\n", + " print(\"none of the answers matches the output. Answer starts with: \")\n", " num_matches, percentage= calStats(output, answer1)\n", - " print (\"Answer starts with '0 0 1', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " print (\" '0 0 1 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", " num_matches, percentage= calStats(output, answer2)\n", - " print (\"Answer starts with '1 0 1', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " print (\" '1 0 1 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", " num_matches, percentage= calStats(output, answer3)\n", - " print (\"Answer starts with '1 1 0', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " print (\" '1 1 0 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", " num_matches, percentage= calStats(output, answer4)\n", - " print (\"Answer starts with '2 1 0', matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", + " print (\" '2 1 0 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", " " ] }, diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb index 0b3a1462..e4c17b53 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Multilayer_Perceptron_With_Keras.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,17 +25,16 @@ "# Training parameters.\n", "learning_rate = 0.0000001\n", "training_steps = 5000\n", - "batch_size = 24\n", - "display_step = 100\n", + "batch_size = 100\n", "\n", "# Network parameters.\n", - "n_hidden_1 = 256 # 1st layer number of neurons.\n", + "n_hidden_1 = 128 # 1st layer number of neurons.\n", "n_hidden_2 = 256 # 2nd layer number of neurons.\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -62,27 +61,29 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential_5\"\n", + "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_9 (Dense) (None, 256) 3840 \n", + "dense (Dense) (None, 128) 1920 \n", "_________________________________________________________________\n", - "dropout_3 (Dropout) (None, 256) 0 \n", + "dense_1 (Dense) (None, 256) 33024 \n", "_________________________________________________________________\n", - "batch_normalization_2 (Batch (None, 256) 1024 \n", + "dropout (Dropout) (None, 256) 0 \n", "_________________________________________________________________\n", - "dense_10 (Dense) (None, 1) 257 \n", + "batch_normalization (BatchNo (None, 256) 1024 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 1) 257 \n", "=================================================================\n", - "Total params: 5,121\n", - "Trainable params: 4,609\n", + "Total params: 36,225\n", + "Trainable params: 35,713\n", "Non-trainable params: 512\n", "_________________________________________________________________\n" ] @@ -92,6 +93,7 @@ "# define model\n", "model = Sequential()\n", "model.add(Dense(n_hidden_1, activation='relu', kernel_initializer='he_normal', input_shape=(num_features,)))\n", + "model.add(Dense(n_hidden_2, activation='relu', kernel_initializer='he_normal', input_shape=(num_features,)))\n", "\n", "# example of using dropout\n", "model.add(Dropout(0.5))\n", @@ -102,7 +104,7 @@ "model.add(Dense(1, activation='sigmoid'))\n", "# compile the model\n", "sgd = SGD(learning_rate=learning_rate, momentum=0.8)\n", - "model.compile(optimizer=sgd, loss='binary_crossentropy')\n", + "model.compile( loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", "\n", "# configure early stopping\n", "es = EarlyStopping(monitor='val_loss', patience=5)\n", @@ -113,17 +115,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] }, - "execution_count": 19, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -135,19 +137,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZmPJHwgk3wAFf900zn/2xf1LaUVfDXicl5lGhix6E+df6Z0Qf+v3YP2cGB30LCkvhrxf7J9yd+6h/KlxPNGz25+L123wdRen+MPta2GO2L2rrqcUP+RZQL//O34XJTtt7772pqalhzJgxlJSUcM4553DCCScwY8YMpk6dyp577pmQuFRZLIPDJ/8Hz/7Ej9JaegB86RrfYS1ePn0Z7jvZFwWd+5gvSumJla/7SmRLhbP/AqVd1u11rXotvH6z75DXXAu7Hw0HX+5j6qvE+NhFsPRRuOBpGBffMfN7S8NQJ6Cy2MzuMrMNZrY0yvoTzWyJmS0yswVmdkhYsUgS2PUL8PWXYM5NULnSd9p6+HzfhDNs5cvgL+f43tGn39vzJAD+4nrh8zvXvHTjRzD3W34gv9du9o8RveQVP4zHhIP79u7ouN/5YqzHvgYNlX23X0moMFsN3Q3M7mb9C8C+zrmpwFeBO0KMRZJBSipM+zJ8eyHM+i/46Fm4+QDfIqdhczjHrC2H+//D93c455G+eU7EsN18Mhi5Fzx0jh+DqSurF/rWSTftD+884ityL3sbTr3DN2UMQ1YBnHqXf0bGP74zsPp1SFShJQLn3EtARTfra93WcqlcQN8o6RuZeTDrKvj2WzDlDP8r+Yap8NotvnK0rzTX+2Kc2g3+uQtF4/pu33nD4fwngualV2xtXuqcLwa75wT44xfg0/n+aXWXL4XjfgtDJvRdDNGUTocjfuRbIr19X/jH60MDrSi8J3ryN4ZaR2BmE4AnnHOTo6w/GfgVMAI4zjn3WpTtLgYuBhg3btz0RNWsywC1bqmvTF7+IqRlwR5fgsmn+jL0nj7qs70dHjkP3n/CD62953F9G/OW47TBM1fBG7f7eGvX+/GK8kv8I0CnfyUxD/hpb4f7ToKyN+Hi+TB8j/jHsJM+/fRT8vPzKS4uxhLRmCAOnHNs2rSJmpoaJk6cuM267uoIEpoIIrY7DPipc+6oHe1TlcXSY6ve8EUo7/7NPxY0I89fwCefCrscAWkZse/rnz/ybf+/9Cv/+M0wOefvap79ERTvBgd/x9/pJLqpbPVaP/RH4Rj42gvhxtPSCP+6AZprIHe4nzqe5Z073L/uIKm3tLRQVlaWkHb68ZSVlUVpaSnp6dvWVfX7RBBs+ymwv3NuY3fbKRFIr7W1+g5fS/8K782FxkrIKoK95vikMOHQ7tvJv3kHPPk932/hmF/Hr6lq9RrIG9m/2vAvewYePMPfncz+VTjHqK/wdSUrX/V1MW1RnlmRkReRGIZDTvHW+Y7l4w7s+w578fLhs75BQg8fa9svH15vZrsBnzjnnJlNAzIADdQu4UtNg11m+enY38LyebD0MZ8Y3roXckfA3if5pFB6wLZt7z98Fp66wrfJn31tfPsrFIyO37FiNWm278vx+i3+rmqPo/t2/5Wr4M+nwuZP4bS7YO9TfNPYunKo2xhM5Vvf1wfvK1f6yvS6jb4jXIfhe4ZbmR6G8g/hnz+Ej5+DGRfC8b/v80OEdkdgZg8Cs4BhwHrgZ0A6gHPuNjO7EjgPaAEagCucc6/saL+6I5DQtDTAR8/5pPDhM77nbUHp1qSQkgZ/OsZ39LrgaV8pLb7Y5o4joWYdfONfkD+qb/a77h3fIqu53tfDTDx05/fR3u7v+Oo2+j4mz1zlW5AddTV8/hu962AXtobNMO86ePOPkJ4Dh18Z9J7fiSLMCAkrGgqDEoHERVONL/ZY+hh8/LwfZsFSfSXt1573D9CRrTZ8ALfP8v0gzv1r7y+wy+fBQ+dCZr7voDdyr76I0ieEx78Ny57yfU9OurXvEldfaWuFt+6B//ulTwbTz4cjfuxbkvWCEoFIbzRs9q2Dls/zTTX76qI02Cz4k3+63Bf/21do99SSR+Dv34Bhu8M5j/rK6L7kHCz8EzzzQ1/BfOJN4bX62lnL5/tBCDe8C+MP8fUuJVP6ZNdKBCISPufg4S/Dsqfhwmf9syN29vOv3ujHjhp/iC8O6osOetGUfwiPXQjrlsD0C/ywJBm54R2vOxWf+ibOHzzh+6Mc/Uv43Jw+rYPS8whEJHxmcMKNkDcKHr1w5zrvtbfB01f6JLD3KfDlv4abBMD3ffjaC/7uZeHd8L+Hw5pF4R6zs6YaeP5q3wP+kxfhCz+Bb74Je50Y14YISgQi0ndyhsKpf/TDXj91RWyfaWmER77ihxQ/8Fv+OQ7x6iORluGLss6bC811fuTXV64P/yFB7e3w9v3wh+n+2RqTT/VDoxz2/YQ0b1UiEJG+Nf4gOOwHsPhBWPJw99vWV/geyu//w3fO+9I1iWnJs8vhvsXTpGPg+Z/BfSdC1epwjrXydbjjCzD3Ul8M9LUX4OTbEtoAQYlARPreYVfA2JnwxHejjwBbuRLumu3b+592V/g9tHckZ6gfPXbOTVC20Peafq+PHibvHGxe4YvM7vqSb2p7yh/hq8/u3FDjIVFlsYiEo3Il3HqIH031q//cdnjude/An0/zfTfOegAm9LNR6Dd94ofaXvOWf9DQ7Ot23G/EOd+ZrWK5nzZ9snW+4lNoqvJjXR10GRxyedwrpvtlz2IRGeSKxsGcG3z5/4vX+E5csLWPQFYBfPWZ/tkct3hX3/Jp3q/g5d/DZ6/6Hsmjp/lf8xXLoSLyQh9c7Jtrt+7DUv05GLqLf0rc0F3gc8f37Si1fUSJQETCs/fJftjsV673Q3rUboC/XxpeH4G+lJoOR/4Udj0yeIToF30ldkv91m1S0vzQ30N3gfEH+9eOqWhc7x5SFEcqGhKRcDXX+V7HNeugqdoP6nfGn8NvHtqXGir9s5rb22DoRH+hL97VD0GSOjB+T6toSEQSJyPXNwm9K3gOxEm3Jn4I7Z2VXQRH/yLRUYRGiUBEwlcyBa74OHE9d6Vbaj4qIvGhJNBvKRGIiCQ5JQIRkSSnRCAikuSUCEREkpwSgYhIklMiEBFJckoEIiJJTolARCTJKRGIiCQ5JQIRkSSnRCAikuSUCEREkpwSgYhIklMiEBFJckoEIiJJTolARCTJKRGIiCQ5JQIRkSQXWiIws7vMbIOZLY2y/hwzWxJMr5rZvmHFIiIi0YV5R3A3MLub9Z8ChzvnpgC/AG4PMRYREYlih4nAzL5lZkN2dsfOuZeAim7Wv+qc2xy8fR0o3dljiIhI78VyRzAKeNPMHjaz2WZmIcRxIfB0tJVmdrGZLTCzBeXl5SEcXkQkee0wETjnfgzsDtwJfAX4yMz+x8x27YsAzOwIfCK4spsYbnfOzXDOzRg+fHhfHFZERAIx1RE45xywLphagSHAo2b2694c3MymAHcAJzrnNvVmXyIi0jNpO9rAzC4Dzgc24i/aVzjnWswsBfgI+EFPDmxm44C/Al92zn3Yk32IiEjv7TARAMOAU5xzn0UudM61m9nx0T5kZg8Cs4BhZlYG/AxIDz57G/BToBi4Jah2aHXOzejJHyEiIj1nvtRnBxuZTQMOARzwL+fcW2EHFs2MGTPcggULEnV4EZEBycwWRvuxHUvz0Z8A9+B/vQ8D/mRmP+7bEEVEJFFiKRo6G9jPOdcIYGbXAm8BvwwzMBERiY9YWg2tALIi3mcCn4QSjYiIxF0sdwRNwLtm9hy+juCLwCtmdiOAc+6yEOMTEZGQxZII/hZMHeaFE4qIiCTCDhOBc+4eM8sA9ggWLXPOtYQbloiIxEssHcpm4VsNrQAMGGtm5weDyomIyAAXS9HQ74CjnXPLAMxsD+BBYHqYgYmISHzE0moovSMJAATDQaSHF5KIiMRTLHcEC83sTuC+4P05wMLwQhIRkXiKJRFcAnwTuAxfR/AScEuYQYmISPx0mwiCEUYXOucmA7+PT0giIhJP3dYROOfagcXBkNEiIjIIxVI0VILvWfwGUNex0Dk3J7SoREQkbmJJBD8PPQoREUmYWBLBsc65bZ4nbGbXAfPDCUlEROIpln4EX+xi2TF9HYiIiCRG1DsCM/sGcCmwi5ktiViVD7wadmAiIhIf3RUNPQA8DfwKuCpieY1zriLUqEREJG6iJgLnXBVQBZxlZqnAyGD7PDPLc86tjFOMIiISolhGH/0WcDWwHmgPFjtgSnhhiYhIvMTSauhyYJJzblPYwYiISPzF0mpoFb6ISEREBqFY7giWA/PM7En884sBcM5p7CERkUEglkSwMpgygklERAaRWJ5ZvN0QE2YWSwIREZEBIGodgZm9EjF/X6fVb4QWkYiIxFV3lcW5EfOTO62zEGIREZEE6C4RuCjzXb0XEZEBqruy/iIzOxmfLIrM7JRguQGFoUcmIiJx0d0dwXxgDnB8MH9CMB2Pf25xt8zsLjPbYGZLo6zf08xeM7MmM/v+zocuIiJ9obuxhi7o5b7vBm4C7o2yvgK4DDipl8cREZFeiKVncY84517CX+yjrd/gnHsTaAkrBhER2bHQEoGIiAwMAyIRmNnFZrbAzBaUl5cnOhwRkUFlh4nAzP7DzPKD+R+b2V/NbFr4oW3lnLvdOTfDOTdj+PDh8Ty0iMigF8sdwU+cczVmdgjwJeAe4NZwwxIRkXiJZcygtuD1OOBW59xcM7t6Rx8ysweBWcAwMysDfgakAzjnbjOzUcACoABoN7PLgb2cc9U7/VeIiEiPxZIIVpvZ/wJHAdeZWSYx3Ek4587awfp1QGlMUYqISGhiKRo6HfgnMNs5VwkMBa4INSoREYmbWO4ISoAnnXNNZjYL/6ziaJ3ERERkgInljuAxoM3MdgPuBCYCD4QalYiIxE0siaDdOdcKnAJc75z7T/xdgoiIDAKxJIIWMzsLOA94IliWHl5IIiIST7EkgguAA4FrnHOfmtlE4M/hhiUiIvESSzPQ94DvA++Y2WSgzDl3beiRiYhIXOyw1VDQUugeYAX+oTRjzez8YHRREREZ4GJpPvo74Gjn3DIAM9sDeBCYHmZgIiISH7HUEaR3JAEA59yHqLJYRGTQiOWOYKGZ3QncF7w/B1gYXkgiIhJPsSSCS4Bv4h8rafjnFd8SZlAiIhI/3SYCM0sBFjrnJgO/j09IIiIST93WETjn2oHFZjYuTvGIiEicxTro3Ltm9gZQ17HQOTcntKhERCRuYkkEPw89ChERSZioiSAYbXSkc25+p+WHAavDDkxEROKjuzqC64GaLpbXB+tERGQQ6C4RTHDOLem80Dm3AJgQWkQiIhJX3SWCrG7WZfd1ICIikhjdJYI3zeyizgvN7ELUs1hEZNDortXQ5cDfzCxySIkZQAZwctiBiYhIfERNBM659cBBZnYEMDlY/KRz7v/iEpmIiMTFDvsROOdeBF6MQywiIpIAsQxDLSIig5gSgYhIklMiEBFJckoEIiJJTolARCTJKRGIiCQ5JQIRkSQXWiIws7vMbIOZLY2y3szsRjP72MyWmNm0sGIREZHowrwjuBuY3c36Y4Ddg+li4NYQYxERkShCSwTOuZeAim42ORG413mvA0VmVhJWPCIi0rVE1hGMAVZFvC8Llm3HzC42swVmtqC8vDwuwYmIJItEJgLrYpnrakPn3O3OuRnOuRnDhw8POSwRkeSSyERQBoyNeF8KrElQLCIiSSuRieBx4Lyg9dBMoMo5tzaB8YiIJKUdDkPdU2b2IDALGGZmZcDPgHQA59xtwFPAscDHQD1wQVixiIhIdKElAufcWTtY74BvhnV8ERGJjXoWi4gkOSUCEZEkp0QgIpLklAhERJKcEoGISJJTIhARSXJKBCIiSU6JQEQkySkRiIgkOSUCEZEkp0QgIpLklAhERJKcEoGISJJTIhARSXJKBCIiSU6JQEQkySkRiIgkOSUCEZEkp0QgIpLklAhERJKcEoGISJJTIhARSXJKBCIiSU6JQEQkySkRiIgkOSUCEZEkp0QgIpLklAhERJKcEoGISJJTIhARSXJKBCIiSS7URGBms81smZl9bGZXdbF+iJn9zcyWmNkbZjY5zHhERGR7oSUCM0sFbgaOAfYCzjKzvTpt9kNgkXNuCnAecENY8YiISNfSQtz3AcDHzrnlAGb2EHAi8F7ENnsBvwJwzn1gZhPMbKRzbn2IccVVW7tjZUU9y9bVULa5nuH5mZQOyaZ0SA7D8zJJSbFEhygiSS7MRDAGWBXxvhpIsusAAA7OSURBVAz4fKdtFgOnAK+Y2QHAeKAU2CYRmNnFwMUA48aN61EwTa1ttLU7cjLC+ZOdc6yrbmTZuho+XF/DB8HrR+traWpt7/IzGakpjC7KonRIDmOKsn2CGJrNmKIcSodkM7Igi9RBlCha29pJS1W1lEh/E2Yi6OoK5jq9vxa4wcwWAe8AbwOt233IuduB2wFmzJjReR8xefGDDVzy57fISk+hODeT4rwMhuZmbJkvzg3e5/llQ3MzGJaXSXZG6nb72lzXzLL1NSxbV8Oy9TV8GLzWNG4NfWRBJnuMzOfLM8ezx6h8Jo3MZ+zQHDbVNlG2uYGyygbKNtdTtrmB1ZsbeOGDDWysbdrmOGkpRklRFqVBYhgzJJvRRdmMKfKvJYVZZKVvH18iVTW08NmmOlZsquezjf51ZYV/La9pYtzQHPYZU8jeYwrYZ0wh+4wppCgnI9FhiyS1MBNBGTA24n0psCZyA+dcNXABgJkZ8Gkw9bndRuRz5ew9qahrYlNtM5vqmtlY28SH62rYWNdMc5Rf7dnpqUFSyCArPZXlG+sor9l6wS7MTmfSyHxOnDqaSaMKmDQynz1G5kW9uA3NzWD3kfldrmtsaWN1ZcOW5LAlUVQ28NJH5WyoacJ1SoPD8jIoKcxmdFHWdkliTFE2w/q4+Mk5R0Vds7/Qb6rjs+C14/3m+pZtth9ZkMn44lyOmDScEflZLN9Yy5LVlTz5ztot25QOyWafMYVMDqZ9xhQyNFfJQSRezHW+svTVjs3SgA+BI4HVwJvA2c65dyO2KQLqnXPNZnYRcKhz7rzu9jtjxgy3YMGCPo3VOUddcxsVtc1srGuioraZirqt85vq/FTf1MrEYblMGpXPHiPzmTQqnxH5mfgcFr6m1jbWVzWxurKBNZUNrK1qYHVlI2uC92sqG6hrbtvmM+mptiVRjCzIIsWMtnZHm3O0tzvanaOtHdpdx7x/bW9nyzZtztHuoKmljdWbG6hp2nrnYwajC7OZMCyH8cW5TCjueM1l3NCcLu+oACrrm1m6upqla6p4Z3UVS1dX8dmm+i3rxxRlM3lMAZNHFzK51CeHYXmZ4ZxYkSRgZgudczO6XBdWIggOfCxwPZAK3OWcu8bMLgFwzt1mZgcC9wJt+ErkC51zm7vbZxiJYLBwzlHd0Mqaqq2JITJRbAjuZFJTDDNINQvmjdQU/96CZX7eb9uxTUaqMaYo21/ogwt/6ZBsMtP6pniqqqGFd9f4pPDO6mqWrq7i0411W9aPKsjaktBG5GcyIuJ1ZEEmI/KzGJKTHrfELDKQJCwRhEGJILnUNLbw7hqfFN5bW826qkY21DSxvrpxmzqZDhmpKQzPz2R4fuaW5DAiP9Mnj4JMxg7NYeyQHDLSVGktyaW7RBBmHYFIr+VnpTNzl2Jm7lK83bqG5jbKa5pYX9PIhuomNtQ0sj543VDdxKcb6/j3pxVUdqq3SDEYXZTti6+Kc2IuzoqFc47aplYfR3Uj64OY1lU1Ul7bRFNLO23t7bS2O1rbfFFcS3s7bcH71mBd5Pu2dkdLmy+yG5qbwciCLEoKsxhVkMWoyNfCLIbnZaplVhcaW9qorG8hPyuN3Exd9jrTGZEBKzsjlXHFOYwrzul2u8aWIGFUN7Kyon5LxfaKTfU8/c7aqBXcHQlifHEOE4pzGVOUTU1ja3Bxb9zm7mR9tU8+66obqe9UTwOQl5nG8PxMstJTSUsx0lLNv6akkJmeRmowH7kuNSWF9FRfNJceXNw31TWzvqqRt1ZuZn1VE81t2zZySDEYnp/JqMJsRhVkUlKYvTVxFGax56j8QdFKq7m1nYq6ZjYFjT8qgsYfFXXNQWOQJl+3F6yrjajXKspJZ3RhRwu8oJFFRIu8ZOzfo6IhSXpV9S18FjRxXRnRAqqjyWt3MtJSGFmQyaiCLF9Xke/rK0YWZAWTr8PIC+FXaEcLrnVBUtry2mk+snIfYOKwXPYtLWTfsUXsO7aIvUoK4tYMubWtnbqmNmqbW6lraqW2yb/6+bYty+qbW/12Tdtut7m+hY21TV0WC4Jvcu2bgWdSHNEcvDgvg6KcdKobWlldWc+aoO5sdWXDdvuKbGDRkRzGFGVTUpRNeorR0u5oaW2npa29y/nW9nZa2hzNnebb2h1DctIpCVr2jS7MoqQoO5TvRldURyDSQ3VNrays8ImhbHMDBdnpjCzwxTEjCzIpzO7/ldN1Ta2sq25k9eYGlq6pYvGqShatqmR9tU9y6anG50oK2LfUJ4apYwvZZVhej34VN7a0Uba5gVUV9ayMmFYFU+dWbdGkphi5GankZfqinNzMNPIy0yjKSWdYXuY2fX46+gEV52ZSkJ220/8e1Y0t2zSuWL25YZuWeOuqG2nv4WXS380Z6SkppKYa1Q0t2+2rICvNJ4Yin3xKCoPEU+gT0ajCrC13hL2hRCAi21lX1ciiVZUsLqtk8apKlpRVbSlCyc9MY8rYwi3JYb+xRYwoyMI5x6a6Zj7bVN/lxX5ddeM2fV2y01MZNzTHV9IPzWZITkZwUU/1F/iMjgv91ot+XmYamWkp/SbBtra1s666kbVVjbS1O9JTU8hITSEt1RfZpQevaalGRmrKlvn0lJTtkmlrWzvra5oiEk0ja6u2zq+patiuTssMRuT7Yr7TZ4zl7M/3bHQFVRaLyHZGFWYxu3AUsyePAvy4WMvLayOSQxW3v7Sc1uAn7PD8TOqaWrerAxlZkMn4obkctOswxg3NYVxx9paL//C8+PWzCUtaagqlQ3IoHdJ9XVSs++ooaoqmvrl1+wRR2cDaqsZeHz8a3RGISFSNLW28u6aaxasqeW9tNQVZ6Ywbmu0r6Yf6i2N/G+ZEuqY7AhHpkaz0VKaPH8L08UMSHYqESA2ORUSSnBKBiEiSUyIQEUlySgQiIklOiUBEJMkpEYiIJDklAhGRJKdEICKS5AZcz2IzKwc+6+HHhwEb+zCcvtbf44P+H6Pi6x3F1zv9Ob7xzrnhXa0YcImgN8xsQbQu1v1Bf48P+n+Miq93FF/v9Pf4olHRkIhIklMiEBFJcsmWCG5PdAA70N/jg/4fo+LrHcXXO/09vi4lVR2BiIhsL9nuCEREpBMlAhGRJDcoE4GZzTazZWb2sZld1cV6M7Mbg/VLzGxaHGMba2Yvmtn7ZvaumX2ni21mmVmVmS0Kpp/GK77g+CvM7J3g2Ns9Di7B529SxHlZZGbVZnZ5p23ifv7M7C4z22BmSyOWDTWz58zso+C1y6e77Oj7GmJ8vzGzD4J/w7+ZWVGUz3b7fQgxvqvNbHXEv+OxUT6bqPP3l4jYVpjZoiifDf389ZpzblBNQCrwCbALkAEsBvbqtM2xwNOAATOBf8cxvhJgWjCfD3zYRXyzgCcSeA5XAMO6WZ+w89fFv/U6fEeZhJ4/4DBgGrA0YtmvgauC+auA66L8Dd1+X0OM72ggLZi/rqv4Yvk+hBjf1cD3Y/gOJOT8dVr/O+CniTp/vZ0G4x3BAcDHzrnlzrlm4CHgxE7bnAjc67zXgSIzK4lHcM65tc65t4L5GuB9YEw8jt2HEnb+OjkS+MQ519Oe5n3GOfcSUNFp8YnAPcH8PcBJXXw0lu9rKPE55551zrUGb18HSvv6uLGKcv5ikbDz18HMDDgdeLCvjxsvgzERjAFWRbwvY/sLbSzbhM7MJgD7Af/uYvWBZrbYzJ42s73jGhg44FkzW2hmF3exvl+cP+BMov/nS+T56zDSObcW/A8AYEQX2/SXc/lV/F1eV3b0fQjTt4Kiq7uiFK31h/N3KLDeOfdRlPWJPH8xGYyJwLpY1rmNbCzbhMrM8oDHgMudc9WdVr+FL+7YF/gD8Pd4xgYc7JybBhwDfNPMDuu0vj+cvwxgDvBIF6sTff52Rn84lz8CWoH7o2yyo+9DWG4FdgWmAmvxxS+dJfz8AWfR/d1Aos5fzAZjIigDxka8LwXW9GCb0JhZOj4J3O+c+2vn9c65audcbTD/FJBuZsPiFZ9zbk3wugH4G/72O1JCz1/gGOAt59z6zisSff4irO8oMgteN3SxTaK/i+cDxwPnuKBAu7MYvg+hcM6td861OefagT9GOW6iz18acArwl2jbJOr87YzBmAjeBHY3s4nBr8Yzgcc7bfM4cF7Q+mUmUNVxCx+2oDzxTuB959zvo2wzKtgOMzsA/++0KU7x5ZpZfsc8vkJxaafNEnb+IkT9FZbI89fJ48D5wfz5wNwutonl+xoKM5sNXAnMcc7VR9kmlu9DWPFF1judHOW4CTt/gaOAD5xzZV2tTOT52ymJrq0OY8K3avkQ35rgR8GyS4BLgnkDbg7WvwPMiGNsh+BvXZcAi4Lp2E7xfQt4F98C4nXgoDjGt0tw3MVBDP3q/AXHz8Ff2AsjliX0/OGT0lqgBf8r9UKgGHgB+Ch4HRpsOxp4qrvva5zi+xhfvt7xPbytc3zRvg9xiu++4Pu1BH9xL+lP5y9YfnfH9y5i27ifv95OGmJCRCTJDcaiIRER2QlKBCIiSU6JQEQkySkRiIgkOSUCEZEkp0Qg0omZtdm2I5z22YiWZjYhcgRLkf4gLdEBiPRDDc65qYkOQiRedEcgEqNgXPnrzOyNYNotWD7ezF4IBkd7wczGBctHBuP8Lw6mg4JdpZrZH80/j+JZM8tO2B8lghKBSFeyOxUNnRGxrto5dwBwE3B9sOwm/LDcU/ADt90YLL8RmO/84HfT8D1LAXYHbnbO7Q1UAqeG/PeIdEs9i0U6MbNa51xeF8tXAF9wzi0PBg5c55wrNrON+OEPWoLla51zw8ysHCh1zjVF7GMC8Jxzbvfg/ZVAunPul+H/ZSJd0x2ByM5xUeajbdOVpoj5NlRXJwmmRCCyc86IeH0tmH8VP+olwDnAK8H8C8A3AMws1cwK4hWkyM7QLxGR7WV3ehD5M865jiakmWb2b/yPqLOCZZcBd5nZFUA5cEGw/DvA7WZ2If6X/zfwI1iK9CuqIxCJUVBHMMM5tzHRsYj0JRUNiYgkOd0RiIgkOd0RiIgkOSUCEZEkp0QgIpLklAhERJKcEoGISJL7/wI2FTuZRO7hAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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" ] @@ -162,19 +169,32 @@ "# fit the model\n", "history = model.fit(X, y, epochs=training_steps, batch_size=batch_size, verbose=0, validation_split=0.3, callbacks=[es])\n", "\n", - "# plot learning curves\n", + "# summarize history for loss\n", "pyplot.title('Learning Curves')\n", "pyplot.xlabel('Epoch')\n", - "pyplot.ylabel('Cross Entropy')\n", + "pyplot.ylabel('Cross Entropy (loss)')\n", "pyplot.plot(history.history['loss'], label='train')\n", "pyplot.plot(history.history['val_loss'], label='val')\n", "pyplot.legend()\n", - "pyplot.show()" + "pyplot.show()\n", + "\n", + "\n", + "# summarize history for accuracy\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.title('model accuracy')\n", + "plt.ylabel('accuracy')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper left')\n", + "plt.show()\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -182,15 +202,15 @@ "output_type": "stream", "text": [ "distribution of values:\n", - " equal to 1: 79.2%\n", - " equal to 0: 20.8%\n", + " equal to 1: 0.0%\n", + " equal to 0: 100.0%\n", "\n", "checking answers:\n", "none of the answers matches the output. Answer starts with: \n", - " '0 0 1 ...' -> matches [21]: 20.79% of values\n", - " '1 0 1 ...' -> matches [47]: 46.53% of values\n", - " '1 1 0 ...' -> matches [80]: 79.21% of values\n", - " '2 1 0 ...' -> matches [66]: 65.35% of values\n" + " '0 0 1 ...' -> matches [80]: 79.21% of values\n", + " '1 0 1 ...' -> matches [55]: 54.46% of values\n", + " '1 1 0 ...' -> matches [21]: 20.79% of values\n", + " '2 1 0 ...' -> matches [15]: 14.85% of values\n" ] } ], @@ -240,13 +260,6 @@ " print (\" '2 1 0 ...' -> matches [\"+ str(num_matches)+\"]: \"+ str(percentage) +\"% of values\")\n", " " ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From fc50ae7a849f2323b12be7633b2a0326fecaa0ef Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Mon, 17 Aug 2020 13:15:21 +0200 Subject: [PATCH 10/13] Add files via upload notebook using tensorflow.keras --- ...yer_Perceptron_with_tensorflow.keras.ipynb | 4176 +++++++++++++++++ 1 file changed, 4176 insertions(+) create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/Advanced_Multilayer_Perceptron_with_tensorflow.keras.ipynb diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Advanced_Multilayer_Perceptron_with_tensorflow.keras.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Advanced_Multilayer_Perceptron_with_tensorflow.keras.ipynb new file mode 100644 index 00000000..5bf974fa --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Advanced_Multilayer_Perceptron_with_tensorflow.keras.ipynb @@ -0,0 +1,4176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on: https://github.com/buomsoo-kim/Easy-deep-learning-with-Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced MLP\n", + "- Advanced techniques for training neural networks\n", + " - Weight Initialization\n", + " - Nonlinearity (Activation function)\n", + " - Optimizers\n", + " - Batch Normalization\n", + " - Dropout (Regularization)\n", + " - Model Ensemble" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.datasets import mnist\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.utils import to_categorical" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset\n", + "- MNIST dataset\n", + "- source: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label: 5\n" + ] + } + ], + "source": [ + "plt.imshow(X_train[0]) # show first number in the dataset\n", + "plt.show()\n", + "print('Label: ', y_train[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label: 7\n" + ] + } + ], + "source": [ + "plt.imshow(X_test[0]) # show first number in the dataset\n", + "plt.show()\n", + "print('Label: ', y_test[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# reshaping X data: (n, 28, 28) => (n, 784)\n", + "X_train = X_train.reshape((X_train.shape[0], -1))\n", + "X_test = X_test.reshape((X_test.shape[0], -1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# use only 33% of training data to expedite the training process\n", + "X_train, _ , y_train, _ = train_test_split(X_train, y_train, test_size = 0.67, random_state = 7)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# converting y data into categorical (one-hot encoding)\n", + "y_train = to_categorical(y_train)\n", + "y_test = to_categorical(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(19800, 784) (10000, 784) (19800, 10) (10000, 10)\n" + ] + } + ], + "source": [ + "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.utils.multiclass import unique_labels\n", + "import numpy as np\n", + "def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=None, cmap=plt.cm.Blues):\n", + " \"\"\"\n", + " This function prints and draws the confusion matrix.\n", + " Normalization can be applied by setting normalize = True.\n", + " \"\"\"\n", + " if not isinstance(y_pred[0], (np.int64, np.uint8)):\n", + " _y_pred = np.eye(y_pred.shape[1])[y_pred.argmax(1)]\n", + " _y_pred = [np.argmax(x) for x in _y_pred]\n", + " _y_pred = np.rint(_y_pred)\n", + " _y_pred = np.array([int(x) for x in _y_pred])\n", + " else:\n", + " _y_pred = y_pred.copy()\n", + " if not isinstance(y_pred[0], (np.int64, np.uint8)):\n", + " _y_true = [np.argmax(x) for x in y_true]\n", + " _y_true = np.rint(_y_true)\n", + " _y_true = np.array([int(x) for x in _y_true])\n", + " else:\n", + " _y_true = y_true.copy()\n", + " \n", + " if not title:\n", + " if normalize:\n", + " title = 'Normalized Confusion Matrix'\n", + " else:\n", + " title = 'Confusion Matrix, without normalization.'\n", + "\n", + " cm = confusion_matrix(_y_true, _y_pred)\n", + " classes = classes[unique_labels(_y_true, _y_pred)]\n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + " print('Normalized Confusion Matrix')\n", + " else:\n", + " print('Confusion Matrix, without normalization.')\n", + "\n", + " print(cm)\n", + "\n", + " fig, ax = plt.subplots()\n", + " im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " ax.figure.colorbar(im, ax=ax)\n", + " ax.set(xticks=np.arange(cm.shape[1]),\n", + " yticks=np.arange(cm.shape[0]),\n", + " xticklabels=classes, yticklabels=classes,\n", + " title=title,\n", + " ylabel='True Label',\n", + " xlabel='Predicted Label')\n", + "\n", + " plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n", + " rotation_mode=\"anchor\")\n", + "\n", + " fmt = '.2f' if normalize else 'd'\n", + " thresh = cm.max() / 2.\n", + " for i in range(cm.shape[0]):\n", + " for j in range(cm.shape[1]):\n", + " ax.text(j, i, format(cm[i, j], fmt),\n", + " ha=\"center\", va=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + " fig.tight_layout()\n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic MLP model\n", + "- Naive MLP model without any alterations" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Activation, Dense\n", + "from tensorflow.keras import optimizers" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(Dense(50, input_shape = (784, )))\n", + "model.add(Activation('sigmoid'))\n", + "model.add(Dense(50))\n", + "model.add(Activation('sigmoid'))\n", + "model.add(Dense(50))\n", + "model.add(Activation('sigmoid'))\n", + "model.add(Dense(50))\n", + "model.add(Activation('sigmoid'))\n", + "model.add(Dense(10))\n", + "model.add(Activation('softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "sgd = optimizers.SGD(lr = 0.001)\n", + "model.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "55/55 [==============================] - 0s 9ms/step - loss: 2.4289 - accuracy: 0.0875 - val_loss: 2.4191 - val_accuracy: 0.0921\n", + "Epoch 2/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.4051 - accuracy: 0.0875 - val_loss: 2.3983 - val_accuracy: 0.0921\n", + "Epoch 3/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3866 - accuracy: 0.0875 - val_loss: 2.3815 - val_accuracy: 0.0921\n", + "Epoch 4/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3716 - accuracy: 0.0875 - val_loss: 2.3679 - val_accuracy: 0.0921\n", + "Epoch 5/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3594 - accuracy: 0.0875 - val_loss: 2.3570 - val_accuracy: 0.0921\n", + "Epoch 6/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3496 - accuracy: 0.0875 - val_loss: 2.3481 - val_accuracy: 0.0921\n", + "Epoch 7/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3416 - accuracy: 0.0875 - val_loss: 2.3409 - val_accuracy: 0.0921\n", + "Epoch 8/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3351 - accuracy: 0.0875 - val_loss: 2.3348 - val_accuracy: 0.0921\n", + "Epoch 9/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3297 - accuracy: 0.0874 - val_loss: 2.3298 - val_accuracy: 0.0921\n", + "Epoch 10/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3252 - accuracy: 0.0877 - val_loss: 2.3258 - val_accuracy: 0.0956\n", + "Epoch 11/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3215 - accuracy: 0.0932 - val_loss: 2.3223 - val_accuracy: 0.0896\n", + "Epoch 12/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3184 - accuracy: 0.0931 - val_loss: 2.3194 - val_accuracy: 0.0870\n", + "Epoch 13/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3158 - accuracy: 0.0999 - val_loss: 2.3169 - val_accuracy: 0.0928\n", + "Epoch 14/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3136 - accuracy: 0.1014 - val_loss: 2.3148 - val_accuracy: 0.0929\n", + "Epoch 15/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3117 - accuracy: 0.1014 - val_loss: 2.3132 - val_accuracy: 0.0929\n", + "Epoch 16/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3103 - accuracy: 0.1014 - val_loss: 2.3117 - val_accuracy: 0.0929\n", + "Epoch 17/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3090 - accuracy: 0.1014 - val_loss: 2.3105 - val_accuracy: 0.0929\n", + "Epoch 18/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3079 - accuracy: 0.1014 - val_loss: 2.3094 - val_accuracy: 0.0929\n", + "Epoch 19/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3069 - accuracy: 0.1014 - val_loss: 2.3085 - val_accuracy: 0.0929\n", + "Epoch 20/100\n", + "55/55 [==============================] - 1s 12ms/step - loss: 2.3061 - accuracy: 0.1014 - val_loss: 2.3077 - val_accuracy: 0.0929\n", + "Epoch 21/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3054 - accuracy: 0.1014 - val_loss: 2.3070 - val_accuracy: 0.0934\n", + "Epoch 22/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3048 - accuracy: 0.1065 - val_loss: 2.3064 - val_accuracy: 0.1170\n", + "Epoch 23/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3043 - accuracy: 0.1478 - val_loss: 2.3060 - val_accuracy: 0.1414\n", + "Epoch 24/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3039 - accuracy: 0.1336 - val_loss: 2.3055 - val_accuracy: 0.1212\n", + "Epoch 25/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3035 - accuracy: 0.1190 - val_loss: 2.3052 - val_accuracy: 0.1125\n", + "Epoch 26/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3032 - accuracy: 0.1146 - val_loss: 2.3048 - val_accuracy: 0.1118\n", + "Epoch 27/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3029 - accuracy: 0.1143 - val_loss: 2.3045 - val_accuracy: 0.1118\n", + "Epoch 28/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.3027 - accuracy: 0.1143 - val_loss: 2.3043 - val_accuracy: 0.1118\n", + "Epoch 29/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3025 - accuracy: 0.1143 - val_loss: 2.3041 - val_accuracy: 0.1118\n", + "Epoch 30/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3023 - accuracy: 0.1143 - val_loss: 2.3039 - val_accuracy: 0.1118\n", + "Epoch 31/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3021 - accuracy: 0.1143 - val_loss: 2.3038 - val_accuracy: 0.1118\n", + "Epoch 32/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3020 - accuracy: 0.1143 - val_loss: 2.3036 - val_accuracy: 0.1118\n", + "Epoch 33/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3019 - accuracy: 0.1143 - val_loss: 2.3035 - val_accuracy: 0.1118\n", + "Epoch 34/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3018 - accuracy: 0.1143 - val_loss: 2.3034 - val_accuracy: 0.1118\n", + "Epoch 35/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3016 - accuracy: 0.1143 - val_loss: 2.3033 - val_accuracy: 0.1118\n", + "Epoch 36/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3016 - accuracy: 0.1143 - val_loss: 2.3032 - val_accuracy: 0.1118\n", + "Epoch 37/100\n", + "55/55 [==============================] - 0s 9ms/step - loss: 2.3015 - accuracy: 0.1143 - val_loss: 2.3031 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[==============================] - 0s 8ms/step - loss: 2.3010 - accuracy: 0.1143 - val_loss: 2.3026 - val_accuracy: 0.1118\n", + "Epoch 45/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3010 - accuracy: 0.1143 - val_loss: 2.3026 - val_accuracy: 0.1118\n", + "Epoch 46/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.3009 - accuracy: 0.1143 - val_loss: 2.3025 - val_accuracy: 0.1118\n", + "Epoch 47/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.3009 - accuracy: 0.1143 - val_loss: 2.3025 - val_accuracy: 0.1118\n", + "Epoch 48/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3009 - accuracy: 0.1143 - val_loss: 2.3024 - val_accuracy: 0.1118\n", + "Epoch 49/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3008 - accuracy: 0.1143 - val_loss: 2.3024 - val_accuracy: 0.1118\n", + "Epoch 50/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3008 - accuracy: 0.1143 - val_loss: 2.3024 - val_accuracy: 0.1118\n", + "Epoch 51/100\n", + "55/55 [==============================] - 1s 9ms/step - loss: 2.3007 - accuracy: 0.1143 - val_loss: 2.3023 - val_accuracy: 0.1118\n", + "Epoch 52/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.3007 - accuracy: 0.1143 - val_loss: 2.3023 - val_accuracy: 0.1118\n", + "Epoch 53/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3007 - accuracy: 0.1143 - val_loss: 2.3022 - val_accuracy: 0.1118\n", + "Epoch 54/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.3006 - accuracy: 0.1143 - val_loss: 2.3022 - val_accuracy: 0.1118\n", + "Epoch 55/100\n", + "55/55 [==============================] - 1s 11ms/step - loss: 2.3006 - accuracy: 0.1143 - val_loss: 2.3021 - val_accuracy: 0.1118\n", + "Epoch 56/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3005 - accuracy: 0.1143 - val_loss: 2.3021 - val_accuracy: 0.1118\n", + "Epoch 57/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3005 - accuracy: 0.1143 - val_loss: 2.3021 - val_accuracy: 0.1118\n", + "Epoch 58/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3005 - accuracy: 0.1143 - val_loss: 2.3020 - val_accuracy: 0.1118\n", + "Epoch 59/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3004 - accuracy: 0.1143 - val_loss: 2.3020 - val_accuracy: 0.1118\n", + "Epoch 60/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.3004 - accuracy: 0.1143 - val_loss: 2.3019 - val_accuracy: 0.1118\n", + "Epoch 61/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3003 - accuracy: 0.1143 - val_loss: 2.3019 - val_accuracy: 0.1118\n", + "Epoch 62/100\n", + "55/55 [==============================] - 0s 4ms/step - loss: 2.3003 - accuracy: 0.1143 - val_loss: 2.3019 - val_accuracy: 0.1118\n", + "Epoch 63/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.3003 - accuracy: 0.1143 - val_loss: 2.3018 - val_accuracy: 0.1118\n", + "Epoch 64/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3002 - accuracy: 0.1143 - val_loss: 2.3018 - val_accuracy: 0.1118\n", + "Epoch 65/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3002 - accuracy: 0.1143 - val_loss: 2.3018 - val_accuracy: 0.1118\n", + "Epoch 66/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3002 - accuracy: 0.1143 - val_loss: 2.3017 - val_accuracy: 0.1118\n", + "Epoch 67/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3002 - accuracy: 0.1143 - val_loss: 2.3017 - val_accuracy: 0.1118\n", + "Epoch 68/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3001 - accuracy: 0.1143 - val_loss: 2.3016 - val_accuracy: 0.1118\n", + "Epoch 69/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3001 - accuracy: 0.1143 - val_loss: 2.3016 - val_accuracy: 0.1118\n", + "Epoch 70/100\n", + "55/55 [==============================] - 1s 9ms/step - loss: 2.3000 - accuracy: 0.1143 - val_loss: 2.3016 - val_accuracy: 0.1118\n", + "Epoch 71/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3000 - accuracy: 0.1143 - val_loss: 2.3016 - val_accuracy: 0.1118\n", + "Epoch 72/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.3000 - accuracy: 0.1143 - val_loss: 2.3015 - val_accuracy: 0.1118\n", + "Epoch 73/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.2999 - accuracy: 0.1143 - val_loss: 2.3015 - val_accuracy: 0.1118\n", + "Epoch 74/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.2999 - accuracy: 0.1143 - val_loss: 2.3015 - val_accuracy: 0.1118\n", + "Epoch 75/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.2999 - accuracy: 0.1143 - val_loss: 2.3014 - val_accuracy: 0.1118\n", + "Epoch 76/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.2998 - accuracy: 0.1143 - val_loss: 2.3014 - val_accuracy: 0.1118\n", + "Epoch 77/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2998 - accuracy: 0.1143 - val_loss: 2.3013 - val_accuracy: 0.1118\n", + "Epoch 78/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.2998 - accuracy: 0.1143 - val_loss: 2.3013 - val_accuracy: 0.1118\n", + "Epoch 79/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2998 - accuracy: 0.1143 - val_loss: 2.3012 - val_accuracy: 0.1118\n", + "Epoch 80/100\n", + "55/55 [==============================] - 0s 7ms/step - loss: 2.2997 - accuracy: 0.1143 - val_loss: 2.3012 - val_accuracy: 0.1118\n", + "Epoch 81/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2997 - accuracy: 0.1143 - val_loss: 2.3012 - val_accuracy: 0.1118\n", + "Epoch 82/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.2997 - accuracy: 0.1143 - val_loss: 2.3011 - val_accuracy: 0.1118\n", + "Epoch 83/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.2996 - accuracy: 0.1143 - val_loss: 2.3011 - val_accuracy: 0.1118\n", + "Epoch 84/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2996 - accuracy: 0.1143 - val_loss: 2.3011 - val_accuracy: 0.1118\n", + "Epoch 85/100\n", + "55/55 [==============================] - 0s 6ms/step - loss: 2.2996 - accuracy: 0.1143 - val_loss: 2.3011 - val_accuracy: 0.1118\n", + "Epoch 86/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2995 - accuracy: 0.1143 - val_loss: 2.3011 - val_accuracy: 0.1118\n", + "Epoch 87/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2995 - accuracy: 0.1143 - val_loss: 2.3010 - val_accuracy: 0.1118\n", + "Epoch 88/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2995 - accuracy: 0.1143 - val_loss: 2.3010 - val_accuracy: 0.1118\n", + "Epoch 89/100\n", + "55/55 [==============================] - 0s 4ms/step - loss: 2.2994 - accuracy: 0.1143 - val_loss: 2.3009 - val_accuracy: 0.1118\n", + "Epoch 90/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2994 - accuracy: 0.1143 - val_loss: 2.3009 - val_accuracy: 0.1118\n", + "Epoch 91/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2994 - accuracy: 0.1143 - val_loss: 2.3009 - val_accuracy: 0.1118\n", + "Epoch 92/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2993 - accuracy: 0.1143 - val_loss: 2.3008 - val_accuracy: 0.1118\n", + "Epoch 93/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2993 - accuracy: 0.1143 - val_loss: 2.3008 - val_accuracy: 0.1118\n", + "Epoch 94/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2993 - accuracy: 0.1143 - val_loss: 2.3008 - val_accuracy: 0.1118\n", + "Epoch 95/100\n", + "55/55 [==============================] - 0s 4ms/step - loss: 2.2992 - accuracy: 0.1143 - val_loss: 2.3007 - val_accuracy: 0.1118\n", + "Epoch 96/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2992 - accuracy: 0.1143 - val_loss: 2.3007 - val_accuracy: 0.1118\n", + "Epoch 97/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2992 - accuracy: 0.1143 - val_loss: 2.3007 - val_accuracy: 0.1118\n", + "Epoch 98/100\n", + "55/55 [==============================] - 0s 5ms/step - loss: 2.2991 - accuracy: 0.1143 - val_loss: 2.3006 - val_accuracy: 0.1118\n", + "Epoch 99/100\n", + "55/55 [==============================] - 0s 4ms/step - loss: 2.2991 - accuracy: 0.1143 - val_loss: 2.3006 - val_accuracy: 0.1118\n", + "Epoch 100/100\n", + "55/55 [==============================] - 0s 8ms/step - loss: 2.2991 - accuracy: 0.1143 - val_loss: 2.3006 - val_accuracy: 0.1118\n" + ] + } + ], + "source": [ + "history = model.fit(X_train, y_train, batch_size = 256, validation_split = 0.3, epochs = 100, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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JP9sfcfc6d6+rqakZ6uklTVGyjQ4rDRZSNfqxpUWApkEQKUTZBP0aYKaZzTCzYmApsDKbk5tZjZmNS70uA64CtpxoYyU7xcl2EpG00o13Ma44+CNMQS9SeIoG28HdO83sTuAFIAo87u4bzez21PblZnYasBaoApJmdjfBCJ0pwE9SI3ciwAp3/3mOrkVSYt6WFvSVAFQXBXPSa4ilSOEZNOgB3H0VsKrXuuVpr/cSlHR6Ww8sOJkGytAVJ9vpjKSVboBxqaBXj16k8OiTsSFU4m10RsuChVTQV6UeJ6ghliKFR0EfNskuYnTS2at0o6dMiRQuBX3YpB460rtHX06qR6+gFyk4CvqwSQV9V/T4Gn000cKYEk2DIFKIFPRhk3pebLI76GNB0Hd/aEo9epHCo6APm+4efdHxpRs6jgZBr4ePiBQcBX3YdPfoY72DvoWxZSrdiBQiBX3YpHr0RPsGfVVpTA8IFylACvqwSQW9x1I1+kgUisqC+W70OEGRgqSgD5tU6ca7a/TQM1VxlYJepCAp6EPGu4M+Vn5sZdqc9PFEFx2dyTy1TkTyQUEfMl3tQdBbLL1HXwmJVsaWxQA08kakwCjoQ6arI3Uztk+P/ihVZcEcdhpLL1JYFPQhk+xoAcCKM5duQPPdiBQaBX3IJDvidLkRjZUcW9l9M7ZUQS9SiBT0IeMdLcQpobgo7VdbXNkzvBI0VbFIocnqwSNy6vCOOG0UE4umB71KNyKFTD36kPFEK+0UU5Qh6Ku6e/QKepGCoqAPm444cS+hOGrH1hVXQmcbpRGnuCiioBcpMAr6sEm0Es9UugFIpOa70Th6kYKioA+bzjhxSjIHvWawFClICvqQsUScNu/dow+eG6v5bkQKk4I+ZCKpHn1xUXqN/tjDR8aWaapikUKjoA8Z62rrv0bf3qypikUKUFZBb2ZLzGyrmW03s2UZtp9jZq+aWbuZfTNt/TQze8nMNpvZRjP7+nA2XvqKdMaJezFFkbRfbeXk4PvRBt2MFSlAgwa9mUWBh4BrgNnAzWY2u9duB4G7gPt7re8E/sLdZwGLga9lOFaGUbSrjbbepZuqKcH3pg9TpZsEyaTnp4EiMuKy6dEvAra7+3vu3gE8BVyXvoO7N7j7GiDRa/0ed38z9boZ2AxMHZaWS0bRznjf0k1JVXBDtmkPVWVFJB2OdqhOL1Iosgn6qcCutOV6TiCszWw6sAB4fajHSpa6EkS8s++oGzMYM6WnRw/6dKxIIckm6C3DuiH93W9mlcAzwN3u3tTPPreZ2VozW9vY2DiU00u31PNi+4yjB6g6HZp2a74bkQKUTdDXA9PSlmuB3dm+gZnFCEL+CXf/aX/7ufsj7l7n7nU1NTXZnl7SpYK+jWKK+wT9VGje0zNVsYZYihSObIJ+DTDTzGaYWTGwFFiZzcnNzIAfAZvd/YETb6ZkJfW82LiXECvq9YdY1RRo3svY0uBXfqi1Y6RbJyJ5MmjQu3sncCfwAsHN1BXuvtHMbjez2wHM7DQzqwf+HPhbM6s3syrgIuCPgCvMbF3q69qcXU2h6ynd9BpeCUHpxrs4q6yFaMTYsidjBU1EQiir+ejdfRWwqte65Wmv9xKUdHp7hcw1fsmF42r0vXv0wf3zsngDH5s8hrd2HR7p1olInuiTsWHSGQR9IlJCUDVLM+bYWPrzpo3l7V2HcddYepFCoKAPk1SPvjNS2ndbqkdP8x7m146jqa2THQdaR7BxIpIvCvowSd2MTWQK+vIJEC2Gpg+ZP20cAG+rfCNSEBT0YdLdo4+W9d0WicCY06BpNzMnVVIWi7JOQS9SEBT0YZLq0Xu0JPP2qqnQtIeiaIRzp47l7XoFvUghUNCHSXePvihDjx56pkEAmD9tLBt3N9HRmRyp1olInijow6Qj6NFnvBkLwVj65j3gzvxp4+joTLJ1b/MINlBE8kFBHyaJVjqJEikqzry9aip0tkH8EPNrgxuy61S+EQk9BX2YJOJ0WAnFRf38WtPmpa+tLmNCRbFG3ogUAAV9mCRaaLfSvjNXduseS9+0BzNj/rRxCnqRAqCgD5NEnHbLMP1Bt6rTg+/dN2Rrx7G98SjNerSgSKgp6MMkEact01z03SonAxbckCUYeeMO/2f9Hjq7NPpGJKyymtRMThGJVtqspO9c9N2isSDsUz36BWdUM2lMCct++jv+4RdbuPzsGk4b28/QTBHJuYriKH925cxhP6+CPkw6Wol7CUX9lW4guCHbFDw3ZmxZjJe+eRm/3dbIrzY18Jt3Gmhq0wNJRPKlprJEQS+DSLQOXLqB4IbsgXd7FitKilgydwpL5k4ZgQaKSD6oRh8miTitmR4jmC717FgRKRwK+jBJxIl78cA9+jFToP0ItB8duXaJSF4p6MMk0UJLpufFpkubl15ECoOCPkwScVoG69F3j6U/sH1k2iQieaegD4tkEjrbaE3GBq7RT54TPITk6T+FN/8F9DhBkdDTqJuwSD0v9mhykOGV5ePhK7+FZ2+HlXfCthfgo1ePUCNFZECxMph307CfVkEfFqkpilu8mAkD9egBxk6FP3oOXv1f8Ou/g83/MQINFJFBVUxS0MsAUk+XamOQGn23SAQu+jqcf6tG4IiMFpabanpWQW9mS4DvA1HgMXf/Xq/t5wA/BhYC33L3+9O2PQ58Cmhw97nD1XDpJfV0qbgPMAVCJqVjgy8RCa1BE8HMosBDwDXAbOBmM5vda7eDwF3A/fT1z8CSk2umDCrRAkArA8xeKSIFKZuu3yJgu7u/5+4dwFPAdek7uHuDu68B+sx36+4vE/xDILnU3aOnhFh/Dx4RkYKUTSJMBXalLden1slokgr6tsHG0YtIwckmETLVAYZ98LWZ3WZma81sbWNj43CfPvxSN2PjKt2ISC/ZBH09MC1tuRYY9lmx3P0Rd69z97qamprhPn34pYZXtg42e6WIFJxsEmENMNPMZphZMbAUWJnbZsmQdffoVboRkV4GTQR37wTuBF4ANgMr3H2jmd1uZrcDmNlpZlYP/Dnwt2ZWb2ZVqW1PAq8CZ6fW/2muLqagddfoGeLwShEJvazG0bv7KmBVr3XL017vJSjpZDr25pNpoGSpZ9SNevQicjwlQlgkWkhGYnRSpJuxInIcBX1YJOIko6UAGkcvIsdRIoRFopWuaBkAsYh+rSJyjBIhLBJxOnt69CrdiMgxCvqw6Gils7tHr5uxIpJGiRAWiVY6I0GPXsMrRSSdEiEsEnE6IiUAlOhmrIikUSKERaKVI50xxpQUMbGyJN+tEZFRREEfFolW9rdHmTWlikhEN2NF5BgFfUh4Ik5DW4TZp1fluykiMsoo6EMi2d5Kc1eMWVPG5LspIjLKKOjDItFCnBJmT9HzX0XkeAr6MEh2EU120EYJMydX5rs1IjLKKOjDIDVzZUVlFaWxaJ4bIyKjjYI+DFJBXz1WZRsR6UtBHwKHDh8CoKZ6XJ5bIiKjkYI+BN7fsx+AyTXj89wSERmNFPQhsHNfEPRTaybkuSUiMhop6EOgvuEAAGMqNYZeRPpS0IfA3v0HgxfFFfltiIiMSgr6U1xboovDR5qChVhZfhsjIqOSgv4Ut23fUUq8LVhQ0ItIBkX5bkAhWfW7PWzbd3RYztXR1cWh1gRb9zZzjnUEK2Plw3JuEQkXBf0IeXHTPr76xJvDdr5oxKguj1FdXkzd6SXQiIJeRDLKKujNbAnwfSAKPObu3+u1/Rzgx8BC4Fvufn+2xxaCI/EE33r2d5xz2hie/dpFw/KoPzMwS807/5vX4SVUuhGRjAYNejOLAg8BVwP1wBozW+num9J2OwjcBVx/AseG3j+s2kxjczuPfqEuN3PRJFohEoNobPjPLSKnvGy6louA7e7+nrt3AE8B16Xv4O4N7r4GSAz12LB7Zdt+nlqzi9su+QjzanM0RUEirrKNiPQrm6CfCuxKW65PrcvGyRx7yuvsSrLsp+s5a2IFd181M3dv1NECxQp6Ecksm6DP9ABSz/L8WR9rZreZ2VozW9vY2Jjl6Ue3nQdbqT8U5/ZLP5Lb6YMTcdXnRaRf2QR9PTAtbbkW2J3l+bM+1t0fcfc6d6+rqanJ8vSj2wcHWgE4qybHn1hV6UZEBpBN0K8BZprZDDMrBpYCK7M8/8kce8rbeaAFgDMm5DiEEy3q0YtIvwYddePunWZ2J/ACwRDJx919o5ndntq+3MxOA9YCVUDSzO4GZrt7U6Zjc3Uxo83Og62UF0epqSzJ7RupRy8iA8hqHL27rwJW9Vq3PO31XoKyTFbHFoqdB1o5Y3z5sfHuuZJohTLNRS8imWmumxzaeaCFM3NdtgHdjBWRASnoc6Qr6ew6GGf6hBGYOrijVaUbEemXgj5H9ja10dGVzP2NWAhKNxpHLyL9UNDnSPeImzPHj0CPXqUbERmAgj5HusfQ57xGn+yCrnaVbkSkXwr6HNl5sJVY1JgytjS3b5QI/kFRj15E+qOgz5GdB1qorS6naBimJB5QIh58V49eRPqhoM+R7jH0OdfTo1fQi0hmCvoTdeRDWPEFiB/us8nd+eBA68iNoQeVbkSkXwr6E7Xhadj0HHzwap9Nh1oTNLd3cuZIjaEH9ehFpF8K+hP17kvB9/3v9Nm0o2do5QiWbjSOXkT6oaA/EYm2Yz35DEE/YkMrQTdjRWRQCvoTses16GyDaAns39Zn885U0E8b0ZuxqtGLSGYK+hPx3mqIFMHs66BxK/jxD83aebCFKWNLc/tUqW4KehEZhIL+RLy3GmovgNMXQNthaD1w3OYRG1oJaUE/Ajd+ReSUpKAfqtaDsHsdnHU5TPxYsK5XnX7nSA2tTCahMfXe6tGLSD+yevCIpHn/ZcDhrMtgzGkAHP5gI9f+W5yqshhn1VSw/2h75qGVySR4cpjasRpevA/2rofpF0Nx5fCcV0RCR0E/VO+thuIxMPV8sAgUlbJ/x+/YfeR0zphQzqbdTZTFopx/ZnWw/1v/Cjv+L+zbENTzu9qHry3jzoDPPALn/neI6I8zEclMQT9U770EMy6GaOpHN2EmdmA70cgSfvLFRZQURXH34PGBRz6E574G5RNhynyYcQmUjhuedlRNCQK+KMfPoxWRU164gv691cG0vbkSPwSHdsDirx5bN3EmY7a+xoyJFZQUBaNsep4R27g5+H7TT2D6x3PXLhGRAYQr6J+8+dgolJwx+OhVxxYnfowJG59ldk1x310btwbfa2bluE0iIv0LV9B/YeXw3ezsT9k4mPCRnsWOcR+hmCQXVB3qu2/D5qBsUzEht20SERlAuIJ+2gUj/pYfRGv5KDCneF/fjY1boeacEW+TiEg6DdU4SRviEwE40z88foN7KujPzkOrRESOySrozWyJmW01s+1mtizDdjOzH6S2rzezhWnbvm5mG8xso5ndPZyNHw02H+ziQ59IdeuO4zc074X2IzBJ9XkRya9Bg97MosBDwDXAbOBmM5vda7drgJmpr9uAH6aOnQt8GVgEzAc+ZWYzh631o8A7e5vZG5tG5ECvyc26R9yoRy8ieZZNj34RsN3d33P3DuAp4Lpe+1wH/G8PvAaMM7MpwCzgNXdvdfdO4DfAZ4ax/Xn3zr6jNFeeFcximT65Wc+IG9XoRSS/sgn6qcCutOX61Lps9tkAXGJmE8ysHLgWmJbpTczsNjNba2ZrGxsbs21/Xh1t7+TDw3GomQmJFmjafWxj4xYoq4aKmvw1UESE7ILeMqzzbPZx983A/wR+BTwPvA10ZnoTd3/E3evcva6m5tQIx237mgGoOD1Vh9+34djGxq3B+HnL9KMRERk52QR9Pcf3wmuB3dnu4+4/cveF7n4JcBDo+6SOU9Q7qaCfNPtiKBkLG54JNrgHY+hVnxeRUSCboF8DzDSzGWZWDCwFVvbaZyXwhdTom8XAEXffA2Bmk1LfzwBuAJ4cttbn2Tv7jlIaizCtZjyc+1nYtBLamuBoQzBPverzIjIKDPqBKXfvNLM7gReAKPC4u280s9tT25cDqwjq79uBVuBP0k7xjJlNABLA19w9w0dIT03v7Gtm5qQxRCIG530O1j4Om54LZpUE9ehFZFTI6pOx7r6KIMzT1y1Pe+3A1/o59uKTaeBo9s6+ZseZAKsAAATgSURBVC76aPCBKaaeDxNmwrp/gzmpgUUaQy8io4A+GXuCjrQm2NfUztmTxwQrzOC8W+CD/wdbV0HpWKicnN9GiogQsrluVqzZRWey94CgkxeNwLjyYqrLi4kYrNt1mFe27wfgY91BDzB/Kfzn3wVz1k+7UCNuRGRUCFXQ37tyI/FEDuejT1NbXcaN59dy4Vnjj62sOj14luy7v1Z9XkRGjVAF/W/+8rI+A/yHQ6IryeHWBIdaO+joTDJ36lgmV5Vm3vm8W1JBr/q8iIwOoQr6Sf2F7zCorc5yx1l/AL//ZzDn+py1RURkKEIV9KNCUQl84n/kuxUiIj006kZEJOQU9CIiIaegFxEJOQW9iEjIKehFREJOQS8iEnIKehGRkFPQi4iEnLnnYtKAk2NmjcDOEzx8IrB/GJtzKijEa4bCvO5CvGYozOse6jWf6e4Zn8M6KoP+ZJjZWnevy3c7RlIhXjMU5nUX4jVDYV73cF6zSjciIiGnoBcRCbkwBv0j+W5AHhTiNUNhXnchXjMU5nUP2zWHrkYvIiLHC2OPXkRE0oQm6M1siZltNbPtZrYs3+3JFTObZmYvmdlmM9toZl9PrR9vZr8ys22p79k+KuWUYWZRM3vLzH6eWi6Eax5nZk+b2ZbU7/z3wn7dZvaN1H/bG8zsSTMrDeM1m9njZtZgZhvS1vV7nWZ2TyrftprZfxvKe4Ui6M0sCjwEXAPMBm42s9n5bVXOdAJ/4e6zgMXA11LXugz4tbvPBH6dWg6brwOb05YL4Zq/Dzzv7ucA8wmuP7TXbWZTgbuAOnefC0SBpYTzmv8ZWNJrXcbrTP0/vhSYkzrm4VTuZSUUQQ8sAra7+3vu3gE8BVyX5zblhLvvcfc3U6+bCf7Hn0pwvT9J7fYTIFTPMjSzWuCTwGNpq8N+zVXAJcCPANy9w90PE/LrJnjyXZmZFQHlwG5CeM3u/jJwsNfq/q7zOuApd2939/eB7QS5l5WwBP1UYFfacn1qXaiZ2XRgAfA6MNnd90DwjwEwKX8ty4kHgb8Ckmnrwn7NZwGNwI9TJavHzKyCEF+3u38I3A98AOwBjrj7LwnxNffS33WeVMaFJegtw7pQDycys0rgGeBud2/Kd3tyycw+BTS4+xv5bssIKwIWAj909wVAC+EoWfQrVZO+DpgBnA5UmNnn89uqUeGkMi4sQV8PTEtbriX4cy+UzCxGEPJPuPtPU6v3mdmU1PYpQEO+2pcDFwGfNrMdBGW5K8zsXwn3NUPw33W9u7+eWn6aIPjDfN1XAe+7e6O7J4CfAr9PuK85XX/XeVIZF5agXwPMNLMZZlZMcNNiZZ7blBNmZgQ1283u/kDappXAH6de/zHw3Ei3LVfc/R53r3X36QS/2/90988T4msGcPe9wC4zOzu16kpgE+G+7g+AxWZWnvpv/UqC+1BhvuZ0/V3nSmCpmZWY2QxgJvBfWZ/V3UPxBVwLvAO8C3wr3+3J4XV+nOBPtvXAutTXtcAEgrv021Lfx+e7rTm6/suAn6deh/6agfOAtanf97NAddivG7gP2AJsAP4FKAnjNQNPEtyHSBD02P90oOsEvpXKt63ANUN5L30yVkQk5MJSuhERkX4o6EVEQk5BLyIScgp6EZGQU9CLiIScgl5EJOQU9CIiIaegFxEJuf8P9vanocF+h+AAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.legend(['training', 'validation'], loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training and validation accuracy seems to improve after around 60 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 0s 1ms/step - loss: 2.2997 - accuracy: 0.1135\n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.11349999904632568\n" + ] + } + ], + "source": [ + "print('Test accuracy: ', results[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=model.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Weight Initialization\n", + "- Changing weight initialization scheme can significantly improve training of the model by preventing vanishing gradient problem up to some degree\n", + "- He normal or Xavier normal initialization schemes are SOTA at the moment\n", + "- Doc: https://keras.io/initializers/" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# from now on, create a function to generate (return) models\n", + "def mlp_model():\n", + " model = Sequential()\n", + " \n", + " model.add(Dense(50, input_shape = (784, ), kernel_initializer='he_normal')) # use he_normal initializer\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50, kernel_initializer='he_normal')) # use he_normal initializer\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50, kernel_initializer='he_normal')) # use he_normal initializer\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50, kernel_initializer='he_normal')) # use he_normal initializer\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(10, kernel_initializer='he_normal')) # use he_normal initializer\n", + " model.add(Activation('softmax'))\n", + " \n", + " sgd = optimizers.SGD(lr = 0.001)\n", + " model.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.4546 - accuracy: 0.1143 - val_loss: 2.3962 - val_accuracy: 0.1118\n", + "Epoch 2/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.3637 - accuracy: 0.1143 - val_loss: 2.3358 - val_accuracy: 0.1120\n", + "Epoch 3/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.3219 - accuracy: 0.1143 - val_loss: 2.3100 - val_accuracy: 0.1121\n", + "Epoch 4/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.3047 - accuracy: 0.1144 - val_loss: 2.3007 - val_accuracy: 0.1120\n", + "Epoch 5/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2986 - accuracy: 0.1143 - val_loss: 2.2978 - val_accuracy: 0.1120\n", + "Epoch 6/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2963 - accuracy: 0.1143 - val_loss: 2.2965 - val_accuracy: 0.1118\n", + "Epoch 7/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2950 - accuracy: 0.1143 - val_loss: 2.2958 - val_accuracy: 0.1118\n", + "Epoch 8/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2940 - accuracy: 0.1143 - val_loss: 2.2948 - val_accuracy: 0.1118\n", + "Epoch 9/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2931 - accuracy: 0.1143 - val_loss: 2.2938 - val_accuracy: 0.1118\n", + "Epoch 10/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2922 - accuracy: 0.1143 - val_loss: 2.2929 - val_accuracy: 0.1120\n", + "Epoch 11/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2912 - accuracy: 0.1143 - val_loss: 2.2920 - val_accuracy: 0.1120\n", + "Epoch 12/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2903 - accuracy: 0.1143 - val_loss: 2.2911 - val_accuracy: 0.1120\n", + "Epoch 13/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2893 - accuracy: 0.1143 - val_loss: 2.2901 - val_accuracy: 0.1118\n", + "Epoch 14/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2883 - accuracy: 0.1143 - val_loss: 2.2891 - val_accuracy: 0.1118\n", + "Epoch 15/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2874 - accuracy: 0.1143 - val_loss: 2.2883 - val_accuracy: 0.1118\n", + "Epoch 16/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2864 - accuracy: 0.1144 - val_loss: 2.2874 - val_accuracy: 0.1118\n", + "Epoch 17/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2855 - accuracy: 0.1143 - val_loss: 2.2866 - val_accuracy: 0.1118\n", + "Epoch 18/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2845 - accuracy: 0.1143 - val_loss: 2.2856 - val_accuracy: 0.1120\n", + "Epoch 19/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2836 - accuracy: 0.1144 - val_loss: 2.2847 - val_accuracy: 0.1120\n", + "Epoch 20/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2826 - accuracy: 0.1144 - val_loss: 2.2837 - val_accuracy: 0.1121\n", + "Epoch 21/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2817 - accuracy: 0.1144 - val_loss: 2.2826 - val_accuracy: 0.1130\n", + "Epoch 22/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2808 - accuracy: 0.1146 - val_loss: 2.2818 - val_accuracy: 0.1135\n", + "Epoch 23/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2799 - accuracy: 0.1149 - val_loss: 2.2808 - val_accuracy: 0.1136\n", + "Epoch 24/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2789 - accuracy: 0.1152 - val_loss: 2.2797 - val_accuracy: 0.1135\n", + "Epoch 25/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2779 - accuracy: 0.1178 - val_loss: 2.2787 - val_accuracy: 0.1125\n", + "Epoch 26/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2768 - accuracy: 0.1160 - val_loss: 2.2777 - val_accuracy: 0.1125\n", + "Epoch 27/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2758 - accuracy: 0.1151 - val_loss: 2.2767 - val_accuracy: 0.1145\n", + "Epoch 28/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2747 - accuracy: 0.1167 - val_loss: 2.2757 - val_accuracy: 0.1148\n", + "Epoch 29/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2737 - accuracy: 0.1215 - val_loss: 2.2746 - val_accuracy: 0.1131\n", + "Epoch 30/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2726 - accuracy: 0.1161 - val_loss: 2.2736 - val_accuracy: 0.1150\n", + "Epoch 31/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2714 - accuracy: 0.1240 - val_loss: 2.2725 - val_accuracy: 0.1138\n", + "Epoch 32/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2703 - accuracy: 0.1159 - val_loss: 2.2713 - val_accuracy: 0.1207\n", + "Epoch 33/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2691 - accuracy: 0.1315 - val_loss: 2.2703 - val_accuracy: 0.1180\n", + "Epoch 34/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2680 - accuracy: 0.1209 - val_loss: 2.2690 - val_accuracy: 0.1209\n", + "Epoch 35/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2668 - accuracy: 0.1272 - val_loss: 2.2678 - val_accuracy: 0.1210\n", + "Epoch 36/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2656 - accuracy: 0.1330 - val_loss: 2.2668 - val_accuracy: 0.1226\n", + "Epoch 37/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.2644 - accuracy: 0.1296 - val_loss: 2.2656 - val_accuracy: 0.1278\n", + "Epoch 38/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2631 - accuracy: 0.1332 - val_loss: 2.2642 - val_accuracy: 0.1342\n", + "Epoch 39/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2619 - accuracy: 0.1430 - val_loss: 2.2628 - val_accuracy: 0.1382\n", + "Epoch 40/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2605 - accuracy: 0.1449 - val_loss: 2.2614 - val_accuracy: 0.1429\n", + "Epoch 41/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2592 - accuracy: 0.1558 - val_loss: 2.2601 - val_accuracy: 0.1448\n", + "Epoch 42/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2578 - accuracy: 0.1542 - val_loss: 2.2587 - val_accuracy: 0.1468\n", + "Epoch 43/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2564 - accuracy: 0.1538 - val_loss: 2.2572 - val_accuracy: 0.1589\n", + "Epoch 44/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2549 - accuracy: 0.1644 - val_loss: 2.2557 - val_accuracy: 0.1668\n", + "Epoch 45/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2533 - accuracy: 0.1689 - val_loss: 2.2541 - val_accuracy: 0.1727\n", + "Epoch 46/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2518 - accuracy: 0.1753 - val_loss: 2.2526 - val_accuracy: 0.1721\n", + "Epoch 47/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2501 - accuracy: 0.1862 - val_loss: 2.2511 - val_accuracy: 0.1717\n", + "Epoch 48/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2484 - accuracy: 0.1790 - val_loss: 2.2494 - val_accuracy: 0.1859\n", + "Epoch 49/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2467 - accuracy: 0.1899 - val_loss: 2.2476 - val_accuracy: 0.1934\n", + "Epoch 50/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2450 - accuracy: 0.1942 - val_loss: 2.2458 - val_accuracy: 0.1976\n", + "Epoch 51/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2432 - accuracy: 0.2026 - val_loss: 2.2440 - val_accuracy: 0.1966\n", + "Epoch 52/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2413 - accuracy: 0.1998 - val_loss: 2.2422 - val_accuracy: 0.1992\n", + "Epoch 53/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2394 - accuracy: 0.1994 - val_loss: 2.2401 - val_accuracy: 0.2040\n", + "Epoch 54/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2373 - accuracy: 0.2115 - val_loss: 2.2381 - val_accuracy: 0.2047\n", + "Epoch 55/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2351 - accuracy: 0.2041 - val_loss: 2.2359 - val_accuracy: 0.2116\n", + "Epoch 56/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2329 - accuracy: 0.2244 - val_loss: 2.2337 - val_accuracy: 0.2071\n", + "Epoch 57/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2307 - accuracy: 0.2152 - val_loss: 2.2315 - val_accuracy: 0.2167\n", + "Epoch 58/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2285 - accuracy: 0.2207 - val_loss: 2.2291 - val_accuracy: 0.2268\n", + "Epoch 59/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2260 - accuracy: 0.2294 - val_loss: 2.2267 - val_accuracy: 0.2290\n", + "Epoch 60/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2235 - accuracy: 0.2453 - val_loss: 2.2241 - val_accuracy: 0.2274\n", + "Epoch 61/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2208 - accuracy: 0.2360 - val_loss: 2.2215 - val_accuracy: 0.2256\n", + "Epoch 62/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2182 - accuracy: 0.2294 - val_loss: 2.2188 - val_accuracy: 0.2438\n", + "Epoch 63/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2154 - accuracy: 0.2385 - val_loss: 2.2159 - val_accuracy: 0.2500\n", + "Epoch 64/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2125 - accuracy: 0.2536 - val_loss: 2.2130 - val_accuracy: 0.2451\n", + "Epoch 65/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2094 - accuracy: 0.2488 - val_loss: 2.2100 - val_accuracy: 0.2549\n", + "Epoch 66/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.2063 - accuracy: 0.2660 - val_loss: 2.2069 - val_accuracy: 0.2461\n", + "Epoch 67/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.2030 - accuracy: 0.2535 - val_loss: 2.2035 - val_accuracy: 0.2581\n", + "Epoch 68/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.1996 - accuracy: 0.2670 - val_loss: 2.2001 - val_accuracy: 0.2643\n", + "Epoch 69/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.1962 - accuracy: 0.2883 - val_loss: 2.1966 - val_accuracy: 0.2512\n", + "Epoch 70/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.1926 - accuracy: 0.2644 - val_loss: 2.1930 - val_accuracy: 0.2559\n", + "Epoch 71/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.1888 - accuracy: 0.2683 - val_loss: 2.1893 - val_accuracy: 0.2663\n", + "Epoch 72/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.1849 - accuracy: 0.2799 - val_loss: 2.1855 - val_accuracy: 0.2625\n", + "Epoch 73/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.1808 - accuracy: 0.2807 - val_loss: 2.1813 - val_accuracy: 0.2722\n", + "Epoch 74/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1766 - accuracy: 0.2770 - val_loss: 2.1771 - val_accuracy: 0.2852\n", + "Epoch 75/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1723 - accuracy: 0.2988 - val_loss: 2.1728 - val_accuracy: 0.2891\n", + "Epoch 76/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1678 - accuracy: 0.3036 - val_loss: 2.1684 - val_accuracy: 0.2825\n", + "Epoch 77/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1632 - accuracy: 0.2910 - val_loss: 2.1638 - val_accuracy: 0.2911\n", + "Epoch 78/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1584 - accuracy: 0.3032 - val_loss: 2.1590 - val_accuracy: 0.3000\n", + "Epoch 79/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1534 - accuracy: 0.3144 - val_loss: 2.1540 - val_accuracy: 0.3034\n", + "Epoch 80/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.1482 - accuracy: 0.3113 - val_loss: 2.1488 - val_accuracy: 0.3074\n", + "Epoch 81/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1428 - accuracy: 0.3128 - val_loss: 2.1433 - val_accuracy: 0.3224\n", + "Epoch 82/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 2.1371 - accuracy: 0.3268 - val_loss: 2.1377 - val_accuracy: 0.3163\n", + "Epoch 83/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.1313 - accuracy: 0.3369 - val_loss: 2.1318 - val_accuracy: 0.3051\n", + "Epoch 84/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.1253 - accuracy: 0.3108 - val_loss: 2.1258 - val_accuracy: 0.3221\n", + "Epoch 85/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.1190 - accuracy: 0.3326 - val_loss: 2.1196 - val_accuracy: 0.3274\n", + "Epoch 86/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.1126 - accuracy: 0.3279 - val_loss: 2.1131 - val_accuracy: 0.3428\n", + "Epoch 87/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.1060 - accuracy: 0.3549 - val_loss: 2.1064 - val_accuracy: 0.3332\n", + "Epoch 88/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.0990 - accuracy: 0.3566 - val_loss: 2.0996 - val_accuracy: 0.3320\n", + "Epoch 89/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 2.0918 - accuracy: 0.3411 - val_loss: 2.0923 - val_accuracy: 0.3348\n", + "Epoch 90/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.0844 - accuracy: 0.3338 - val_loss: 2.0851 - val_accuracy: 0.3551\n", + "Epoch 91/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.0768 - accuracy: 0.3552 - val_loss: 2.0776 - val_accuracy: 0.3598\n", + "Epoch 92/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.0690 - accuracy: 0.3706 - val_loss: 2.0699 - val_accuracy: 0.3596\n", + "Epoch 93/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.0610 - accuracy: 0.3595 - val_loss: 2.0619 - val_accuracy: 0.3680\n", + "Epoch 94/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.0528 - accuracy: 0.3697 - val_loss: 2.0537 - val_accuracy: 0.3707\n", + "Epoch 95/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.0444 - accuracy: 0.3757 - val_loss: 2.0452 - val_accuracy: 0.3697\n", + "Epoch 96/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.0357 - accuracy: 0.3799 - val_loss: 2.0367 - val_accuracy: 0.3731\n", + "Epoch 97/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 2.0268 - accuracy: 0.3824 - val_loss: 2.0279 - val_accuracy: 0.3715\n", + "Epoch 98/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.0176 - accuracy: 0.3743 - val_loss: 2.0187 - val_accuracy: 0.3764\n", + "Epoch 99/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.0082 - accuracy: 0.3894 - val_loss: 2.0093 - val_accuracy: 0.3778\n", + "Epoch 100/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.9987 - accuracy: 0.3851 - val_loss: 2.0000 - val_accuracy: 0.3830\n" + ] + } + ], + "source": [ + "model = mlp_model()\n", + "history = model.fit(X_train, y_train, validation_split = 0.3, epochs = 100, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.legend(['training', 'validation'], loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training and validation accuracy seems to improve after around 60 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 0s 1ms/step - loss: 1.9991 - accuracy: 0.3827\n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.38269999623298645\n" + ] + } + ], + "source": [ + "print('Test accuracy: ', results[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[6.18367347e-01 2.16326531e-01 3.06122449e-02 1.02040816e-03\n", + " 2.85714286e-02 0.00000000e+00 8.26530612e-02 2.24489796e-02\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [0.00000000e+00 9.93832599e-01 1.76211454e-03 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 1.76211454e-03 2.64317181e-03\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [1.35658915e-02 3.70155039e-01 3.65310078e-01 1.93798450e-03\n", + " 1.35658915e-02 0.00000000e+00 1.60852713e-01 7.07364341e-02\n", + " 2.90697674e-03 9.68992248e-04]\n", + " [1.68316832e-02 3.22772277e-01 1.05940594e-01 3.50495050e-01\n", + " 4.95049505e-03 0.00000000e+00 4.95049505e-02 1.49504950e-01\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [9.16496945e-03 3.66598778e-02 2.54582485e-02 0.00000000e+00\n", + " 6.21181263e-02 0.00000000e+00 2.03665988e-03 8.57433809e-01\n", + " 0.00000000e+00 7.12830957e-03]\n", + " [2.46636771e-01 3.01569507e-01 5.82959641e-02 5.94170404e-02\n", + " 3.92376682e-02 0.00000000e+00 1.24439462e-01 1.70403587e-01\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [1.67014614e-02 6.51356994e-01 3.13152401e-02 0.00000000e+00\n", + " 6.26304802e-03 0.00000000e+00 2.86012526e-01 8.35073069e-03\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [1.94552529e-03 3.30739300e-02 9.72762646e-03 9.72762646e-04\n", + " 9.72762646e-04 0.00000000e+00 0.00000000e+00 9.52334630e-01\n", + " 9.72762646e-04 0.00000000e+00]\n", + " [2.66940452e-02 2.31006160e-01 3.09034908e-01 3.18275154e-02\n", + " 1.43737166e-02 0.00000000e+00 6.36550308e-02 2.73100616e-01\n", + " 4.82546201e-02 2.05338809e-03]\n", + " [1.18929633e-02 1.18929633e-02 9.91080278e-03 0.00000000e+00\n", + " 1.38751239e-02 0.00000000e+00 1.98216056e-03 9.49454906e-01\n", + " 0.00000000e+00 9.91080278e-04]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=model.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Nonlinearity (Activation function)\n", + "- Sigmoid functions suffer from gradient vanishing problem, making training slower\n", + "- There are many choices apart from sigmoid and tanh; try many of them!\n", + " - **'relu'** (rectified linear unit) is one of the most popular ones\n", + " - **'selu'** (scaled exponential linear unit) is one of the most recent ones\n", + "- Doc: https://keras.io/activations/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
**Sigmoid Activation Function**
\n", + "\n", + "
**Relu Activation Function**
" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def mlp_model():\n", + " model = Sequential()\n", + " \n", + " model.add(Dense(50, input_shape = (784, )))\n", + " model.add(Activation('relu')) # use relu\n", + " model.add(Dense(50))\n", + " model.add(Activation('relu')) # use relu\n", + " model.add(Dense(50))\n", + " model.add(Activation('relu')) # use relu\n", + " model.add(Dense(50))\n", + " model.add(Activation('relu')) # use relu\n", + " model.add(Dense(10))\n", + " model.add(Activation('softmax'))\n", + " \n", + " sgd = optimizers.SGD(lr = 0.001)\n", + " model.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.0110 - accuracy: 0.5953 - val_loss: 0.8677 - val_accuracy: 0.7369\n", + "Epoch 2/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.6794 - accuracy: 0.7986 - val_loss: 0.6026 - val_accuracy: 0.8170\n", + "Epoch 3/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.5043 - accuracy: 0.8493 - val_loss: 0.5074 - val_accuracy: 0.8411\n", + "Epoch 4/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.4196 - accuracy: 0.8719 - val_loss: 0.6869 - val_accuracy: 0.8013\n", + "Epoch 5/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.3644 - accuracy: 0.8877 - val_loss: 0.4076 - val_accuracy: 0.8768\n", + "Epoch 6/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.3219 - accuracy: 0.9009 - val_loss: 0.4033 - val_accuracy: 0.8800\n", + "Epoch 7/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2931 - accuracy: 0.9086 - val_loss: 0.3891 - val_accuracy: 0.8857\n", + "Epoch 8/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2733 - accuracy: 0.9152 - val_loss: 0.3820 - val_accuracy: 0.8870\n", + "Epoch 9/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2536 - accuracy: 0.9214 - val_loss: 0.3655 - val_accuracy: 0.8921\n", + "Epoch 10/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2372 - accuracy: 0.9249 - val_loss: 0.3433 - val_accuracy: 0.8987\n", + "Epoch 11/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2208 - accuracy: 0.9320 - val_loss: 0.3384 - val_accuracy: 0.9040\n", + "Epoch 12/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2102 - accuracy: 0.9341 - val_loss: 0.3393 - val_accuracy: 0.9027\n", + "Epoch 13/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1970 - accuracy: 0.9373 - val_loss: 0.3228 - val_accuracy: 0.9101\n", + "Epoch 14/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1856 - accuracy: 0.9400 - val_loss: 0.3321 - val_accuracy: 0.9086\n", + "Epoch 15/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1788 - accuracy: 0.9457 - val_loss: 0.3561 - val_accuracy: 0.9034\n", + "Epoch 16/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.1686 - accuracy: 0.9476 - val_loss: 0.3213 - val_accuracy: 0.9077\n", + "Epoch 17/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.1600 - accuracy: 0.9494 - val_loss: 0.3330 - val_accuracy: 0.9091\n", + "Epoch 18/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1555 - accuracy: 0.9504 - val_loss: 0.3161 - val_accuracy: 0.9150\n", + "Epoch 19/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1451 - accuracy: 0.9546 - val_loss: 0.3111 - val_accuracy: 0.9162\n", + "Epoch 20/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1401 - accuracy: 0.9568 - val_loss: 0.4167 - val_accuracy: 0.8919\n", + "Epoch 21/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1372 - accuracy: 0.9575 - val_loss: 0.3049 - val_accuracy: 0.9172\n", + "Epoch 22/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1272 - accuracy: 0.9590 - val_loss: 0.5210 - val_accuracy: 0.8645\n", + "Epoch 23/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1261 - accuracy: 0.9590 - val_loss: 0.3753 - val_accuracy: 0.9025\n", + "Epoch 24/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1202 - accuracy: 0.9628 - val_loss: 0.3271 - val_accuracy: 0.9130\n", + "Epoch 25/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1134 - accuracy: 0.9635 - val_loss: 0.3211 - val_accuracy: 0.9162\n", + "Epoch 26/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1088 - accuracy: 0.9675 - val_loss: 0.4257 - val_accuracy: 0.8901\n", + "Epoch 27/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1053 - accuracy: 0.9662 - val_loss: 0.3142 - val_accuracy: 0.9199\n", + "Epoch 28/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.1013 - accuracy: 0.9703 - val_loss: 0.3477 - val_accuracy: 0.9096\n", + "Epoch 29/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0976 - accuracy: 0.9696 - val_loss: 0.3136 - val_accuracy: 0.9180\n", + "Epoch 30/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0939 - accuracy: 0.9703 - val_loss: 0.3286 - val_accuracy: 0.9178\n", + "Epoch 31/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0899 - accuracy: 0.9714 - val_loss: 1.5631 - val_accuracy: 0.7330\n", + "Epoch 32/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0964 - accuracy: 0.9716 - val_loss: 0.3254 - val_accuracy: 0.9158\n", + "Epoch 33/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0845 - accuracy: 0.9727 - val_loss: 0.3224 - val_accuracy: 0.9200\n", + "Epoch 34/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0802 - accuracy: 0.9758 - val_loss: 0.4094 - val_accuracy: 0.9093\n", + "Epoch 35/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0794 - accuracy: 0.9752 - val_loss: 0.3260 - val_accuracy: 0.9205\n", + "Epoch 36/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0758 - accuracy: 0.9775 - val_loss: 0.3405 - val_accuracy: 0.9190\n", + "Epoch 37/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0729 - accuracy: 0.9775 - val_loss: 0.3568 - val_accuracy: 0.9135\n", + "Epoch 38/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0706 - accuracy: 0.9792 - val_loss: 0.3372 - val_accuracy: 0.9190\n", + "Epoch 39/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0674 - accuracy: 0.9802 - val_loss: 0.3313 - val_accuracy: 0.9217\n", + "Epoch 40/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9801 - val_loss: 0.3709 - val_accuracy: 0.9152\n", + "Epoch 41/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0629 - accuracy: 0.9802 - val_loss: 0.4039 - val_accuracy: 0.9089\n", + "Epoch 42/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0618 - accuracy: 0.9817 - val_loss: 0.3331 - val_accuracy: 0.9224\n", + "Epoch 43/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0587 - accuracy: 0.9830 - val_loss: 0.3455 - val_accuracy: 0.9202\n", + "Epoch 44/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0572 - accuracy: 0.9841 - val_loss: 0.3485 - val_accuracy: 0.9217\n", + "Epoch 45/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0540 - accuracy: 0.9842 - val_loss: 0.3471 - val_accuracy: 0.9221\n", + "Epoch 46/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0528 - accuracy: 0.9845 - val_loss: 0.4474 - val_accuracy: 0.8985\n", + "Epoch 47/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9854 - val_loss: 0.3460 - val_accuracy: 0.9232\n", + "Epoch 48/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0477 - accuracy: 0.9875 - val_loss: 0.5837 - val_accuracy: 0.8838\n", + "Epoch 49/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0503 - accuracy: 0.9846 - val_loss: 0.3594 - val_accuracy: 0.9184\n", + "Epoch 50/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9882 - val_loss: 0.3544 - val_accuracy: 0.9197\n", + "Epoch 51/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0428 - accuracy: 0.9889 - val_loss: 0.3541 - val_accuracy: 0.9219\n", + "Epoch 52/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0419 - accuracy: 0.9882 - val_loss: 0.3639 - val_accuracy: 0.9197\n", + "Epoch 53/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0389 - accuracy: 0.9900 - val_loss: 0.4586 - val_accuracy: 0.9025\n", + "Epoch 54/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0383 - accuracy: 0.9915 - val_loss: 0.3587 - val_accuracy: 0.9237\n", + "Epoch 55/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0361 - accuracy: 0.9900 - val_loss: 0.4175 - val_accuracy: 0.9172\n", + "Epoch 56/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0360 - accuracy: 0.9911 - val_loss: 0.3711 - val_accuracy: 0.9217\n", + "Epoch 57/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0342 - accuracy: 0.9912 - val_loss: 0.3754 - val_accuracy: 0.9210\n", + "Epoch 58/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0315 - accuracy: 0.9926 - val_loss: 0.3802 - val_accuracy: 0.9200\n", + "Epoch 59/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0312 - accuracy: 0.9924 - val_loss: 0.3789 - val_accuracy: 0.9207\n", + "Epoch 60/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0298 - accuracy: 0.9929 - val_loss: 0.3850 - val_accuracy: 0.9192\n", + "Epoch 61/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0289 - accuracy: 0.9932 - val_loss: 0.3800 - val_accuracy: 0.9212\n", + "Epoch 62/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0279 - accuracy: 0.9936 - val_loss: 0.3873 - val_accuracy: 0.9210\n", + "Epoch 63/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0269 - accuracy: 0.9942 - val_loss: 0.4172 - val_accuracy: 0.9173\n", + "Epoch 64/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0261 - accuracy: 0.9942 - val_loss: 0.3952 - val_accuracy: 0.9209\n", + "Epoch 65/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0245 - accuracy: 0.9949 - val_loss: 0.3964 - val_accuracy: 0.9221\n", + "Epoch 66/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0234 - accuracy: 0.9953 - val_loss: 0.3914 - val_accuracy: 0.9231\n", + "Epoch 67/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0228 - accuracy: 0.9952 - val_loss: 0.3985 - val_accuracy: 0.9226\n", + "Epoch 68/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9960 - val_loss: 0.3958 - val_accuracy: 0.9239\n", + "Epoch 69/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0214 - accuracy: 0.9957 - val_loss: 0.4045 - val_accuracy: 0.9207\n", + "Epoch 70/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0212 - accuracy: 0.9952 - val_loss: 0.4025 - val_accuracy: 0.9207\n", + "Epoch 71/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0193 - accuracy: 0.9962 - val_loss: 0.3990 - val_accuracy: 0.9232\n", + "Epoch 72/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0192 - accuracy: 0.9965 - val_loss: 0.4069 - val_accuracy: 0.9239\n", + "Epoch 73/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0180 - accuracy: 0.9966 - val_loss: 0.4285 - val_accuracy: 0.9207\n", + "Epoch 74/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0182 - accuracy: 0.9968 - val_loss: 0.4098 - val_accuracy: 0.9227\n", + "Epoch 75/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0173 - accuracy: 0.9966 - val_loss: 0.4126 - val_accuracy: 0.9229\n", + "Epoch 76/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0161 - accuracy: 0.9973 - val_loss: 0.4139 - val_accuracy: 0.9246\n", + "Epoch 77/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0158 - accuracy: 0.9970 - val_loss: 0.4127 - val_accuracy: 0.9222\n", + "Epoch 78/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0156 - accuracy: 0.9973 - val_loss: 0.4135 - val_accuracy: 0.9249\n", + "Epoch 79/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0155 - accuracy: 0.9975 - val_loss: 0.4178 - val_accuracy: 0.9236\n", + "Epoch 80/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0139 - accuracy: 0.9980 - val_loss: 0.4237 - val_accuracy: 0.9224\n", + "Epoch 81/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0136 - accuracy: 0.9977 - val_loss: 0.4317 - val_accuracy: 0.9212\n", + "Epoch 82/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0133 - accuracy: 0.9981 - val_loss: 0.4270 - val_accuracy: 0.9241\n", + "Epoch 83/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0132 - accuracy: 0.9978 - val_loss: 0.4279 - val_accuracy: 0.9234\n", + "Epoch 84/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0124 - accuracy: 0.9980 - val_loss: 0.4321 - val_accuracy: 0.9232\n", + "Epoch 85/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0127 - accuracy: 0.9978 - val_loss: 0.4910 - val_accuracy: 0.9146\n", + "Epoch 86/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0126 - accuracy: 0.9982 - val_loss: 0.4904 - val_accuracy: 0.9163\n", + "Epoch 87/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0120 - accuracy: 0.9979 - val_loss: 0.4334 - val_accuracy: 0.9227\n", + "Epoch 88/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0107 - accuracy: 0.9985 - val_loss: 0.4390 - val_accuracy: 0.9236\n", + "Epoch 89/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0103 - accuracy: 0.9986 - val_loss: 0.4407 - val_accuracy: 0.9244\n", + "Epoch 90/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0098 - accuracy: 0.9986 - val_loss: 0.4421 - val_accuracy: 0.9239\n", + "Epoch 91/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.0097 - accuracy: 0.9985 - val_loss: 0.4477 - val_accuracy: 0.9222\n", + "Epoch 92/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0095 - accuracy: 0.9986 - val_loss: 0.4510 - val_accuracy: 0.9227\n", + "Epoch 93/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0091 - accuracy: 0.9990 - val_loss: 0.4476 - val_accuracy: 0.9229\n", + "Epoch 94/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0085 - accuracy: 0.9991 - val_loss: 0.4503 - val_accuracy: 0.9242\n", + "Epoch 95/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0086 - accuracy: 0.9988 - val_loss: 0.4593 - val_accuracy: 0.9229\n", + "Epoch 96/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0083 - accuracy: 0.9991 - val_loss: 0.4600 - val_accuracy: 0.9231\n", + "Epoch 97/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0082 - accuracy: 0.9988 - val_loss: 0.4607 - val_accuracy: 0.9231\n", + "Epoch 98/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0077 - accuracy: 0.9993 - val_loss: 0.4600 - val_accuracy: 0.9241\n", + "Epoch 99/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0075 - accuracy: 0.9994 - val_loss: 0.4628 - val_accuracy: 0.9236\n", + "Epoch 100/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.0073 - accuracy: 0.9994 - val_loss: 0.4618 - val_accuracy: 0.9246\n" + ] + } + ], + "source": [ + "model = mlp_model()\n", + "history = model.fit(X_train, y_train, validation_split = 0.3, epochs = 100, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.legend(['training', 'validation'], loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training and validation accuracy improve instantaneously, but reach a plateau after around 30 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 1s 2ms/step - loss: 0.4555 - accuracy: 0.9272\n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.9272000193595886\n" + ] + } + ], + "source": [ + "print('Test accuracy: ', results[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[9.67346939e-01 0.00000000e+00 4.08163265e-03 2.04081633e-03\n", + " 1.02040816e-03 6.12244898e-03 1.12244898e-02 1.02040816e-03\n", + " 4.08163265e-03 3.06122449e-03]\n", + " [8.81057269e-04 9.81497797e-01 3.52422907e-03 1.76211454e-03\n", + " 8.81057269e-04 2.64317181e-03 1.76211454e-03 8.81057269e-04\n", + " 6.16740088e-03 0.00000000e+00]\n", + " [9.68992248e-03 5.81395349e-03 9.16666667e-01 2.13178295e-02\n", + " 2.90697674e-03 7.75193798e-03 7.75193798e-03 8.72093023e-03\n", + " 1.55038760e-02 3.87596899e-03]\n", + " [3.96039604e-03 9.90099010e-04 1.48514851e-02 9.15841584e-01\n", + " 0.00000000e+00 2.67326733e-02 0.00000000e+00 9.90099010e-03\n", + " 2.17821782e-02 5.94059406e-03]\n", + " [2.03665988e-03 1.01832994e-03 7.12830957e-03 0.00000000e+00\n", + " 9.18533605e-01 2.03665988e-03 1.01832994e-02 6.10997963e-03\n", + " 4.07331976e-03 4.88798371e-02]\n", + " [1.34529148e-02 1.12107623e-03 7.84753363e-03 4.48430493e-02\n", + " 5.60538117e-03 8.82286996e-01 8.96860987e-03 4.48430493e-03\n", + " 2.35426009e-02 7.84753363e-03]\n", + " [1.46137787e-02 2.08768267e-03 9.39457203e-03 2.08768267e-03\n", + " 8.35073069e-03 1.98329854e-02 9.38413361e-01 0.00000000e+00\n", + " 5.21920668e-03 0.00000000e+00]\n", + " [1.94552529e-03 6.80933852e-03 1.94552529e-02 6.80933852e-03\n", + " 9.72762646e-03 1.94552529e-03 0.00000000e+00 9.23151751e-01\n", + " 9.72762646e-04 2.91828794e-02]\n", + " [1.02669405e-02 4.10677618e-03 9.24024641e-03 2.66940452e-02\n", + " 7.18685832e-03 1.23203285e-02 7.18685832e-03 8.21355236e-03\n", + " 9.08624230e-01 6.16016427e-03]\n", + " [7.92864222e-03 6.93756194e-03 9.91080278e-04 1.48662042e-02\n", + " 3.07234886e-02 4.95540139e-03 9.91080278e-04 1.58572844e-02\n", + " 7.92864222e-03 9.08820614e-01]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=model.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Optimizers\n", + "- Many variants of SGD are proposed and employed nowadays\n", + "- One of the most popular ones are Adam (Adaptive Moment Estimation)\n", + "- Doc: https://keras.io/optimizers/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
**Relative convergence speed of different optimizers**

" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "def mlp_model():\n", + " model = Sequential()\n", + " \n", + " model.add(Dense(50, input_shape = (784, )))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(10))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001) # use Adam optimizer\n", + " model.compile(optimizer = adam, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.9451 - accuracy: 0.3699 - val_loss: 1.2722 - val_accuracy: 0.6564\n", + "Epoch 2/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.9927 - accuracy: 0.6971 - val_loss: 0.8881 - val_accuracy: 0.6953\n", + "Epoch 3/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.7985 - accuracy: 0.7390 - val_loss: 0.7749 - val_accuracy: 0.7396\n", + "Epoch 4/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.7144 - accuracy: 0.7749 - val_loss: 0.6728 - val_accuracy: 0.7953\n", + "Epoch 5/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6378 - accuracy: 0.8063 - val_loss: 0.6361 - val_accuracy: 0.8051\n", + "Epoch 6/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5941 - accuracy: 0.8181 - val_loss: 0.6600 - val_accuracy: 0.7749\n", + "Epoch 7/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5871 - accuracy: 0.8126 - val_loss: 0.5563 - val_accuracy: 0.8247\n", + "Epoch 8/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5237 - accuracy: 0.8369 - val_loss: 0.5161 - val_accuracy: 0.8375\n", + "Epoch 9/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5078 - accuracy: 0.8445 - val_loss: 0.4913 - val_accuracy: 0.8520\n", + "Epoch 10/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4851 - accuracy: 0.8503 - val_loss: 0.4873 - val_accuracy: 0.8559\n", + "Epoch 11/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4705 - accuracy: 0.8582 - val_loss: 0.4657 - val_accuracy: 0.8611\n", + "Epoch 12/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4438 - accuracy: 0.8646 - val_loss: 0.4384 - val_accuracy: 0.8687\n", + "Epoch 13/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4352 - accuracy: 0.8696 - val_loss: 0.4446 - val_accuracy: 0.8697\n", + "Epoch 14/100\n", + "434/434 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loss: 0.4002 - accuracy: 0.8765 - val_loss: 0.4634 - val_accuracy: 0.8485\n", + "Epoch 21/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4011 - accuracy: 0.8743 - val_loss: 0.4200 - val_accuracy: 0.8699\n", + "Epoch 22/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3921 - accuracy: 0.8772 - val_loss: 0.3871 - val_accuracy: 0.8741\n", + "Epoch 23/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3995 - accuracy: 0.8748 - val_loss: 0.4094 - val_accuracy: 0.8657\n", + "Epoch 24/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3963 - accuracy: 0.8726 - val_loss: 0.3752 - val_accuracy: 0.8862\n", + "Epoch 25/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3758 - accuracy: 0.8848 - val_loss: 0.3759 - val_accuracy: 0.8874\n", + "Epoch 26/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3663 - accuracy: 0.8856 - val_loss: 0.3769 - val_accuracy: 0.8796\n", + "Epoch 27/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3658 - accuracy: 0.8855 - val_loss: 0.3628 - val_accuracy: 0.8877\n", + "Epoch 28/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3522 - accuracy: 0.8919 - val_loss: 0.3612 - val_accuracy: 0.8877\n", + "Epoch 29/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3452 - accuracy: 0.8934 - val_loss: 0.3527 - val_accuracy: 0.8901\n", + "Epoch 30/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3490 - accuracy: 0.8933 - val_loss: 0.3525 - val_accuracy: 0.8891\n", + "Epoch 31/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3247 - accuracy: 0.9033 - val_loss: 0.3124 - val_accuracy: 0.9029\n", + "Epoch 32/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3141 - accuracy: 0.9048 - val_loss: 0.3266 - val_accuracy: 0.9005\n", + "Epoch 33/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3184 - accuracy: 0.9043 - val_loss: 0.3415 - val_accuracy: 0.8966\n", + "Epoch 34/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3389 - accuracy: 0.8947 - val_loss: 0.3382 - val_accuracy: 0.8965\n", + "Epoch 35/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3442 - accuracy: 0.8932 - val_loss: 0.3452 - val_accuracy: 0.8854\n", + "Epoch 36/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3262 - accuracy: 0.8982 - val_loss: 0.3203 - val_accuracy: 0.8976\n", + "Epoch 37/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2983 - accuracy: 0.9115 - val_loss: 0.3118 - val_accuracy: 0.9008\n", + "Epoch 38/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3099 - accuracy: 0.9062 - val_loss: 0.3347 - val_accuracy: 0.8995\n", + "Epoch 39/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.3169 - accuracy: 0.9037 - val_loss: 0.3332 - val_accuracy: 0.8973\n", + "Epoch 40/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3227 - accuracy: 0.9010 - val_loss: 0.3340 - val_accuracy: 0.8931\n", + "Epoch 41/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3204 - accuracy: 0.9040 - val_loss: 0.3599 - val_accuracy: 0.8857\n", + "Epoch 42/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3347 - accuracy: 0.8958 - val_loss: 0.3392 - val_accuracy: 0.8965\n", + "Epoch 43/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3189 - accuracy: 0.9015 - val_loss: 0.3255 - val_accuracy: 0.9017\n", + "Epoch 44/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3138 - accuracy: 0.9043 - val_loss: 0.3216 - val_accuracy: 0.8975\n", + "Epoch 45/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2959 - accuracy: 0.9082 - val_loss: 0.3105 - val_accuracy: 0.9071\n", + "Epoch 46/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2991 - accuracy: 0.9071 - val_loss: 0.3113 - val_accuracy: 0.9020\n", + "Epoch 47/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2968 - accuracy: 0.9066 - val_loss: 0.3163 - val_accuracy: 0.8985\n", + "Epoch 48/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.3010 - accuracy: 0.9051 - val_loss: 0.3142 - val_accuracy: 0.9045\n", + "Epoch 49/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2798 - accuracy: 0.9125 - val_loss: 0.3023 - val_accuracy: 0.9099\n", + "Epoch 50/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2808 - accuracy: 0.9123 - val_loss: 0.3017 - val_accuracy: 0.9114\n", + "Epoch 51/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2937 - accuracy: 0.9098 - val_loss: 0.2922 - val_accuracy: 0.9113\n", + "Epoch 52/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.2871 - accuracy: 0.9123 - val_loss: 0.2874 - val_accuracy: 0.9150\n", + "Epoch 53/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2772 - accuracy: 0.9163 - val_loss: 0.2916 - val_accuracy: 0.9104\n", + "Epoch 54/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2732 - accuracy: 0.9164 - val_loss: 0.2796 - val_accuracy: 0.9141\n", + "Epoch 55/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2533 - accuracy: 0.9233 - val_loss: 0.2639 - val_accuracy: 0.9226\n", + "Epoch 56/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2451 - accuracy: 0.9236 - val_loss: 0.2699 - val_accuracy: 0.9199\n", + "Epoch 57/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2696 - accuracy: 0.9182 - val_loss: 0.3003 - val_accuracy: 0.9076\n", + "Epoch 58/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2795 - accuracy: 0.9151 - val_loss: 0.2868 - val_accuracy: 0.9111\n", + "Epoch 59/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2628 - accuracy: 0.9177 - val_loss: 0.2779 - val_accuracy: 0.9128\n", + "Epoch 60/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2646 - accuracy: 0.9197 - val_loss: 0.2831 - val_accuracy: 0.9109\n", + "Epoch 61/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2584 - accuracy: 0.9196 - val_loss: 0.2931 - val_accuracy: 0.9089\n", + "Epoch 62/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.2534 - accuracy: 0.9240 - val_loss: 0.3003 - val_accuracy: 0.9088\n", + "Epoch 63/100\n", + "434/434 [==============================] - 1s 2ms/step - loss: 0.2640 - accuracy: 0.9184 - val_loss: 0.2940 - val_accuracy: 0.9098\n", + "Epoch 64/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2601 - accuracy: 0.9168 - val_loss: 0.2795 - val_accuracy: 0.9128\n", + "Epoch 65/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2542 - accuracy: 0.9207 - val_loss: 0.2839 - val_accuracy: 0.9140\n", + "Epoch 66/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2460 - accuracy: 0.9278 - val_loss: 0.2706 - val_accuracy: 0.9140\n", + "Epoch 67/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2382 - accuracy: 0.9257 - val_loss: 0.2677 - val_accuracy: 0.9167\n", + "Epoch 68/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2400 - accuracy: 0.9259 - val_loss: 0.2707 - val_accuracy: 0.9165\n", + "Epoch 69/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2482 - accuracy: 0.9229 - val_loss: 0.2802 - val_accuracy: 0.9114\n", + "Epoch 70/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2472 - accuracy: 0.9236 - val_loss: 0.2725 - val_accuracy: 0.9195\n", + "Epoch 71/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2364 - accuracy: 0.9297 - val_loss: 0.2587 - val_accuracy: 0.9195\n", + "Epoch 72/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2323 - accuracy: 0.9277 - val_loss: 0.2551 - val_accuracy: 0.9229\n", + "Epoch 73/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2238 - accuracy: 0.9310 - val_loss: 0.2577 - val_accuracy: 0.9217\n", + "Epoch 74/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2425 - accuracy: 0.9268 - val_loss: 0.2779 - val_accuracy: 0.9123\n", + "Epoch 75/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2407 - accuracy: 0.9258 - val_loss: 0.2632 - val_accuracy: 0.9172\n", + "Epoch 76/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2524 - accuracy: 0.9225 - val_loss: 0.2725 - val_accuracy: 0.9128\n", + "Epoch 77/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2481 - accuracy: 0.9215 - val_loss: 0.2562 - val_accuracy: 0.9212\n", + "Epoch 78/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.2317 - accuracy: 0.9291 - val_loss: 0.2423 - val_accuracy: 0.9254\n", + "Epoch 79/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2328 - accuracy: 0.9276 - val_loss: 0.2594 - val_accuracy: 0.9229\n", + "Epoch 80/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2310 - accuracy: 0.9278 - val_loss: 0.2780 - val_accuracy: 0.9153\n", + "Epoch 81/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2449 - accuracy: 0.9220 - val_loss: 0.2742 - val_accuracy: 0.9141\n", + "Epoch 82/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2324 - accuracy: 0.9246 - val_loss: 0.2633 - val_accuracy: 0.9185\n", + "Epoch 83/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2283 - accuracy: 0.9263 - val_loss: 0.2536 - val_accuracy: 0.9231\n", + "Epoch 84/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2252 - accuracy: 0.9281 - val_loss: 0.2624 - val_accuracy: 0.9194\n", + "Epoch 85/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2238 - accuracy: 0.9289 - val_loss: 0.2527 - val_accuracy: 0.9215\n", + "Epoch 86/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2262 - accuracy: 0.9286 - val_loss: 0.2575 - val_accuracy: 0.9205\n", + "Epoch 87/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2299 - accuracy: 0.9263 - val_loss: 0.2554 - val_accuracy: 0.9199\n", + "Epoch 88/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 0.2258 - accuracy: 0.9282 - val_loss: 0.2454 - val_accuracy: 0.9204\n", + "Epoch 89/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.2156 - accuracy: 0.9313 - val_loss: 0.2489 - val_accuracy: 0.9231\n", + "Epoch 90/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2206 - accuracy: 0.9312 - val_loss: 0.2621 - val_accuracy: 0.9185\n", + "Epoch 91/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2215 - accuracy: 0.9310 - val_loss: 0.2751 - val_accuracy: 0.9172\n", + "Epoch 92/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2148 - accuracy: 0.9359 - val_loss: 0.2611 - val_accuracy: 0.9167\n", + "Epoch 93/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.2145 - accuracy: 0.9308 - val_loss: 0.2714 - val_accuracy: 0.9158\n", + "Epoch 94/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2134 - accuracy: 0.9325 - val_loss: 0.2373 - val_accuracy: 0.9263\n", + "Epoch 95/100\n", + "434/434 [==============================] - 2s 5ms/step - loss: 0.2098 - accuracy: 0.9343 - val_loss: 0.2590 - val_accuracy: 0.9237\n", + "Epoch 96/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.1973 - accuracy: 0.9361 - val_loss: 0.2519 - val_accuracy: 0.9227\n", + "Epoch 97/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2035 - accuracy: 0.9341 - val_loss: 0.2390 - val_accuracy: 0.9269\n", + "Epoch 98/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.2142 - accuracy: 0.9325 - val_loss: 0.2543 - val_accuracy: 0.9227\n", + "Epoch 99/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.2138 - accuracy: 0.9341 - val_loss: 0.2547 - val_accuracy: 0.9229\n", + "Epoch 100/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.2167 - accuracy: 0.9318 - val_loss: 0.2672 - val_accuracy: 0.9182\n" + ] + } + ], + "source": [ + "model = mlp_model()\n", + "history = model.fit(X_train, y_train, validation_split = 0.3, epochs = 100, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.legend(['training', 'validation'], loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training and validation accuracy improve instantaneously, but reach plateau after around 50 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 0s 1ms/step - loss: 0.2678 - accuracy: 0.9173\n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.9172999858856201\n" + ] + } + ], + "source": [ + "print('Test accuracy: ', results[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[9.68367347e-01 0.00000000e+00 1.02040816e-03 3.06122449e-03\n", + " 0.00000000e+00 9.18367347e-03 1.02040816e-02 4.08163265e-03\n", + " 3.06122449e-03 1.02040816e-03]\n", + " [0.00000000e+00 9.77092511e-01 1.76211454e-03 5.28634361e-03\n", + " 1.76211454e-03 0.00000000e+00 5.28634361e-03 0.00000000e+00\n", + " 7.92951542e-03 8.81057269e-04]\n", + " [1.16279070e-02 6.78294574e-03 9.08914729e-01 2.90697674e-02\n", + " 4.84496124e-03 2.90697674e-03 1.16279070e-02 7.75193798e-03\n", + " 1.55038760e-02 9.68992248e-04]\n", + " [2.97029703e-03 9.90099010e-04 1.78217822e-02 9.09900990e-01\n", + " 0.00000000e+00 2.77227723e-02 9.90099010e-04 1.28712871e-02\n", + " 2.27722772e-02 3.96039604e-03]\n", + " [3.05498982e-03 3.05498982e-03 6.10997963e-03 0.00000000e+00\n", + " 9.32790224e-01 3.05498982e-03 8.14663951e-03 3.05498982e-03\n", + " 7.12830957e-03 3.36048880e-02]\n", + " [1.56950673e-02 2.24215247e-03 4.48430493e-03 4.70852018e-02\n", + " 8.96860987e-03 8.72197309e-01 1.56950673e-02 6.72645740e-03\n", + " 2.01793722e-02 6.72645740e-03]\n", + " [1.56576200e-02 3.13152401e-03 1.14822547e-02 1.04384134e-03\n", + " 1.46137787e-02 1.46137787e-02 9.29018789e-01 0.00000000e+00\n", + " 9.39457203e-03 1.04384134e-03]\n", + " [0.00000000e+00 9.72762646e-03 1.65369650e-02 1.07003891e-02\n", + " 3.89105058e-03 9.72762646e-04 0.00000000e+00 9.37743191e-01\n", + " 9.72762646e-04 1.94552529e-02]\n", + " [1.23203285e-02 5.13347023e-03 1.02669405e-02 2.66940452e-02\n", + " 9.24024641e-03 2.97741273e-02 1.33470226e-02 8.21355236e-03\n", + " 8.51129363e-01 3.38809035e-02]\n", + " [4.95540139e-03 7.92864222e-03 0.00000000e+00 1.48662042e-02\n", + " 5.25272547e-02 1.78394450e-02 9.91080278e-04 1.88305253e-02\n", + " 8.91972250e-03 8.73141724e-01]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=model.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Batch Normalization\n", + "- Batch Normalization, one of the methods to prevent the \"internal covariance shift\" problem, has proven to be highly effective\n", + "- Normalize each mini-batch before nonlinearity\n", + "- Doc: https://keras.io/optimizers/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "
Batch normalization layer is usually inserted after dense/convolution and before nonlinearity" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import BatchNormalization" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def mlp_model():\n", + " model = Sequential()\n", + " \n", + " model.add(Dense(50, input_shape = (784, )))\n", + " model.add(BatchNormalization()) # Add Batchnorm layer before Activation\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(BatchNormalization()) # Add Batchnorm layer before Activation\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(BatchNormalization()) # Add Batchnorm layer before Activation\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(BatchNormalization()) # Add Batchnorm layer before Activation\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(10))\n", + " model.add(Activation('softmax'))\n", + " \n", + " sgd = optimizers.SGD(lr = 0.001)\n", + " model.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.2577 - accuracy: 0.2309 - val_loss: 2.1423 - val_accuracy: 0.3283\n", + "Epoch 2/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.0092 - accuracy: 0.4118 - val_loss: 1.9131 - val_accuracy: 0.4936\n", + "Epoch 3/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.8629 - accuracy: 0.5313 - val_loss: 1.7874 - val_accuracy: 0.5751\n", + "Epoch 4/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.7558 - accuracy: 0.6006 - val_loss: 1.6936 - val_accuracy: 0.6354\n", + "Epoch 5/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.6728 - accuracy: 0.6499 - val_loss: 1.6170 - val_accuracy: 0.6753\n", + "Epoch 6/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.6026 - accuracy: 0.6830 - val_loss: 1.5533 - val_accuracy: 0.7066\n", + "Epoch 7/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.5452 - accuracy: 0.7104 - val_loss: 1.4961 - val_accuracy: 0.7273\n", + "Epoch 8/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.4932 - accuracy: 0.7289 - val_loss: 1.4461 - val_accuracy: 0.7475\n", + "Epoch 9/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.4490 - accuracy: 0.7432 - val_loss: 1.4011 - val_accuracy: 0.7611\n", + "Epoch 10/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.4034 - accuracy: 0.7623 - val_loss: 1.3597 - val_accuracy: 0.7724\n", + "Epoch 11/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.3643 - accuracy: 0.7720 - val_loss: 1.3216 - val_accuracy: 0.7833\n", + "Epoch 12/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.3265 - accuracy: 0.7821 - val_loss: 1.2849 - val_accuracy: 0.7918\n", + "Epoch 13/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.2935 - accuracy: 0.7892 - val_loss: 1.2520 - val_accuracy: 0.8002\n", + "Epoch 14/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.2617 - accuracy: 0.7946 - val_loss: 1.2178 - val_accuracy: 0.8113\n", + "Epoch 15/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.2269 - accuracy: 0.8076 - val_loss: 1.1883 - val_accuracy: 0.8180\n", + "Epoch 16/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 1.1994 - accuracy: 0.8105 - val_loss: 1.1623 - val_accuracy: 0.8205\n", + "Epoch 17/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 1.1727 - accuracy: 0.8147 - val_loss: 1.1338 - val_accuracy: 0.8268\n", + "Epoch 18/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.1472 - accuracy: 0.8247 - val_loss: 1.1071 - val_accuracy: 0.8311\n", + "Epoch 19/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.1204 - accuracy: 0.8271 - val_loss: 1.0810 - val_accuracy: 0.8375\n", + "Epoch 20/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.0948 - accuracy: 0.8294 - val_loss: 1.0572 - val_accuracy: 0.8406\n", + "Epoch 21/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.0694 - accuracy: 0.8338 - val_loss: 1.0331 - val_accuracy: 0.8448\n", + "Epoch 22/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.0492 - accuracy: 0.8370 - val_loss: 1.0105 - val_accuracy: 0.8475\n", + "Epoch 23/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 1.0260 - accuracy: 0.8400 - val_loss: 0.9882 - val_accuracy: 0.8497\n", + "Epoch 24/100\n", + "434/434 [==============================] - 2s 5ms/step - loss: 1.0056 - accuracy: 0.8409 - val_loss: 0.9691 - val_accuracy: 0.8529\n", + "Epoch 25/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.9889 - accuracy: 0.8452 - val_loss: 0.9488 - val_accuracy: 0.8545\n", + "Epoch 26/100\n", + "434/434 [==============================] - 2s 5ms/step - loss: 0.9668 - accuracy: 0.8499 - val_loss: 0.9288 - val_accuracy: 0.8574\n", + "Epoch 27/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.9474 - accuracy: 0.8496 - val_loss: 0.9112 - val_accuracy: 0.8608\n", + "Epoch 28/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.9267 - accuracy: 0.8545 - val_loss: 0.8918 - val_accuracy: 0.8640\n", + "Epoch 29/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.9103 - accuracy: 0.8566 - val_loss: 0.8737 - val_accuracy: 0.8640\n", + "Epoch 30/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.8967 - accuracy: 0.8548 - val_loss: 0.8578 - val_accuracy: 0.8665\n", + "Epoch 31/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.8778 - accuracy: 0.8613 - val_loss: 0.8417 - val_accuracy: 0.8685\n", + "Epoch 32/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.8625 - accuracy: 0.8600 - val_loss: 0.8268 - val_accuracy: 0.8670\n", + "Epoch 33/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 0.8493 - accuracy: 0.8611 - val_loss: 0.8127 - val_accuracy: 0.8712\n", + "Epoch 34/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.8324 - accuracy: 0.8646 - val_loss: 0.7994 - val_accuracy: 0.8717\n", + "Epoch 35/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.8195 - accuracy: 0.8636 - val_loss: 0.7836 - val_accuracy: 0.8744\n", + "Epoch 36/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.8086 - accuracy: 0.8633 - val_loss: 0.7703 - val_accuracy: 0.8751\n", + "Epoch 37/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 0.7919 - accuracy: 0.8652 - val_loss: 0.7590 - val_accuracy: 0.8753\n", + "Epoch 38/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.7804 - accuracy: 0.8693 - val_loss: 0.7449 - val_accuracy: 0.8776\n", + "Epoch 39/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.7654 - accuracy: 0.8723 - val_loss: 0.7343 - val_accuracy: 0.8774\n", + "Epoch 40/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.7548 - accuracy: 0.8715 - val_loss: 0.7237 - val_accuracy: 0.8786\n", + "Epoch 41/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.7433 - accuracy: 0.8734 - val_loss: 0.7134 - val_accuracy: 0.8796\n", + "Epoch 42/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.7343 - accuracy: 0.8709 - val_loss: 0.7016 - val_accuracy: 0.8811\n", + "Epoch 43/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.7209 - accuracy: 0.8765 - val_loss: 0.6905 - val_accuracy: 0.8828\n", + "Epoch 44/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.7131 - accuracy: 0.8770 - val_loss: 0.6826 - val_accuracy: 0.8825\n", + "Epoch 45/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.7016 - accuracy: 0.8760 - val_loss: 0.6709 - val_accuracy: 0.8840\n", + "Epoch 46/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.6923 - accuracy: 0.8755 - val_loss: 0.6602 - val_accuracy: 0.8852\n", + "Epoch 47/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6808 - accuracy: 0.8790 - val_loss: 0.6511 - val_accuracy: 0.8837\n", + "Epoch 48/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6713 - accuracy: 0.8805 - val_loss: 0.6436 - val_accuracy: 0.8855\n", + "Epoch 49/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6668 - accuracy: 0.8792 - val_loss: 0.6344 - val_accuracy: 0.8859\n", + "Epoch 50/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6575 - accuracy: 0.8804 - val_loss: 0.6265 - val_accuracy: 0.8884\n", + "Epoch 51/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6490 - accuracy: 0.8815 - val_loss: 0.6185 - val_accuracy: 0.8877\n", + "Epoch 52/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6425 - accuracy: 0.8804 - val_loss: 0.6095 - val_accuracy: 0.8889\n", + "Epoch 53/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6318 - accuracy: 0.8835 - val_loss: 0.6041 - val_accuracy: 0.8891\n", + "Epoch 54/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.6248 - accuracy: 0.8831 - val_loss: 0.5958 - val_accuracy: 0.8896\n", + "Epoch 55/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.6166 - accuracy: 0.8846 - val_loss: 0.5871 - val_accuracy: 0.8906\n", + "Epoch 56/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.6118 - accuracy: 0.8833 - val_loss: 0.5818 - val_accuracy: 0.8906\n", + "Epoch 57/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.6027 - accuracy: 0.8852 - val_loss: 0.5747 - val_accuracy: 0.8912\n", + "Epoch 58/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5949 - accuracy: 0.8880 - val_loss: 0.5671 - val_accuracy: 0.8916\n", + "Epoch 59/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5899 - accuracy: 0.8852 - val_loss: 0.5631 - val_accuracy: 0.8914\n", + "Epoch 60/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5832 - accuracy: 0.8875 - val_loss: 0.5564 - val_accuracy: 0.8923\n", + "Epoch 61/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5764 - accuracy: 0.8875 - val_loss: 0.5503 - val_accuracy: 0.8928\n", + "Epoch 62/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5708 - accuracy: 0.8877 - val_loss: 0.5465 - val_accuracy: 0.8931\n", + "Epoch 63/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.5641 - accuracy: 0.8882 - val_loss: 0.5389 - val_accuracy: 0.8943\n", + "Epoch 64/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5583 - accuracy: 0.8894 - val_loss: 0.5346 - val_accuracy: 0.8939\n", + "Epoch 65/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.5518 - accuracy: 0.8902 - val_loss: 0.5295 - val_accuracy: 0.8953\n", + "Epoch 66/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.5449 - accuracy: 0.8901 - val_loss: 0.5236 - val_accuracy: 0.8946\n", + "Epoch 67/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5405 - accuracy: 0.8927 - val_loss: 0.5189 - val_accuracy: 0.8958\n", + "Epoch 68/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5362 - accuracy: 0.8928 - val_loss: 0.5131 - val_accuracy: 0.8958\n", + "Epoch 69/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5319 - accuracy: 0.8928 - val_loss: 0.5097 - val_accuracy: 0.8949\n", + "Epoch 70/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.5238 - accuracy: 0.8967 - val_loss: 0.5052 - val_accuracy: 0.8961\n", + "Epoch 71/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5204 - accuracy: 0.8947 - val_loss: 0.5005 - val_accuracy: 0.8973\n", + "Epoch 72/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5179 - accuracy: 0.8940 - val_loss: 0.4948 - val_accuracy: 0.8983\n", + "Epoch 73/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5132 - accuracy: 0.8936 - val_loss: 0.4918 - val_accuracy: 0.8973\n", + "Epoch 74/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5038 - accuracy: 0.8990 - val_loss: 0.4866 - val_accuracy: 0.8976\n", + "Epoch 75/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.5024 - accuracy: 0.8961 - val_loss: 0.4833 - val_accuracy: 0.8987\n", + "Epoch 76/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4987 - accuracy: 0.8988 - val_loss: 0.4798 - val_accuracy: 0.8992\n", + "Epoch 77/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.4934 - accuracy: 0.8973 - val_loss: 0.4756 - val_accuracy: 0.8997\n", + "Epoch 78/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4913 - accuracy: 0.8963 - val_loss: 0.4738 - val_accuracy: 0.9007\n", + "Epoch 79/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4850 - accuracy: 0.9006 - val_loss: 0.4684 - val_accuracy: 0.9000\n", + "Epoch 80/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.4837 - accuracy: 0.8996 - val_loss: 0.4650 - val_accuracy: 0.9005\n", + "Epoch 81/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4798 - accuracy: 0.8998 - val_loss: 0.4603 - val_accuracy: 0.9003\n", + "Epoch 82/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4761 - accuracy: 0.8996 - val_loss: 0.4584 - val_accuracy: 0.9022\n", + "Epoch 83/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4718 - accuracy: 0.8980 - val_loss: 0.4558 - val_accuracy: 0.9017\n", + "Epoch 84/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.4708 - accuracy: 0.9009 - val_loss: 0.4514 - val_accuracy: 0.9027\n", + "Epoch 85/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4634 - accuracy: 0.9030 - val_loss: 0.4470 - val_accuracy: 0.9032\n", + "Epoch 86/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4592 - accuracy: 0.9028 - val_loss: 0.4442 - val_accuracy: 0.9039\n", + "Epoch 87/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 0.4563 - accuracy: 0.9037 - val_loss: 0.4418 - val_accuracy: 0.9027\n", + "Epoch 88/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4532 - accuracy: 0.9029 - val_loss: 0.4403 - val_accuracy: 0.9044\n", + "Epoch 89/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4511 - accuracy: 0.9045 - val_loss: 0.4362 - val_accuracy: 0.9040\n", + "Epoch 90/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4497 - accuracy: 0.9020 - val_loss: 0.4315 - val_accuracy: 0.9035\n", + "Epoch 91/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4452 - accuracy: 0.9054 - val_loss: 0.4300 - val_accuracy: 0.9042\n", + "Epoch 92/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4405 - accuracy: 0.9045 - val_loss: 0.4285 - val_accuracy: 0.9054\n", + "Epoch 93/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4372 - accuracy: 0.9040 - val_loss: 0.4254 - val_accuracy: 0.9052\n", + "Epoch 94/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4349 - accuracy: 0.9093 - val_loss: 0.4219 - val_accuracy: 0.9066\n", + "Epoch 95/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4321 - accuracy: 0.9045 - val_loss: 0.4199 - val_accuracy: 0.9071\n", + "Epoch 96/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4299 - accuracy: 0.9086 - val_loss: 0.4168 - val_accuracy: 0.9069\n", + "Epoch 97/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4231 - accuracy: 0.9079 - val_loss: 0.4151 - val_accuracy: 0.9074\n", + "Epoch 98/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4227 - accuracy: 0.9058 - val_loss: 0.4124 - val_accuracy: 0.9089\n", + "Epoch 99/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4199 - accuracy: 0.9100 - val_loss: 0.4094 - val_accuracy: 0.9088\n", + "Epoch 100/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 0.4188 - accuracy: 0.9071 - val_loss: 0.4078 - val_accuracy: 0.9086\n" + ] + } + ], + "source": [ + "model = mlp_model()\n", + "history = model.fit(X_train, y_train, validation_split = 0.3, epochs = 100, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.legend(['training', 'validation'], loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training and validation accuracy improve consistently, but reach plateau after around 60 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 1s 2ms/step - loss: 0.3933 - accuracy: 0.9112\n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.9111999869346619\n" + ] + } + ], + "source": [ + "print('Test accuracy: ', results[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[9.73469388e-01 0.00000000e+00 3.06122449e-03 1.02040816e-03\n", + " 1.02040816e-03 8.16326531e-03 8.16326531e-03 1.02040816e-03\n", + " 3.06122449e-03 1.02040816e-03]\n", + " [0.00000000e+00 9.81497797e-01 2.64317181e-03 2.64317181e-03\n", + " 8.81057269e-04 1.76211454e-03 2.64317181e-03 1.76211454e-03\n", + " 6.16740088e-03 0.00000000e+00]\n", + " [1.06589147e-02 6.78294574e-03 8.86627907e-01 1.25968992e-02\n", + " 1.45348837e-02 9.68992248e-04 1.35658915e-02 1.64728682e-02\n", + " 3.68217054e-02 9.68992248e-04]\n", + " [1.98019802e-03 2.97029703e-03 2.27722772e-02 9.00000000e-01\n", + " 2.97029703e-03 2.07920792e-02 2.97029703e-03 1.78217822e-02\n", + " 2.27722772e-02 4.95049505e-03]\n", + " [0.00000000e+00 3.05498982e-03 4.07331976e-03 0.00000000e+00\n", + " 9.33808554e-01 1.01832994e-03 1.32382892e-02 2.03665988e-03\n", + " 5.09164969e-03 3.76782077e-02]\n", + " [1.68161435e-02 1.12107623e-03 6.72645740e-03 4.48430493e-02\n", + " 1.23318386e-02 8.37443946e-01 2.13004484e-02 1.34529148e-02\n", + " 3.69955157e-02 8.96860987e-03]\n", + " [9.39457203e-03 5.21920668e-03 8.35073069e-03 0.00000000e+00\n", + " 9.39457203e-03 2.08768267e-02 9.43632568e-01 2.08768267e-03\n", + " 1.04384134e-03 0.00000000e+00]\n", + " [2.91828794e-03 1.75097276e-02 2.14007782e-02 3.89105058e-03\n", + " 1.16731518e-02 9.72762646e-04 0.00000000e+00 8.98832685e-01\n", + " 1.94552529e-03 4.08560311e-02]\n", + " [6.16016427e-03 1.95071869e-02 1.02669405e-02 1.74537988e-02\n", + " 1.74537988e-02 3.28542094e-02 1.12936345e-02 1.54004107e-02\n", + " 8.63449692e-01 6.16016427e-03]\n", + " [1.28840436e-02 7.92864222e-03 9.91080278e-04 1.28840436e-02\n", + " 4.95540139e-02 5.94648167e-03 9.91080278e-04 1.98216056e-02\n", + " 9.91080278e-03 8.79088206e-01]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=model.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Dropout (Regularization)\n", + "- Dropout is one of powerful ways to prevent overfitting\n", + "- The idea is simple. It is disconnecting some (randomly selected) neurons in each layer\n", + "- The probability of each neuron to be disconnected, namely 'Dropout rate', has to be designated\n", + "- Doc: https://keras.io/layers/core/#dropout" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Dropout" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "def mlp_model():\n", + " model = Sequential()\n", + " \n", + " model.add(Dense(50, input_shape = (784, )))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dropout(0.2)) # Dropout layer after Activation\n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid'))\n", + " model.add(Dropout(0.2)) # Dropout layer after Activation\n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dropout(0.2)) # Dropout layer after Activation\n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dropout(0.2)) # Dropout layer after Activation\n", + " model.add(Dense(10))\n", + " model.add(Activation('softmax'))\n", + " \n", + " sgd = optimizers.SGD(lr = 0.001)\n", + " model.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.5138 - accuracy: 0.1040 - val_loss: 2.4026 - val_accuracy: 0.0929\n", + "Epoch 2/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.4075 - accuracy: 0.1023 - val_loss: 2.3374 - val_accuracy: 0.1237\n", + "Epoch 3/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3678 - accuracy: 0.1062 - val_loss: 2.3129 - val_accuracy: 0.1118\n", + "Epoch 4/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3579 - accuracy: 0.1001 - val_loss: 2.3046 - val_accuracy: 0.1118\n", + "Epoch 5/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3507 - accuracy: 0.1046 - val_loss: 2.3019 - val_accuracy: 0.1118\n", + "Epoch 6/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3501 - accuracy: 0.1018 - val_loss: 2.3008 - val_accuracy: 0.1118\n", + "Epoch 7/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3472 - accuracy: 0.1046 - val_loss: 2.3002 - val_accuracy: 0.1118\n", + "Epoch 8/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3497 - accuracy: 0.0999 - val_loss: 2.2999 - val_accuracy: 0.1118\n", + "Epoch 9/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3481 - accuracy: 0.1021 - val_loss: 2.2997 - val_accuracy: 0.1118\n", + "Epoch 10/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3483 - accuracy: 0.1035 - val_loss: 2.2995 - val_accuracy: 0.1118\n", + "Epoch 11/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3480 - accuracy: 0.1022 - val_loss: 2.2993 - val_accuracy: 0.1118\n", + "Epoch 12/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3425 - accuracy: 0.1058 - val_loss: 2.2991 - val_accuracy: 0.1118\n", + "Epoch 13/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3447 - accuracy: 0.1043 - val_loss: 2.2990 - val_accuracy: 0.1118\n", + "Epoch 14/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3436 - accuracy: 0.1082 - val_loss: 2.2989 - val_accuracy: 0.1118\n", + "Epoch 15/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3417 - accuracy: 0.1071 - val_loss: 2.2987 - val_accuracy: 0.1118\n", + "Epoch 16/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3426 - accuracy: 0.1064 - val_loss: 2.2985 - val_accuracy: 0.1118\n", + "Epoch 17/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3393 - accuracy: 0.1105 - val_loss: 2.2984 - val_accuracy: 0.1118\n", + "Epoch 18/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3386 - accuracy: 0.1083 - val_loss: 2.2983 - val_accuracy: 0.1118\n", + "Epoch 19/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 2.3360 - accuracy: 0.1069 - val_loss: 2.2982 - val_accuracy: 0.1118\n", + "Epoch 20/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3376 - accuracy: 0.1035 - val_loss: 2.2980 - val_accuracy: 0.1118\n", + "Epoch 21/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3388 - accuracy: 0.1045 - val_loss: 2.2979 - val_accuracy: 0.1118\n", + "Epoch 22/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3360 - accuracy: 0.1048 - val_loss: 2.2977 - val_accuracy: 0.1118\n", + "Epoch 23/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3345 - accuracy: 0.1135 - val_loss: 2.2976 - val_accuracy: 0.1118\n", + "Epoch 24/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3348 - accuracy: 0.1073 - val_loss: 2.2975 - val_accuracy: 0.1118\n", + "Epoch 25/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3364 - accuracy: 0.1043 - val_loss: 2.2974 - val_accuracy: 0.1118\n", + "Epoch 26/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3348 - accuracy: 0.1015 - val_loss: 2.2972 - val_accuracy: 0.1118\n", + "Epoch 27/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3374 - accuracy: 0.1037 - val_loss: 2.2971 - val_accuracy: 0.1118\n", + "Epoch 28/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3356 - accuracy: 0.1063 - val_loss: 2.2970 - val_accuracy: 0.1118\n", + "Epoch 29/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3364 - accuracy: 0.1064 - val_loss: 2.2968 - val_accuracy: 0.1118\n", + "Epoch 30/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3333 - accuracy: 0.1101 - val_loss: 2.2967 - val_accuracy: 0.1118\n", + "Epoch 31/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3320 - accuracy: 0.1108 - val_loss: 2.2965 - val_accuracy: 0.1118\n", + "Epoch 32/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3307 - accuracy: 0.1090 - val_loss: 2.2965 - val_accuracy: 0.1118\n", + "Epoch 33/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3320 - accuracy: 0.1084 - val_loss: 2.2963 - val_accuracy: 0.1118\n", + "Epoch 34/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3312 - accuracy: 0.1063 - val_loss: 2.2961 - val_accuracy: 0.1118\n", + "Epoch 35/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3288 - accuracy: 0.1079 - val_loss: 2.2960 - val_accuracy: 0.1118\n", + "Epoch 36/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3343 - accuracy: 0.1010 - val_loss: 2.2959 - val_accuracy: 0.1118\n", + "Epoch 37/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3314 - accuracy: 0.1067 - val_loss: 2.2958 - val_accuracy: 0.1118\n", + "Epoch 38/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3294 - accuracy: 0.1118 - val_loss: 2.2957 - val_accuracy: 0.1118\n", + "Epoch 39/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3322 - accuracy: 0.1041 - val_loss: 2.2955 - val_accuracy: 0.1118\n", + "Epoch 40/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3310 - accuracy: 0.1035 - val_loss: 2.2955 - val_accuracy: 0.1118\n", + "Epoch 41/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3264 - accuracy: 0.1066 - val_loss: 2.2953 - val_accuracy: 0.1118\n", + "Epoch 42/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3231 - accuracy: 0.1086 - val_loss: 2.2952 - val_accuracy: 0.1118\n", + "Epoch 43/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3281 - accuracy: 0.1060 - val_loss: 2.2951 - val_accuracy: 0.1118\n", + "Epoch 44/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3297 - accuracy: 0.1073 - val_loss: 2.2950 - val_accuracy: 0.1118\n", + "Epoch 45/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3277 - accuracy: 0.1078 - val_loss: 2.2948 - val_accuracy: 0.1118\n", + "Epoch 46/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3253 - accuracy: 0.1108 - val_loss: 2.2947 - val_accuracy: 0.1118\n", + "Epoch 47/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3263 - accuracy: 0.1077 - val_loss: 2.2946 - val_accuracy: 0.1118\n", + "Epoch 48/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3257 - accuracy: 0.1081 - val_loss: 2.2945 - val_accuracy: 0.1118\n", + "Epoch 49/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3296 - accuracy: 0.1043 - val_loss: 2.2943 - val_accuracy: 0.1118\n", + "Epoch 50/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3257 - accuracy: 0.1068 - val_loss: 2.2942 - val_accuracy: 0.1118\n", + "Epoch 51/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3244 - accuracy: 0.1096 - val_loss: 2.2940 - val_accuracy: 0.1118\n", + "Epoch 52/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3255 - accuracy: 0.1118 - val_loss: 2.2939 - val_accuracy: 0.1118\n", + "Epoch 53/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3224 - accuracy: 0.1100 - val_loss: 2.2938 - val_accuracy: 0.1118\n", + "Epoch 54/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3230 - accuracy: 0.1076 - val_loss: 2.2936 - val_accuracy: 0.1118\n", + "Epoch 55/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3233 - accuracy: 0.1122 - val_loss: 2.2935 - val_accuracy: 0.1118\n", + "Epoch 56/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3214 - accuracy: 0.1074 - val_loss: 2.2933 - val_accuracy: 0.1118\n", + "Epoch 57/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3227 - accuracy: 0.1106 - val_loss: 2.2932 - val_accuracy: 0.1118\n", + "Epoch 58/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3177 - accuracy: 0.1133 - val_loss: 2.2930 - val_accuracy: 0.1118\n", + "Epoch 59/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3226 - accuracy: 0.1071 - val_loss: 2.2929 - val_accuracy: 0.1118\n", + "Epoch 60/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3207 - accuracy: 0.1096 - val_loss: 2.2928 - val_accuracy: 0.1118\n", + "Epoch 61/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3232 - accuracy: 0.1108 - val_loss: 2.2927 - val_accuracy: 0.1118\n", + "Epoch 62/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3179 - accuracy: 0.1120 - val_loss: 2.2925 - val_accuracy: 0.1118\n", + "Epoch 63/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3186 - accuracy: 0.1108 - val_loss: 2.2923 - val_accuracy: 0.1118\n", + "Epoch 64/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3171 - accuracy: 0.1143 - val_loss: 2.2921 - val_accuracy: 0.1118\n", + "Epoch 65/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3199 - accuracy: 0.1078 - val_loss: 2.2921 - val_accuracy: 0.1118\n", + "Epoch 66/100\n", + "434/434 [==============================] - 2s 3ms/step - loss: 2.3200 - accuracy: 0.1106 - val_loss: 2.2919 - val_accuracy: 0.1118\n", + "Epoch 67/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3196 - accuracy: 0.1084 - val_loss: 2.2918 - val_accuracy: 0.1118\n", + "Epoch 68/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3186 - accuracy: 0.1100 - val_loss: 2.2916 - val_accuracy: 0.1118\n", + "Epoch 69/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3176 - accuracy: 0.1119 - val_loss: 2.2915 - val_accuracy: 0.1118\n", + "Epoch 70/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3152 - accuracy: 0.1152 - val_loss: 2.2913 - val_accuracy: 0.1118\n", + "Epoch 71/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3168 - accuracy: 0.1159 - val_loss: 2.2911 - val_accuracy: 0.1118\n", + "Epoch 72/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3209 - accuracy: 0.1108 - val_loss: 2.2910 - val_accuracy: 0.1118\n", + "Epoch 73/100\n", + "434/434 [==============================] - 2s 4ms/step - loss: 2.3158 - accuracy: 0.1127 - val_loss: 2.2909 - val_accuracy: 0.1118\n", + "Epoch 74/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3131 - accuracy: 0.1127 - val_loss: 2.2907 - val_accuracy: 0.1118\n", + "Epoch 75/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3166 - accuracy: 0.1167 - val_loss: 2.2905 - val_accuracy: 0.1118\n", + "Epoch 76/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3162 - accuracy: 0.1133 - val_loss: 2.2903 - val_accuracy: 0.1118\n", + "Epoch 77/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3132 - accuracy: 0.1150 - val_loss: 2.2901 - val_accuracy: 0.1118\n", + "Epoch 78/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3127 - accuracy: 0.1167 - val_loss: 2.2900 - val_accuracy: 0.1118\n", + "Epoch 79/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3149 - accuracy: 0.1136 - val_loss: 2.2898 - val_accuracy: 0.1118\n", + "Epoch 80/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3140 - accuracy: 0.1131 - val_loss: 2.2896 - val_accuracy: 0.1118\n", + "Epoch 81/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3133 - accuracy: 0.1113 - val_loss: 2.2894 - val_accuracy: 0.1118\n", + "Epoch 82/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3131 - accuracy: 0.1133 - val_loss: 2.2893 - val_accuracy: 0.1118\n", + "Epoch 83/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3142 - accuracy: 0.1148 - val_loss: 2.2891 - val_accuracy: 0.1118\n", + "Epoch 84/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3103 - accuracy: 0.1179 - val_loss: 2.2889 - val_accuracy: 0.1118\n", + "Epoch 85/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3140 - accuracy: 0.1128 - val_loss: 2.2887 - val_accuracy: 0.1118\n", + "Epoch 86/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3108 - accuracy: 0.1171 - val_loss: 2.2885 - val_accuracy: 0.1118\n", + "Epoch 87/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3082 - accuracy: 0.1190 - val_loss: 2.2883 - val_accuracy: 0.1118\n", + "Epoch 88/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3106 - accuracy: 0.1136 - val_loss: 2.2880 - val_accuracy: 0.1118\n", + "Epoch 89/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3122 - accuracy: 0.1166 - val_loss: 2.2878 - val_accuracy: 0.1118\n", + "Epoch 90/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3113 - accuracy: 0.1135 - val_loss: 2.2876 - val_accuracy: 0.1118\n", + "Epoch 91/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3136 - accuracy: 0.1135 - val_loss: 2.2875 - val_accuracy: 0.1118\n", + "Epoch 92/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3100 - accuracy: 0.1115 - val_loss: 2.2873 - val_accuracy: 0.1118\n", + "Epoch 93/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3104 - accuracy: 0.1133 - val_loss: 2.2871 - val_accuracy: 0.1118\n", + "Epoch 94/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3104 - accuracy: 0.1159 - val_loss: 2.2868 - val_accuracy: 0.1118\n", + "Epoch 95/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3074 - accuracy: 0.1177 - val_loss: 2.2866 - val_accuracy: 0.1118\n", + "Epoch 96/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3071 - accuracy: 0.1183 - val_loss: 2.2864 - val_accuracy: 0.1118\n", + "Epoch 97/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3096 - accuracy: 0.1189 - val_loss: 2.2862 - val_accuracy: 0.1118\n", + "Epoch 98/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3080 - accuracy: 0.1170 - val_loss: 2.2859 - val_accuracy: 0.1118\n", + "Epoch 99/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3064 - accuracy: 0.1169 - val_loss: 2.2857 - val_accuracy: 0.1118\n", + "Epoch 100/100\n", + "434/434 [==============================] - 1s 3ms/step - loss: 2.3071 - accuracy: 0.1137 - val_loss: 2.2854 - val_accuracy: 0.1118\n" + ] + } + ], + "source": [ + "model = mlp_model()\n", + "history = model.fit(X_train, y_train, validation_split = 0.3, epochs = 100, verbose = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.legend(['training', 'validation'], loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Validation results does not improve since it did not show signs of overfitting, yet.\n", + "
Hence, the key takeaway message is that apply dropout when you see a signal of overfitting." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 1s 2ms/step - loss: 2.2844 - accuracy: 0.1135\n" + ] + } + ], + "source": [ + "results = model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.11349999904632568\n" + ] + } + ], + "source": [ + "print('Test accuracy: ', results[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=model.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Model Ensemble\n", + "- Model ensemble is a reliable and promising way to boost performance of the model\n", + "- Usually create 8 to 10 independent networks and merge their results\n", + "- Here, we resort to scikit-learn API, **VotingClassifier**\n", + "- Doc: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n", + "from sklearn.ensemble import VotingClassifier\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "y_train = np.argmax(y_train, axis = 1)\n", + "y_test = np.argmax(y_test, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "def mlp_model():\n", + " model = Sequential()\n", + " \n", + " model.add(Dense(50, input_shape = (784, )))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(50))\n", + " model.add(Activation('sigmoid')) \n", + " model.add(Dense(10))\n", + " model.add(Activation('softmax'))\n", + " \n", + " sgd = optimizers.SGD(lr = 0.001)\n", + " model.compile(optimizer = sgd, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "model1 = KerasClassifier(build_fn = mlp_model, epochs = 100, verbose = 0)\n", + "model1._estimator_type = \"classifier\"\n", + "model2 = KerasClassifier(build_fn = mlp_model, epochs = 100, verbose = 0)\n", + "model2._estimator_type = \"classifier\"\n", + "model3 = KerasClassifier(build_fn = mlp_model, epochs = 100, verbose = 0)\n", + "model3._estimator_type = \"classifier\"" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "ensemble_clf = VotingClassifier(estimators = [('model1', model1), ('model2', model2), ('model3', model3)], voting = 'soft')" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('model1',\n", + " ),\n", + " ('model2',\n", + " ),\n", + " ('model3',\n", + " )],\n", + " voting='soft')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ensemble_clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\Aeon\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\wrappers\\scikit_learn.py:264: Sequential.predict_proba (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n", + "Instructions for updating:\n", + "Please use `model.predict()` instead.\n" + ] + } + ], + "source": [ + "y_pred = ensemble_clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.276\n" + ] + } + ], + "source": [ + "print('Test accuracy:', accuracy_score(y_pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[8.00000000e-01 1.98979592e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 1.02040816e-03 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [0.00000000e+00 1.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [6.78294574e-03 9.91279070e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.93798450e-03\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [9.90099010e-03 9.89108911e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 9.90099010e-04\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [0.00000000e+00 6.27291242e-01 0.00000000e+00 0.00000000e+00\n", + " 1.01832994e-03 0.00000000e+00 2.03665988e-03 3.69653768e-01\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [9.64125561e-02 8.99103139e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.48430493e-03\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [1.35699374e-02 7.66179541e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 2.18162839e-01 2.08768267e-03\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [0.00000000e+00 3.86186770e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.13813230e-01\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [1.33470226e-02 9.72279261e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.43737166e-02\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [1.98216056e-03 3.96432111e-01 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.01585728e-01\n", + " 0.00000000e+00 0.00000000e+00]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=ensemble_clf.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Slight boost in the test accuracy from the outset **(0.2144 => 0.3045)**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "|Model | Naive Model | He normal | Relu | Adam | Batchnorm | Dropout | Ensemble |\n", + "|----------------|-------------|------------|-------------|-------------|------------|-----------|------------|\n", + "|Test Accuracy | 0.2144 | 0.4105 | 0.9208 | 0.9248 | 0.9154 | 0.1135 | 0.3045 |\n", + "\n", + "
\n", + "It turns out that most methods improve the model training & test performance.\n", + "Why don't try them out altogether?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Putting it altogether" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from sklearn.ensemble import VotingClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.datasets import mnist\n", + "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n", + "from tensorflow.keras.datasets import mnist\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.utils import to_categorical\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Activation, Dense, BatchNormalization, Dropout\n", + "from tensorflow.keras import optimizers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# reshaping X data: (n, 28, 28) => (n, 784)\n", + "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1] * X_train.shape[2]))\n", + "X_test = X_test.reshape((X_test.shape[0], X_test.shape[1] * X_test.shape[2]))" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 784)\n", + "(10000, 784)\n", + "(60000,)\n", + "(10000,)\n" + ] + } + ], + "source": [ + "# We use all training data and validate on all test data\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training & Validating Model\n", + "- Measures to improve training is applied simultaneously\n", + " - More training set\n", + " - Weight Initialization scheme\n", + " - Nonlinearity (Activation function)\n", + " - Optimizers: adaptvie\n", + " - Batch Normalization\n", + " - Dropout (Regularization)\n", + " - Model Ensemble" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def mlp_model():\n", + " model = Sequential()\n", + " \n", + " model.add(Dense(50, input_shape = (784, ), kernel_initializer='he_normal'))\n", + " model.add(BatchNormalization())\n", + " model.add(Activation('relu'))\n", + " model.add(Dropout(0.2))\n", + " model.add(Dense(50, kernel_initializer='he_normal'))\n", + " model.add(BatchNormalization())\n", + " model.add(Activation('relu')) \n", + " model.add(Dropout(0.2))\n", + " model.add(Dense(50, kernel_initializer='he_normal'))\n", + " model.add(BatchNormalization())\n", + " model.add(Activation('relu'))\n", + " model.add(Dropout(0.2))\n", + " model.add(Dense(50, kernel_initializer='he_normal'))\n", + " model.add(BatchNormalization())\n", + " model.add(Activation('relu'))\n", + " model.add(Dropout(0.2))\n", + " model.add(Dense(10, kernel_initializer='he_normal'))\n", + " model.add(Activation('softmax'))\n", + " \n", + " adam = optimizers.Adam(lr = 0.001)\n", + " model.compile(optimizer = adam, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "# create 5 models to ensemble\n", + "model1 = KerasClassifier(build_fn = mlp_model, epochs = 100)\n", + "model1._estimator_type = \"classifier\"\n", + "model2 = KerasClassifier(build_fn = mlp_model, epochs = 100)\n", + "model2._estimator_type = \"classifier\"\n", + "model3 = KerasClassifier(build_fn = mlp_model, epochs = 100)\n", + "model3._estimator_type = \"classifier\"\n", + "model4 = KerasClassifier(build_fn = mlp_model, epochs = 100)\n", + "model4._estimator_type = \"classifier\"\n", + "model5 = KerasClassifier(build_fn = mlp_model, epochs = 100)\n", + "model5._estimator_type = \"classifier\"" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "ensemble_clf = VotingClassifier(estimators = [('model1', model1), ('model2', model2), ('model3', model3), ('model4', model4), ('model5', model5)], voting = 'soft')" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7718 - accuracy: 0.7637\n", + "Epoch 2/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4351 - accuracy: 0.8747\n", + "Epoch 3/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3609 - accuracy: 0.8984\n", + "Epoch 4/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3160 - accuracy: 0.9107\n", + "Epoch 5/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2935 - accuracy: 0.9173\n", + "Epoch 6/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2726 - accuracy: 0.9219\n", + "Epoch 7/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2638 - accuracy: 0.9257\n", + "Epoch 8/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2552 - accuracy: 0.9285\n", + "Epoch 9/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2505 - accuracy: 0.9288\n", + "Epoch 10/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2414 - accuracy: 0.9323\n", + "Epoch 11/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2314 - accuracy: 0.9342\n", + "Epoch 12/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2267 - accuracy: 0.9355\n", + "Epoch 13/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2228 - accuracy: 0.9356\n", + "Epoch 14/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2216 - accuracy: 0.9360\n", + "Epoch 15/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2171 - accuracy: 0.9393\n", + "Epoch 16/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2095 - accuracy: 0.9402\n", + "Epoch 17/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2075 - accuracy: 0.9402\n", + "Epoch 18/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2074 - accuracy: 0.9410\n", + "Epoch 19/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2006 - accuracy: 0.9426\n", + "Epoch 20/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1973 - accuracy: 0.9438\n", + "Epoch 21/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1939 - accuracy: 0.9438\n", + "Epoch 22/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1955 - accuracy: 0.9428\n", + "Epoch 23/100\n", + "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1896 - accuracy: 0.9463\n", + "Epoch 24/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1935 - accuracy: 0.9449\n", + "Epoch 25/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1859 - accuracy: 0.9467\n", + "Epoch 26/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1879 - accuracy: 0.9456\n", + "Epoch 27/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1870 - accuracy: 0.9468\n", + "Epoch 28/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1817 - accuracy: 0.9478\n", + "Epoch 29/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1858 - accuracy: 0.9471\n", + "Epoch 30/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1806 - accuracy: 0.9475\n", + "Epoch 31/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1804 - accuracy: 0.9477\n", + "Epoch 32/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1803 - accuracy: 0.9482\n", + "Epoch 33/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1763 - accuracy: 0.9490\n", + "Epoch 34/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1740 - accuracy: 0.9498\n", + "Epoch 35/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1758 - accuracy: 0.9492\n", + "Epoch 36/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1743 - accuracy: 0.9492\n", + "Epoch 37/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1692 - accuracy: 0.9508\n", + "Epoch 38/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1754 - accuracy: 0.9492\n", + "Epoch 39/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1741 - accuracy: 0.9494\n", + "Epoch 40/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1672 - accuracy: 0.9515\n", + "Epoch 41/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1655 - accuracy: 0.9520\n", + "Epoch 42/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1662 - accuracy: 0.9521\n", + "Epoch 43/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1687 - accuracy: 0.9507\n", + "Epoch 44/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1643 - accuracy: 0.9520\n", + "Epoch 45/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1671 - accuracy: 0.9512\n", + "Epoch 46/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1638 - accuracy: 0.9528\n", + "Epoch 47/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1657 - accuracy: 0.9518\n", + "Epoch 48/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1688 - accuracy: 0.9514\n", + "Epoch 49/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1678 - accuracy: 0.9515\n", + "Epoch 50/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1625 - accuracy: 0.9524\n", + "Epoch 51/100\n", + "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1607 - accuracy: 0.9534\n", + "Epoch 52/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1581 - accuracy: 0.9543\n", + "Epoch 53/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1653 - accuracy: 0.9524\n", + "Epoch 54/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1611 - accuracy: 0.9536\n", + "Epoch 55/100\n", + "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1615 - accuracy: 0.9528\n", + "Epoch 56/100\n", + "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1570 - accuracy: 0.9545\n", + "Epoch 57/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1635 - accuracy: 0.9523\n", + "Epoch 58/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1552 - accuracy: 0.9547\n", + "Epoch 59/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1561 - accuracy: 0.9559\n", + "Epoch 60/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1595 - accuracy: 0.9541\n", + "Epoch 61/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1564 - accuracy: 0.9546\n", + "Epoch 62/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1559 - accuracy: 0.9542\n", + "Epoch 63/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1556 - accuracy: 0.9551\n", + "Epoch 64/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1549 - accuracy: 0.9549\n", + "Epoch 65/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1542 - accuracy: 0.9563\n", + "Epoch 66/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1523 - accuracy: 0.9566\n", + "Epoch 67/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1513 - accuracy: 0.9567\n", + "Epoch 68/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1584 - accuracy: 0.9541\n", + "Epoch 69/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1537 - accuracy: 0.9552\n", + "Epoch 70/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1529 - accuracy: 0.9549\n", + "Epoch 71/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1518 - accuracy: 0.9554\n", + "Epoch 72/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1518 - accuracy: 0.9549\n", + "Epoch 73/100\n", + "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1491 - accuracy: 0.9568\n", + "Epoch 74/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1536 - accuracy: 0.9553\n", + "Epoch 75/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1470 - accuracy: 0.9569\n", + "Epoch 76/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1511 - accuracy: 0.9558\n", + "Epoch 77/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1488 - accuracy: 0.9562\n", + "Epoch 78/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1508 - accuracy: 0.9565\n", + "Epoch 79/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1458 - accuracy: 0.9572\n", + "Epoch 80/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1485 - accuracy: 0.9563\n", + "Epoch 81/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1473 - accuracy: 0.9574\n", + "Epoch 82/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1466 - accuracy: 0.9578\n", + "Epoch 83/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1482 - accuracy: 0.9575\n", + "Epoch 84/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1487 - accuracy: 0.9564\n", + "Epoch 85/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1433 - accuracy: 0.9584\n", + "Epoch 86/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1493 - accuracy: 0.9561\n", + "Epoch 87/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1452 - accuracy: 0.9580\n", + "Epoch 88/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1458 - accuracy: 0.9569\n", + "Epoch 89/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1449 - accuracy: 0.9570\n", + "Epoch 90/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1486 - accuracy: 0.9575\n", + "Epoch 91/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1451 - accuracy: 0.9575\n", + "Epoch 92/100\n", + "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1448 - accuracy: 0.9579\n", + "Epoch 93/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1451 - accuracy: 0.9574\n", + "Epoch 94/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1425 - accuracy: 0.9596\n", + "Epoch 95/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1454 - accuracy: 0.9565\n", + "Epoch 96/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1416 - accuracy: 0.9581\n", + "Epoch 97/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1455 - accuracy: 0.9581\n", + "Epoch 98/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1411 - accuracy: 0.9583\n", + "Epoch 99/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1427 - accuracy: 0.9577\n", + "Epoch 100/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1391 - accuracy: 0.9594\n", + "Epoch 1/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.7638 - accuracy: 0.7666\n", + "Epoch 2/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4399 - accuracy: 0.8748\n", + "Epoch 3/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3683 - accuracy: 0.8952\n", + "Epoch 4/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3257 - accuracy: 0.9064\n", + "Epoch 5/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2972 - accuracy: 0.9147\n", + "Epoch 6/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2836 - accuracy: 0.9205\n", + "Epoch 7/100\n", + "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2672 - accuracy: 0.9245\n", + "Epoch 8/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2607 - accuracy: 0.9268\n", + "Epoch 9/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2470 - accuracy: 0.9302\n", + "Epoch 10/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2409 - accuracy: 0.9318\n", + "Epoch 11/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2301 - accuracy: 0.9350\n", + "Epoch 12/100\n", + "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2262 - accuracy: 0.9356\n", + "Epoch 13/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2242 - accuracy: 0.9368\n", + "Epoch 14/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.2189 - accuracy: 0.9373\n", + "Epoch 15/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2173 - accuracy: 0.9379\n", + "Epoch 16/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2137 - accuracy: 0.9396\n", + "Epoch 17/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2066 - accuracy: 0.9408\n", + "Epoch 18/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2046 - accuracy: 0.9410\n", + "Epoch 19/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2002 - accuracy: 0.9428\n", + "Epoch 20/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1962 - accuracy: 0.9441\n", + "Epoch 21/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1954 - accuracy: 0.9440\n", + "Epoch 22/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1925 - accuracy: 0.9451\n", + "Epoch 23/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1919 - accuracy: 0.9453\n", + "Epoch 24/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1849 - accuracy: 0.9475\n", + "Epoch 25/100\n", + "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1918 - accuracy: 0.9452\n", + "Epoch 26/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1892 - accuracy: 0.9455\n", + "Epoch 27/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1852 - accuracy: 0.9468\n", + "Epoch 28/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1833 - accuracy: 0.9476\n", + "Epoch 29/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1843 - accuracy: 0.9462\n", + "Epoch 30/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1816 - accuracy: 0.9478\n", + "Epoch 31/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1799 - accuracy: 0.9481\n", + "Epoch 32/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1750 - accuracy: 0.9495\n", + "Epoch 33/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1745 - accuracy: 0.9503\n", + "Epoch 34/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1785 - accuracy: 0.9478\n", + "Epoch 35/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1733 - accuracy: 0.9503\n", + "Epoch 36/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1772 - accuracy: 0.9492\n", + "Epoch 37/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1728 - accuracy: 0.9501\n", + "Epoch 38/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1730 - accuracy: 0.9504\n", + "Epoch 39/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1730 - accuracy: 0.9503\n", + "Epoch 40/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1700 - accuracy: 0.9511\n", + "Epoch 41/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1719 - accuracy: 0.9503\n", + "Epoch 42/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1640 - accuracy: 0.9526\n", + "Epoch 43/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1669 - accuracy: 0.9509\n", + "Epoch 44/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1667 - accuracy: 0.9526\n", + "Epoch 45/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1664 - accuracy: 0.9521\n", + "Epoch 46/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1665 - accuracy: 0.9519\n", + "Epoch 47/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1693 - accuracy: 0.9515\n", + "Epoch 48/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1653 - accuracy: 0.9528\n", + "Epoch 49/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1646 - accuracy: 0.9528\n", + "Epoch 50/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1649 - accuracy: 0.9529\n", + "Epoch 51/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1660 - accuracy: 0.9523\n", + "Epoch 52/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1621 - accuracy: 0.9533\n", + "Epoch 53/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1630 - accuracy: 0.9521\n", + "Epoch 54/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1617 - accuracy: 0.9533\n", + "Epoch 55/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1613 - accuracy: 0.9531\n", + "Epoch 56/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1629 - accuracy: 0.9529\n", + "Epoch 57/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1563 - accuracy: 0.9545\n", + "Epoch 58/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1586 - accuracy: 0.9538\n", + "Epoch 59/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1574 - accuracy: 0.9545\n", + "Epoch 60/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1617 - accuracy: 0.9525\n", + "Epoch 61/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1576 - accuracy: 0.9544\n", + "Epoch 62/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1582 - accuracy: 0.9542\n", + "Epoch 63/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1554 - accuracy: 0.9556\n", + "Epoch 64/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1567 - accuracy: 0.9546\n", + "Epoch 65/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1565 - accuracy: 0.9554\n", + "Epoch 66/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1543 - accuracy: 0.9548\n", + "Epoch 67/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1561 - accuracy: 0.9550\n", + "Epoch 68/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1600 - accuracy: 0.9541\n", + "Epoch 69/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1512 - accuracy: 0.9559\n", + "Epoch 70/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1521 - accuracy: 0.9562\n", + "Epoch 71/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1547 - accuracy: 0.9549\n", + "Epoch 72/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1531 - accuracy: 0.9545\n", + "Epoch 73/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1471 - accuracy: 0.9566\n", + "Epoch 74/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1498 - accuracy: 0.9569\n", + "Epoch 75/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1514 - accuracy: 0.9565\n", + "Epoch 76/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1525 - accuracy: 0.9561\n", + "Epoch 77/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1513 - accuracy: 0.9555\n", + "Epoch 78/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1518 - accuracy: 0.9550\n", + "Epoch 79/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1518 - accuracy: 0.9554\n", + "Epoch 80/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1469 - accuracy: 0.9572\n", + "Epoch 81/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1473 - accuracy: 0.9571\n", + "Epoch 82/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1494 - accuracy: 0.9571\n", + "Epoch 83/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1451 - accuracy: 0.9575\n", + "Epoch 84/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1512 - accuracy: 0.9570\n", + "Epoch 85/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1484 - accuracy: 0.9565\n", + "Epoch 86/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1456 - accuracy: 0.9572\n", + "Epoch 87/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1494 - accuracy: 0.9568\n", + "Epoch 88/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1493 - accuracy: 0.9568\n", + "Epoch 89/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1480 - accuracy: 0.9565\n", + "Epoch 90/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1458 - accuracy: 0.9573\n", + "Epoch 91/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1424 - accuracy: 0.9581\n", + "Epoch 92/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1467 - accuracy: 0.9574\n", + "Epoch 93/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1499 - accuracy: 0.9562\n", + "Epoch 94/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1507 - accuracy: 0.9557\n", + "Epoch 95/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1446 - accuracy: 0.9580\n", + "Epoch 96/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1430 - accuracy: 0.9587\n", + "Epoch 97/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1426 - accuracy: 0.9593\n", + "Epoch 98/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1453 - accuracy: 0.9580\n", + "Epoch 99/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1439 - accuracy: 0.9575\n", + "Epoch 100/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1427 - accuracy: 0.9583\n", + "Epoch 1/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7670 - accuracy: 0.7670\n", + "Epoch 2/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4196 - accuracy: 0.8786\n", + "Epoch 3/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3609 - accuracy: 0.8970\n", + "Epoch 4/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3198 - accuracy: 0.9098\n", + "Epoch 5/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2941 - accuracy: 0.9155\n", + "Epoch 6/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2779 - accuracy: 0.9203\n", + "Epoch 7/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2641 - accuracy: 0.9252\n", + "Epoch 8/100\n", + "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2510 - accuracy: 0.9279\n", + "Epoch 9/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2483 - accuracy: 0.9296\n", + "Epoch 10/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2352 - accuracy: 0.9323\n", + "Epoch 11/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2325 - accuracy: 0.9323\n", + "Epoch 12/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2231 - accuracy: 0.9367\n", + "Epoch 13/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2216 - accuracy: 0.9357\n", + "Epoch 14/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2195 - accuracy: 0.9370\n", + "Epoch 15/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2139 - accuracy: 0.9387\n", + "Epoch 16/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2107 - accuracy: 0.9406\n", + "Epoch 17/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2048 - accuracy: 0.9420\n", + "Epoch 18/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2042 - accuracy: 0.9407\n", + "Epoch 19/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2021 - accuracy: 0.9426\n", + "Epoch 20/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1977 - accuracy: 0.9432\n", + "Epoch 21/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1954 - accuracy: 0.9433\n", + "Epoch 22/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1929 - accuracy: 0.9446\n", + "Epoch 23/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1955 - accuracy: 0.9434\n", + "Epoch 24/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1906 - accuracy: 0.9444\n", + "Epoch 25/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1900 - accuracy: 0.9456\n", + "Epoch 26/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1899 - accuracy: 0.9459\n", + "Epoch 27/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1863 - accuracy: 0.9463\n", + "Epoch 28/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1803 - accuracy: 0.9478\n", + "Epoch 29/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1807 - accuracy: 0.9485\n", + "Epoch 30/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1790 - accuracy: 0.9483\n", + "Epoch 31/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1791 - accuracy: 0.9476\n", + "Epoch 32/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1760 - accuracy: 0.9491\n", + "Epoch 33/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1789 - accuracy: 0.9480\n", + "Epoch 34/100\n", + "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1776 - accuracy: 0.9496\n", + "Epoch 35/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1795 - accuracy: 0.9483\n", + "Epoch 36/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1769 - accuracy: 0.9487\n", + "Epoch 37/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1717 - accuracy: 0.9502\n", + "Epoch 38/100\n", + "1875/1875 [==============================] - 8s 5ms/step - loss: 0.1724 - accuracy: 0.9501\n", + "Epoch 39/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1720 - accuracy: 0.9506\n", + "Epoch 40/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1701 - accuracy: 0.9508\n", + "Epoch 41/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1688 - accuracy: 0.9513\n", + "Epoch 42/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1683 - accuracy: 0.9509\n", + "Epoch 43/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1695 - accuracy: 0.9515\n", + "Epoch 44/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1687 - accuracy: 0.9518\n", + "Epoch 45/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1677 - accuracy: 0.9509\n", + "Epoch 46/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1600 - accuracy: 0.9538\n", + "Epoch 47/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1671 - accuracy: 0.9514\n", + "Epoch 48/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1655 - accuracy: 0.9513\n", + "Epoch 49/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1638 - accuracy: 0.9520\n", + "Epoch 50/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1580 - accuracy: 0.9542\n", + "Epoch 51/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1588 - accuracy: 0.9538\n", + "Epoch 52/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1651 - accuracy: 0.9513\n", + "Epoch 53/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1582 - accuracy: 0.9530\n", + "Epoch 54/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1615 - accuracy: 0.9533\n", + "Epoch 55/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1585 - accuracy: 0.9542\n", + "Epoch 56/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1616 - accuracy: 0.9535\n", + "Epoch 57/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1572 - accuracy: 0.9551\n", + "Epoch 58/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1591 - accuracy: 0.9544\n", + "Epoch 59/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1558 - accuracy: 0.9548\n", + "Epoch 60/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1557 - accuracy: 0.9556\n", + "Epoch 61/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1590 - accuracy: 0.9542\n", + "Epoch 62/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1555 - accuracy: 0.9542\n", + "Epoch 63/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1570 - accuracy: 0.9550\n", + "Epoch 64/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1562 - accuracy: 0.9552\n", + "Epoch 65/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1545 - accuracy: 0.9553\n", + "Epoch 66/100\n", + "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1543 - accuracy: 0.9554\n", + "Epoch 67/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1524 - accuracy: 0.9552\n", + "Epoch 68/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1545 - accuracy: 0.9562\n", + "Epoch 69/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1547 - accuracy: 0.9554\n", + "Epoch 70/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1538 - accuracy: 0.9554\n", + "Epoch 71/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1500 - accuracy: 0.9556\n", + "Epoch 72/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1506 - accuracy: 0.9568\n", + "Epoch 73/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1515 - accuracy: 0.9559\n", + "Epoch 74/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1515 - accuracy: 0.9569\n", + "Epoch 75/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1476 - accuracy: 0.9573\n", + "Epoch 76/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1500 - accuracy: 0.9561\n", + "Epoch 77/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1504 - accuracy: 0.9554\n", + "Epoch 78/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1504 - accuracy: 0.9555\n", + "Epoch 79/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1500 - accuracy: 0.9569\n", + "Epoch 80/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1511 - accuracy: 0.9563\n", + "Epoch 81/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1513 - accuracy: 0.9564\n", + "Epoch 82/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1515 - accuracy: 0.9563\n", + "Epoch 83/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1500 - accuracy: 0.9565\n", + "Epoch 84/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1436 - accuracy: 0.9580\n", + "Epoch 85/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1458 - accuracy: 0.9576\n", + "Epoch 86/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1448 - accuracy: 0.9576\n", + "Epoch 87/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1480 - accuracy: 0.9565\n", + "Epoch 88/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1484 - accuracy: 0.9570\n", + "Epoch 89/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1450 - accuracy: 0.9586\n", + "Epoch 90/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1421 - accuracy: 0.9581\n", + "Epoch 91/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1475 - accuracy: 0.9575\n", + "Epoch 92/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1462 - accuracy: 0.9575\n", + "Epoch 93/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1429 - accuracy: 0.9579\n", + "Epoch 94/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1424 - accuracy: 0.9583\n", + "Epoch 95/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1432 - accuracy: 0.9585\n", + "Epoch 96/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1445 - accuracy: 0.9580\n", + "Epoch 97/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1450 - accuracy: 0.9579\n", + "Epoch 98/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1451 - accuracy: 0.9577\n", + "Epoch 99/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1414 - accuracy: 0.9585\n", + "Epoch 100/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1455 - accuracy: 0.9577\n", + "Epoch 1/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.7666 - accuracy: 0.7677\n", + "Epoch 2/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.4348 - accuracy: 0.8763\n", + "Epoch 3/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3607 - accuracy: 0.8973\n", + "Epoch 4/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3194 - accuracy: 0.9097\n", + "Epoch 5/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2967 - accuracy: 0.9159\n", + "Epoch 6/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2792 - accuracy: 0.9202\n", + "Epoch 7/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2631 - accuracy: 0.9236\n", + "Epoch 8/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2571 - accuracy: 0.9262\n", + "Epoch 9/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2445 - accuracy: 0.9303\n", + "Epoch 10/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2381 - accuracy: 0.9317\n", + "Epoch 11/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2266 - accuracy: 0.9360\n", + "Epoch 12/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2255 - accuracy: 0.9355\n", + "Epoch 13/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2176 - accuracy: 0.9381\n", + "Epoch 14/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2155 - accuracy: 0.9385\n", + "Epoch 15/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2108 - accuracy: 0.9404\n", + "Epoch 16/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2086 - accuracy: 0.9402\n", + "Epoch 17/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2045 - accuracy: 0.9415\n", + "Epoch 18/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2042 - accuracy: 0.9421\n", + "Epoch 19/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1999 - accuracy: 0.9427\n", + "Epoch 20/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2021 - accuracy: 0.9423\n", + "Epoch 21/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1928 - accuracy: 0.9441\n", + "Epoch 22/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1936 - accuracy: 0.9434\n", + "Epoch 23/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1899 - accuracy: 0.9451\n", + "Epoch 24/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1887 - accuracy: 0.9459\n", + "Epoch 25/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1881 - accuracy: 0.9459\n", + "Epoch 26/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1833 - accuracy: 0.9463\n", + "Epoch 27/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1856 - accuracy: 0.9459\n", + "Epoch 28/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1816 - accuracy: 0.9474\n", + "Epoch 29/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1795 - accuracy: 0.9487\n", + "Epoch 30/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1805 - accuracy: 0.9484\n", + "Epoch 31/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1791 - accuracy: 0.9482\n", + "Epoch 32/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1764 - accuracy: 0.9490\n", + "Epoch 33/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1769 - accuracy: 0.9497\n", + "Epoch 34/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1758 - accuracy: 0.9487\n", + "Epoch 35/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1710 - accuracy: 0.9509\n", + "Epoch 36/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1713 - accuracy: 0.9505\n", + "Epoch 37/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1728 - accuracy: 0.9499\n", + "Epoch 38/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1685 - accuracy: 0.9505\n", + "Epoch 39/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1682 - accuracy: 0.9509\n", + "Epoch 40/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1646 - accuracy: 0.9518\n", + "Epoch 41/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1659 - accuracy: 0.9506\n", + "Epoch 42/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1606 - accuracy: 0.9525\n", + "Epoch 43/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1610 - accuracy: 0.9524\n", + "Epoch 44/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1653 - accuracy: 0.9520\n", + "Epoch 45/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1608 - accuracy: 0.9524\n", + "Epoch 46/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1677 - accuracy: 0.9516\n", + "Epoch 47/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1625 - accuracy: 0.9532\n", + "Epoch 48/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1643 - accuracy: 0.9529\n", + "Epoch 49/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1621 - accuracy: 0.9522\n", + "Epoch 50/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1576 - accuracy: 0.9546\n", + "Epoch 51/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1615 - accuracy: 0.9528\n", + "Epoch 52/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1621 - accuracy: 0.9522\n", + "Epoch 53/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1624 - accuracy: 0.9535\n", + "Epoch 54/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1593 - accuracy: 0.9538\n", + "Epoch 55/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1596 - accuracy: 0.9538\n", + "Epoch 56/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1555 - accuracy: 0.9548\n", + "Epoch 57/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1566 - accuracy: 0.9537\n", + "Epoch 58/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1541 - accuracy: 0.9550\n", + "Epoch 59/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1589 - accuracy: 0.9536\n", + "Epoch 60/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1521 - accuracy: 0.9562\n", + "Epoch 61/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1552 - accuracy: 0.9544\n", + "Epoch 62/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1577 - accuracy: 0.9543\n", + "Epoch 63/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1515 - accuracy: 0.9563\n", + "Epoch 64/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1543 - accuracy: 0.9555\n", + "Epoch 65/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1530 - accuracy: 0.9562\n", + "Epoch 66/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1513 - accuracy: 0.9563\n", + "Epoch 67/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1540 - accuracy: 0.9547\n", + "Epoch 68/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1515 - accuracy: 0.9563\n", + "Epoch 69/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1507 - accuracy: 0.9561\n", + "Epoch 70/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1490 - accuracy: 0.9562\n", + "Epoch 71/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1515 - accuracy: 0.9553\n", + "Epoch 72/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1513 - accuracy: 0.9550\n", + "Epoch 73/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1500 - accuracy: 0.9569\n", + "Epoch 74/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1514 - accuracy: 0.9557\n", + "Epoch 75/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1486 - accuracy: 0.9567\n", + "Epoch 76/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1474 - accuracy: 0.9569\n", + "Epoch 77/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1476 - accuracy: 0.9567\n", + "Epoch 78/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1507 - accuracy: 0.9566\n", + "Epoch 79/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1439 - accuracy: 0.9577\n", + "Epoch 80/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1480 - accuracy: 0.9572\n", + "Epoch 81/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1468 - accuracy: 0.9573\n", + "Epoch 82/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1468 - accuracy: 0.9569\n", + "Epoch 83/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1458 - accuracy: 0.9563\n", + "Epoch 84/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1511 - accuracy: 0.9563\n", + "Epoch 85/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1496 - accuracy: 0.9564\n", + "Epoch 86/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1425 - accuracy: 0.9585\n", + "Epoch 87/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1515 - accuracy: 0.9554\n", + "Epoch 88/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1406 - accuracy: 0.9577\n", + "Epoch 89/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1451 - accuracy: 0.9578\n", + "Epoch 90/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1418 - accuracy: 0.9582\n", + "Epoch 91/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1502 - accuracy: 0.9560\n", + "Epoch 92/100\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1477 - accuracy: 0.9568\n", + "Epoch 93/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1447 - accuracy: 0.9573\n", + "Epoch 94/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1416 - accuracy: 0.9591\n", + "Epoch 95/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1447 - accuracy: 0.9575\n", + "Epoch 96/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1392 - accuracy: 0.9593\n", + "Epoch 97/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1418 - accuracy: 0.9578\n", + "Epoch 98/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1432 - accuracy: 0.9582\n", + "Epoch 99/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1413 - accuracy: 0.9590\n", + "Epoch 100/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1415 - accuracy: 0.9584\n", + "Epoch 1/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7567 - accuracy: 0.7686\n", + "Epoch 2/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4217 - accuracy: 0.8794\n", + "Epoch 3/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3620 - accuracy: 0.8972\n", + "Epoch 4/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3199 - accuracy: 0.9092\n", + "Epoch 5/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2953 - accuracy: 0.9166\n", + "Epoch 6/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2800 - accuracy: 0.9215\n", + "Epoch 7/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2678 - accuracy: 0.9241\n", + "Epoch 8/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2558 - accuracy: 0.9262\n", + "Epoch 9/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2459 - accuracy: 0.9308\n", + "Epoch 10/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2358 - accuracy: 0.9330\n", + "Epoch 11/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2310 - accuracy: 0.9344\n", + "Epoch 12/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2282 - accuracy: 0.9353\n", + "Epoch 13/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2252 - accuracy: 0.9367\n", + "Epoch 14/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2211 - accuracy: 0.9370\n", + "Epoch 15/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2139 - accuracy: 0.9385\n", + "Epoch 16/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2116 - accuracy: 0.9387\n", + "Epoch 17/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2068 - accuracy: 0.9412\n", + "Epoch 18/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2018 - accuracy: 0.9422\n", + "Epoch 19/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1988 - accuracy: 0.9437\n", + "Epoch 20/100\n", + "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2020 - accuracy: 0.9431\n", + "Epoch 21/100\n", + "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2002 - accuracy: 0.9420\n", + "Epoch 22/100\n", + "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1985 - accuracy: 0.9427\n", + "Epoch 23/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1934 - accuracy: 0.9444\n", + "Epoch 24/100\n", + "1875/1875 [==============================] - 8s 5ms/step - loss: 0.1951 - accuracy: 0.9438\n", + "Epoch 25/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1882 - accuracy: 0.9457\n", + "Epoch 26/100\n", + "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1878 - accuracy: 0.9469\n", + "Epoch 27/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1865 - accuracy: 0.9454\n", + "Epoch 28/100\n", + "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1909 - accuracy: 0.9443\n", + "Epoch 29/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1849 - accuracy: 0.9466\n", + "Epoch 30/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1856 - accuracy: 0.9461\n", + "Epoch 31/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1829 - accuracy: 0.9478\n", + "Epoch 32/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1797 - accuracy: 0.9490\n", + "Epoch 33/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1797 - accuracy: 0.9488\n", + "Epoch 34/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1796 - accuracy: 0.9483\n", + "Epoch 35/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1759 - accuracy: 0.9489\n", + "Epoch 36/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1794 - accuracy: 0.9478\n", + "Epoch 37/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1738 - accuracy: 0.9497\n", + "Epoch 38/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1742 - accuracy: 0.9491\n", + "Epoch 39/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1735 - accuracy: 0.9503\n", + "Epoch 40/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1742 - accuracy: 0.9495\n", + "Epoch 41/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1737 - accuracy: 0.9489\n", + "Epoch 42/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1714 - accuracy: 0.9517\n", + "Epoch 43/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1733 - accuracy: 0.9507\n", + "Epoch 44/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1651 - accuracy: 0.9516\n", + "Epoch 45/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1658 - accuracy: 0.9530\n", + "Epoch 46/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1674 - accuracy: 0.9511\n", + "Epoch 47/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1662 - accuracy: 0.9523\n", + "Epoch 48/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1681 - accuracy: 0.9510\n", + "Epoch 49/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1672 - accuracy: 0.9518\n", + "Epoch 50/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1651 - accuracy: 0.9533\n", + "Epoch 51/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1621 - accuracy: 0.9520\n", + "Epoch 52/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1647 - accuracy: 0.9523\n", + "Epoch 53/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1648 - accuracy: 0.9521\n", + "Epoch 54/100\n", + "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1655 - accuracy: 0.9517\n", + "Epoch 55/100\n", + "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1662 - accuracy: 0.9527\n", + "Epoch 56/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1574 - accuracy: 0.9540\n", + "Epoch 57/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1624 - accuracy: 0.9531\n", + "Epoch 58/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1607 - accuracy: 0.9541\n", + "Epoch 59/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1608 - accuracy: 0.9526\n", + "Epoch 60/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1622 - accuracy: 0.9531\n", + "Epoch 61/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1578 - accuracy: 0.9541\n", + "Epoch 62/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1647 - accuracy: 0.9527\n", + "Epoch 63/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1543 - accuracy: 0.9555\n", + "Epoch 64/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1588 - accuracy: 0.9544\n", + "Epoch 65/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1552 - accuracy: 0.9550\n", + "Epoch 66/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1587 - accuracy: 0.9538\n", + "Epoch 67/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1586 - accuracy: 0.9543\n", + "Epoch 68/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1551 - accuracy: 0.9549\n", + "Epoch 69/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1577 - accuracy: 0.9542\n", + "Epoch 70/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1573 - accuracy: 0.9538\n", + "Epoch 71/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1562 - accuracy: 0.9544\n", + "Epoch 72/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1560 - accuracy: 0.9545\n", + "Epoch 73/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1533 - accuracy: 0.9557\n", + "Epoch 74/100\n", + "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1572 - accuracy: 0.9540\n", + "Epoch 75/100\n", + "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1545 - accuracy: 0.9556\n", + "Epoch 76/100\n", + "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1540 - accuracy: 0.9557\n", + "Epoch 77/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1506 - accuracy: 0.9559\n", + "Epoch 78/100\n", + "1875/1875 [==============================] - 8s 5ms/step - loss: 0.1480 - accuracy: 0.9564\n", + "Epoch 79/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1570 - accuracy: 0.9549\n", + "Epoch 80/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1528 - accuracy: 0.9561\n", + "Epoch 81/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1516 - accuracy: 0.9553\n", + "Epoch 82/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1543 - accuracy: 0.9542\n", + "Epoch 83/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1505 - accuracy: 0.9565: 0s - loss: 0.1507 - \n", + "Epoch 84/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1502 - accuracy: 0.9566\n", + "Epoch 85/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1557 - accuracy: 0.9545\n", + "Epoch 86/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1485 - accuracy: 0.9558\n", + "Epoch 87/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1513 - accuracy: 0.9559\n", + "Epoch 88/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1472 - accuracy: 0.9576\n", + "Epoch 89/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1476 - accuracy: 0.9556\n", + "Epoch 90/100\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1474 - accuracy: 0.9558\n", + "Epoch 91/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1516 - accuracy: 0.9555\n", + "Epoch 92/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1484 - accuracy: 0.9565\n", + "Epoch 93/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1500 - accuracy: 0.9566\n", + "Epoch 94/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1506 - accuracy: 0.9563\n", + "Epoch 95/100\n", + "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1502 - accuracy: 0.9571\n", + "Epoch 96/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1474 - accuracy: 0.9570\n", + "Epoch 97/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1471 - accuracy: 0.9567\n", + "Epoch 98/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1442 - accuracy: 0.9582\n", + "Epoch 99/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1488 - accuracy: 0.9573\n", + "Epoch 100/100\n", + "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1460 - accuracy: 0.9568\n" + ] + }, + { + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('model1',\n", + " ),\n", + " ('model2',\n", + " ),\n", + " ('model3',\n", + " ),\n", + " ('model4',\n", + " ),\n", + " ('model5',\n", + " )],\n", + " voting='soft')" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ensemble_clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = ensemble_clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Acc: 0.98\n" + ] + } + ], + "source": [ + "print('Acc: ', accuracy_score(y_pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Confusion Matrix\n", + "[[9.91836735e-01 1.02040816e-03 2.04081633e-03 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 2.04081633e-03 1.02040816e-03\n", + " 2.04081633e-03 0.00000000e+00]\n", + " [0.00000000e+00 9.91189427e-01 2.64317181e-03 8.81057269e-04\n", + " 0.00000000e+00 0.00000000e+00 1.76211454e-03 8.81057269e-04\n", + " 2.64317181e-03 0.00000000e+00]\n", + " [1.93798450e-03 0.00000000e+00 9.82558140e-01 1.93798450e-03\n", + " 1.93798450e-03 0.00000000e+00 0.00000000e+00 7.75193798e-03\n", + " 3.87596899e-03 0.00000000e+00]\n", + " [0.00000000e+00 0.00000000e+00 2.97029703e-03 9.85148515e-01\n", + " 0.00000000e+00 2.97029703e-03 0.00000000e+00 5.94059406e-03\n", + " 2.97029703e-03 0.00000000e+00]\n", + " [1.01832994e-03 0.00000000e+00 3.05498982e-03 0.00000000e+00\n", + " 9.76578411e-01 0.00000000e+00 5.09164969e-03 3.05498982e-03\n", + " 2.03665988e-03 9.16496945e-03]\n", + " [2.24215247e-03 0.00000000e+00 0.00000000e+00 7.84753363e-03\n", + " 0.00000000e+00 9.76457399e-01 5.60538117e-03 2.24215247e-03\n", + " 3.36322870e-03 2.24215247e-03]\n", + " [5.21920668e-03 3.13152401e-03 1.04384134e-03 0.00000000e+00\n", + " 2.08768267e-03 4.17536534e-03 9.84342380e-01 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00]\n", + " [1.94552529e-03 7.78210117e-03 8.75486381e-03 1.94552529e-03\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 9.73735409e-01\n", + " 0.00000000e+00 5.83657588e-03]\n", + " [7.18685832e-03 3.08008214e-03 3.08008214e-03 2.05338809e-03\n", + " 5.13347023e-03 4.10677618e-03 1.02669405e-03 5.13347023e-03\n", + " 9.67145791e-01 2.05338809e-03]\n", + " [2.97324083e-03 2.97324083e-03 0.00000000e+00 7.92864222e-03\n", + " 7.92864222e-03 9.91080278e-04 9.91080278e-04 5.94648167e-03\n", + " 9.91080278e-04 9.69276511e-01]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(y_true=y_test, y_pred=ensemble_clf.predict(X_test), classes=np.array(range(10)), normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Final model produces over **98% of accuracy**, which is rather improved from before" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 30ba3f24f89cca82c71bb5e98ac60af80663eecb Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Sun, 6 Sep 2020 19:56:00 +0200 Subject: [PATCH 11/13] Add files via upload A short tutorial on CNN and DenseNet --- .../Convolutional_neural_networks.ipynb | 504 ++++++ .../Densely_connected_networks_ipy.ipynb | 1417 +++++++++++++++++ 2 files changed, 1921 insertions(+) create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/Convolutional_neural_networks.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/Densely_connected_networks_ipy.ipynb diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Convolutional_neural_networks.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Convolutional_neural_networks.ipynb new file mode 100644 index 00000000..b97ec6ec --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Convolutional_neural_networks.ipynb @@ -0,0 +1,504 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "e80sv5tuZl2J" + }, + "source": [ + "# Chapter 6: Convolutional Neural Networks\n", + "\n", + "(by Miguel Tomás : mtpsilva@gmail.com)\n", + "\n", + "Deep Learning is becoming a very popular subset of machine learning due to his high performance results achieved on a wide range of datasets types. One known way to use this ML technique in particular, is to classify images and photos. The algorithm is known as Convolutional Neural Network (CNN) and is available on the Keras library in Python with all its simplicity for building neural networks.\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "The computer uses pixels to view images. The pixels in an image are usually related. For example, a certain set of pixels can represent edges in an image or some other pattern. Convolution Neural Networks can be used to help identify patterns, objects and even people in images and photos.\n", + "\n", + "Convolution multiplies the pixel matrix with the filter matrix or “kernel” and then adds the multiplied values. Then, the process of convolution slides to the next pixel and repeats the same process until all image pixels are covered. This process is shown further bellow.\n", + "\n", + "The purpose of computer vision is to enable machines, computers or programs to observe the world like humans and apply knowledge to specific tasks, such as image and video recognition and image classification. With the development of deep learning algorithms (more specifically, convolutional neural networks), this has become possible nowadays. And affordable to everyone.\n", + "\n", + "The architecture of the Convolutional networks are the connectivity pattern of neurons in the animal brain, and are inspired by biological processes, the connectivity pattern between neurons resembles the organization of the animal visual cortex.\n", + "Convolutional neural networks have the ability to capture spatial and temporal correlations in input images by applying correlation filters. Due to the reduced number of parameters involved and the reusability of weights, it performs better on image data sets.\n", + "\n", + "## 1. How does it work?\n", + "Convolutional neural networks (CNNs) do not view images like we humans. We treat the image as a flat canvas with colors. We may not care about the width and height of the image, but we can perceive these parameters.\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "However, CNNs will view the image into different three-dimensional objects, such as three-dimensional objects where the three-dimensions are the color coding (or color channel), mainly red, green and blue (RGB), when combined, produce the colors we see.\n", + "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAIbCAYAAAAJo00VAAAgAElEQVR4AeydB3hV15W2PTUzmZk/0/tMZjIlyUy6Y8fYFHWJbgOmGTAudNQL4BZ3e1zjCkKA7cQ17gVj05GEhOimuyYuiW1MR+X273/efe66OlxLdBfg7OfZ7HN23+tcdN+z7tprn6YgBBIIJBBIIJBAIIFAAoEEAgkEEjiJJXDaSby2YGmBBAIJBBIIJBBIIJBAIIFAAoEEFABv8CEIJBBIIJBAIIFAAoEEAgkEEjipJRAA70n9eIPFBRIIJBBIIJBAIIFAAoEEAgkEwBt8BgIJBBIIJBBIIJBAIIFAAoEETmoJBMB7Uj/eYHGBBAIJBBIIJBBIIJBAIIFAAgHwBp+BQAKBBAIJBBIIJBBIIJBAIIGTWgIB8J7UjzdYXCCBQAKBBAIJBBIIJBBIIJBAALzBZyCQQCCBQAKBBAIJBBL4UkggkUjI4pdiQsEkThoJBMB70jzKYCGBBAIJHG8J2BdvZ+nxHi/o7/AlwDM5VYN9Hk/W9Z/s6ztZn9uXfV0B8H7Zn1Awv0ACgQQCCQQSOEACpzIQ2dpJv+zB5po+z87y/fVOhPX55xtcf/klEADvl/8ZBTMMJBBIIJDAKSmBg0HPwcpOSWEFiw4kEEjgoBIIgPeg4gkKAwkEEggkEEjgi5KAaQI7g9tDlX9R8w7GDSQQSODLJ4EAeL98zySYUSCBQAKBBAIJSKnNS4cDvJ3VCQT55ZVA8My+vM/mZJxZALwn41MN1hRI4ASRgF9D19H1oZbRUZsjyTtU/0H5l08C9nzj8fgBQEz+iRZsLUeSnmhrZL6sj+dlz8zWYOu2+yANJPBZSiAA3s9SukHfgQQCCRxcAvGElIx8+XFtX4Lu/uCtFY9HFUsQ4+0xLsWTkS69eGC/qTFicSWS0ftCjro+6ZcYiYQUJYYjLkYiEUUisVQ8xPSC4uMsAZ4bzykWi7mYeo5JV1bHebjPvDuDwMNNT8T1pj8z1koewb+ez1zYwQCnvAQC4D3lPwKBAAIJfEES4DvPooFvLK54NKZ4LOKiEjG5qLhEtPru+zKuuCKKKuL+DcdjCscTCselaMyLdBvjPhp3MRaJi+i6o4+opHBUiUjUjReNtakttF/h0H7FY22KRlqlaEiR1haFW1pd22hEioS9ucdiSWC3eSW/xDuTqFWztLN6X8Z8A5d0WIlGEWJ7oDwcRkDHL9gYfjCk9+bmZjdIS0tLCoCBYZvjoWbgr8d1+v2h2h+qnPn6A3PzXpoiYk02ptVDbv75G9gbJPrnR7/p9/6xjuT6SPpJr8t9ep5/bMp2797t1s21fTaQAzKwtfvbBNeBBD4LCQTA+1lINegzkEAggUNLwKivozQFuGnAa2DsNMExJRRVVGFFFVNUCVMWp7jYVY8rCRH0BTcnHFRHQmEpknBk7CA77nqgFxdtAWiAY6GwQs0tikcT2r+vzUH0/uawA2DKUwMeAkLSl2pjfFlTAMWgq7M5GrBQLz3Q/mAxvX76PVDkD6FQKHW7f/9+7du3z90zB+DJYIpMrhnbHzqai5Vbmf/eri21Oh2lVqejlPrMERkxT4Ne8igjz782yoF4yq0O9dJDR/OwvPS6nd1b/1bub2/XHaUd1SfP6nJN36yDtfE8iOSxXiLyoL4Fa2up5QdpIIHjIYEAeI+HFIM+AgkEEjg6CaDBNbg1Da6vJ/viszRFtAa+1pbvTLgThRrXye9Q2nn9A05tCkeaFQrvVzwBOHmAG0nE1RoJqy0eVUtc2huOaXdrQvvbnPJXKDDR6u7dgxYRcJHCkZibCtAMLLub5Ji+6X/q0qZm6acqnAAZQArQYsFAF4Dxh/R7f9mRXBsk0cYPStwDUq2trWpra3NdUk7e8Rr7SOZ5sLr2+UVuRGRGZG3M1WTqfV7be6KutW3P/fTV4dT5dCsvJ31Mfz3r92B1/PU7u+b5GNBz7dfuHmvfnY0Z5AcSSJdAALzpEgnuAwkEEvgcJZA0VUCrmvC0Pf4vWQME++JPgaWDS5+xLqYFznZBTmvrWULEFY+FnGlCLN6ihICikBIuRhSKt2l3qE17ozHtl7QnIe2RhM5wf0LaF5be/1h6bcvHeuChp/XS3Pl659e/UjQeUWuI/uIKt4UUi0RPWuBF7gZk6R8KA11LrZw26eFYoCb17H2dYsqwY8cONzegEZhiDK4tpM/L8r+o1D7XrIe5EZGtyXj79u1uamitTbtrbQ4lv0OVfxZrPpwxWSPPhDXxjPbu3as9e/hfJge9lHf0fD+L+QZ9BhIIgDf4DAQSCCTwBUoA8wE0gx74tn/Be5vNTIF7gGbXYBd1bjwqxYgY1kalcIxvUqeSBXY9I92IEgJwW9UWa1Uo7mlz98djwgIUwP0kIX0Qkn4Tkd7cLT275H1dccvzumjSnRo15loNGDpe8xbWan+o2fVFf23hZoVD2PWevMCb/sEAUNJBB2iz50Z97v3BytLb+fM7u2Y8C/5rIImAdpdAe/LQHhpAWR0rI+0o2NhWln5v+R2lVrez1OZibVkD2k0g0OCc1LSf9GPBYNj6tnx/amX+dlbeUZ6Vpaf+fjprZ3Wsra3N8jtKqWufB9Pskgf4Whkysb5cZvKfzubhrxNcBxI4EgkEwHsk0grqBhIIJHBcJeDZy7bbzn6qc77/LWK3i0cFIBcVbjwixcJSrM1tLFMklIJd6uBlIRwNic1soUTMmSu0AEmSSHfHpXf2Slv2SMve/Ej3PLNMo6bdqx7DrlLXAdcob+gtyuhzhX7UdbRuuO0B/frDnW6DXHPbLrVF9ioaa1Uk3HZSmzQYdAEkRH8wkCEPiDMgpZ5BjL/+0V4bSFl7GxdIfP/99x3sYs+L1pd5dBS+LPCEbJi/Qa6BLnPeuXOnA3jKkZ+lJvuO1tBRXkfrP5y8dDmntzlUeXp97mlj8+fZ8IxsgyHrsz5J00NHeel1gvtAAkcigQB4j0RaQd1AAoEEjqsEbIOYMS2duy86s9GlwAe6Mbw3JMIuKhGWEi1SYr8Ub3bgi1aXOs5bQwIDBg9w0eIS+TH1g2apaVubnln2nipvf1b9Cm/TD/pP1vfPLVW3kTcq99J71OuSWTr30gfUd+RdGjDqRjWsfVf7o05P7HpsC+9SItGqWDTsIDwF5Tb/TqRk67S0k2pf6mwDGCZpXhLI8weDlY7yybNy0oNF65M2BkjkAYoPPfSQ/v7v/15z585NwbYfvKlvwcbz31u5je8vS69vZUebMhaRddhagF7Alp/477jjDv3FX/yF3nrrLe3atSu1HtpQx14g0udFX/5g/fvzDnad3t7qMo6Na3k27/Q5WHl6avVsTm+//bb69Omj/Px8Z45CuT921j49P7gPJHC0EgiA92glF7QLJBBI4JglYODnUtMGRWPOTABXYfgXIwUso1EAIaIYwCvPTMGzyW1TTCGFExG1xeNqjkn74tKeuPRJVNoR98wUXlzxG10zfaGGFM1Q1wHX6bv5l+nMQTerx+h7lDuhWnkTZ6pX0c81oOxJ9Zv4pPJH1KjHudfr7jn1+s0uzziiNbxPkQjY3Kp4pMVznQac20JOQuAFWH71q1/prrvu0ve+9z2ddtpp+pu/+RsHL6tXr3bwCfgCoZgYADH2cz15BnoAlGk2AVP6JT1Y5ANGH9QlAIfUZ4yamhr9/u//vl5++eUUDFNmYxuwGWwaNHJPn2gaubbxrZ7/nnr0Rx7l9GGQxnwM5mys9JS69OHXQJNHPf+67r33Xv3O7/yO1q1blzLToI5/XMairT/4y5knZgNE2vrnZ/NMT83MIH3e9MuzJLJu2jFf7imzedDO+jT5+e9tjdR744031LdvX+Xm5uo3v/mNa0dffvnSlr5JbQ3+9QbXgQSORQIB8B6L9IK2gQQCCRyzBFLf4fjLDUcc4CqOyUJM4ZZmZ4+LnSxfjnyNY6LQEg075G11qOtpcoFcLAOJv94vNb2zX/c/s0Ljrn5AXYdeqTMH/lRdhtyqn5x/l7peMEt5455Sv6J56ls8Tz2LXlB+4TPqX/aS+hU+owFFzypzyF0aPO4+1a7brX0RqQ1vDIp42uRom2dOEY14Gl6D3jRp8OXtj57DM79eO61BEpitzadLP58cGx8oROs4ePBg/eEf/qGGDBmiOXPm6Nxzz9Wf/Mmf6D/+4z+0aNEiByjU5RkRgR/rA3tN4IV7ggEU96YhBrxsMxOQBARRj5RIQPNJsH6rq6sdfL/44osuH6i0umQYnHFNmQX6N5hiDIKlNjbl+I61OVsd+iePcjZiAWb0TXuDR/qw/lgf5cjE+rJ5UY9AP3feeaeT75IlS1yf1p521CeYrLj+8MMPHYDSJ5v3qGNtyLMxmR+B+Vo/9MnYjGtyMJta1mAypK29YNj8TS6MRaQu9YhW55NPPnFjUZd8q4uGF9jt3r27m7+bWPIf6jBv5vXRRx+l2pvM/HXt+mBlVidIAwn4JRAAr18awXUggUACn7sEHAclNaTxcERENp4lON0hmY8DBnATPwt7o3GX7o552tuPo9L7zdK6d1v0yIINKrvlURVcco1+1L9Up587VacP+qm6XHCrelw8U7njH1dB4YvqU7ZI51Y2qFdRvXoWLlfO+IU6r6pB/coWqmDCM+o55mF1G3iDbqleql/vkECucDSiaKRFiTAmFGyU87TPKbdkh3FKXHI5tqxOZc2X+Rf5hW7jkwIqY8eO1UsvveTma6A2btw4B5xXXnllarMYkGMQZVBGI+DKQIoUeHv33Xddf5QBPMAW1wZmlgfQ0SdzIQ+wIjz44IP63d/9XT355JOpvimnvcEx9RjL5gQIcs0c6AdIB8pYE+OTcm/1aQ/0AYT0bc+Ea4LVZwyurZz+uacf+iMwHuMbuHJvZT/72c+ctnrLli1ubqzZvDbQlnkSgHDGNlm6zCSwU4cxqeOfv18WVp95Mj71bC2WRx2TDdfIxfLs2dDO1so19scEWxvX5FFGX4R33nlHBQUFysvLc5BOPvNFJgT6pj6BvunLL3NXEPwTSOAYJBAA7zEIL2gaSCCQwDFKAAKEB5IR8wUHumhOI/z0HFFbKKbWqLQ/Juc+7OM26eOwtPVj6en693VNzWINLLxb5wy5SmcOvFJnDLpWZw39P3Ud+TNljalRr9LHde7UF9Vvyjz1rlqg3lOWqmdFnbIm16pnyTr1Lt2g/KI16lu2SvkTFyj7osfUbeidGlY8Q+t/Hdb+uBSK4fUMjVxEiXALjnjdnN18ofFONLzp0jlc4KWdAUV6H5/HPWMTDXC4NjgDgLh+6qmnnA1tWVmZgxuDMNpwTT2ugUXAhohpBHkffPCBuwfqgFwLQDLAhpaPa6AHcAI6gR/qGkDdd999DngXLlzo5sqY1CECTr/97W+FthHg45r50NbqsCb6YyzyGZdAPnW4//Wvf+3yWC/zByr5OZ5r2nL98ccfuzr0YfNjLOZuAMd6qUt75oJMKGfOjHfrrbe6l4e1a9e6MtbLumln2lrqERib+TEe80OmrJH5IlvKCIxDXdqxuY/xKEeu3NO/PSfq2LOinDLWTJ/0zRqZE/UYlzJSxrJ85MjzpO7rr7+eAmVkQXjvvffcLwNZWVlubvRBQEbIhbb0RT2u6Zv5WT3qmgxcw+CfQAJHKIEAeI9QYEH1QAKBBI6jBPzAyzUnPOBmzPnlTThPYxwAsbNN2vpBixq27NS9j9fpwqr71GXgVH27YIp+dN5N6jryHvW4cIYyLn5AeRMfV5+y53Xe1Fd1/lWL1Wfqq8opfUkZxXOVWTxfGcWL1KNosbpPrlVuyTplTV6vPhVb1LMQ4F2o7iMfVPao2/Xwq1s8n7zxhPY171UiCbyKtDkNtIN0bIzTTlo7mHSOBHgP1s9nXQZYGFwALIATESAxjdy1117rII0NV8AJ9YAT4MUCdQEpwubNmzVy5Ej93u/9ntNmsuGsuLhY69evd9BD/4AOpgr//d//7frGrvX0008XWmSDT+sP0wrsiZcuXer6B7gIjHnPPffo7LPP1l/91V/pD/7gD/SXf/mXmjJlijZs2JCCPOqiWaWPX/ziF66tX5PLJjLKmBMgC5hdddVVztZ2zZo1ys7OdsD9ox/9yGkv6cCgG1lYX8jmgQce0A9+8ANXn/mceeaZAthNltOnT3cmDbxEMPfvf//7bux//ud/dvfIhj6BVoPlRx99VDk5Oe6lg3l+/etf1/333+8glToWunTpoj/90z/Vm2++qcsvv1z0SX1SzFEIzJGAFhYNbM+ePTV//nwNHDjQPSvqo51duXKlq2fPivUyFnL893//dzcOdtXMq66uzn1eaEA9QPa8885Tt27dHMiTz3rQ0Pfr18+ZyDAOz+qRRx5xnyPWbC8N1Pd/Lt1Egn8CCRyBBALgPQJhBVUDCQQSOM4SMOAlVdxtTovjU9cd7yvt2B/Wo88t1BW3PqgBY6/R2edW6ge9q3T6gGuUfelMFUx60tnf9iyaq7zJc9WrZIHOnVKv/lV1yi1eoKyiV5UxeZ4yil5VTsVS5VXWKaeyXnlVK9T78nXKq1ync8Y1KL90o/KLVinz0pf1k/Pv06Trn9LWHXHtQMssNsW1Kh5vUTzc6rlAQzsVi7rNdSmTBreGg38pG/CaLe9xluZx687AghRYIZjGE80hG8W++93vurh48eIUlACjQArBwJRrQMmg7LrrrhOAd8UVV+ib3/ymfvrTn7r6aAYxnQBye/XqpVmzZjnYy8jIcG1HjBjhtKRoFgmzZ8/WH/3RHzlTC9MCotGtqKhwYJmZmSmgHDCcOHGiAzdsSFmHBfrANhnQZJ30Y4AFEGIyYVBKm5KSEgd1f/u3f6t/+qd/chv3mBcyoS0ABzxyTUQGrBN75969ezuYZ10XXHCBm7tBPDJgHqyVcW+55RYHr0AyAHnzzTenoJR5TJs2zclkwIABTk4zZszQGWecoa985SuaOXOmewb2wkIdZP/DH/5Q//AP/+A8QgCo3/rWt5xniMbGRqetZd2YVGBjC3T+4z/+owNUYH3y5MnuxQEYBooJQC/rPv/8852c8MDAPNhM+G//9m9uTF5eCMgC0xigGRg2Mws26f3Xf/2Xq88aX3nlFWcnzgsOWmbGoK0F+1zafZAGEjgSCQTAeyTSCuoGEggkcHwlYASIujQRUSTconCkRVF858altdve1YCLy/StrkPU5bwp6jbs/9Rzws/Vq/BpFRS/pN5li9SrvFa9KxrUq6xR+cUNyp5c72Ju8XJlFtUpp7ReuRUNyi6rVVZpnTKJxfXqVrhMWZUrlFWxSnnl65U1oV4/Gvywek94RPPW7xZbpHbH9qslvktxdL3xVsU4YQ3b4nhEsWib5xPYzBmSwHswAdlyU2lqx97BWn3+ZQYWwCXXaDnRSAKOaP8AOMDs5z//uSs3KAZOgBSgz8CU60GDBjlgWrBggVsMfZLPT+toeLl++umnHSSxkx9NMgBmcdSoUa4MTSxj0DduyQC5F154wUEsdRsaGtw4aHcZwwKAhYYX7TJaWuZLfdaExvWJJ55w/ZLPXAiAGP0zRzS89AeIAsFodYE1ApDLnOjPv2b6wUQBbTUwioaVQD0Cc2WdhIcfftiNRb+AP3WYy8aNG10+oEhgDtj3Mg9AlGDj1tfXO432xRdf7EDbwB5I/fM//3MNGzbMrc3kAnizvgsvvDDVN1DKhkTyMbOwuqzRwHbZsmUpYEVufBZKS0tT0ExngDDadTTZ9tkA7nnhAHhZNzLD8wdAj1bb6tmaeGb2+XMTDDS8JoYgPUoJBMB7lIILmgUSCCRwfCTAkcKeRjfiYDcUaVFbPOpsdrd8sFtVN89R1rDLdcGURzWo4ln1LHpJeYULVVBWr9ziZcqavFg5RfUqKG1SQekaZRWuUnbRahWUv6aC8nUqqFij3NIVyiysV3ZxgwqqVqtn5Wpllzeqa8lC5UxbrryqVeo+frG+P/ABXXjVXNW/06KP4xw5vFt7wx8oFP7EAS+gC/CyeS0e84DXHYbBgRgxT6tnkIB0+FK3SL5pdkkd9PpMB46PNI9PL8yVCLQBHZgLFBUVuZ/agZh//dd/dTAD6AB+1ANY0mGXtpg4AIloGCm3vkm5JwBxaPXQ2BrUIjcL/Oz913/9104DjI0pAVdeACyu0dCkAoqAILAGDNvY1gceEPhpH00rkEj/eEcAuNACE2zNpJgIAMPAHvOkPnM0LSr1DV5tXeQhC7Tc9HH77be7+WB+QTBzD8qsLSn9Mo/ly5e7drZ2INE06WayYe2ow5rRhPJCwnP49re/7cDU6jImLxDIHwBGJgaWmBh89atfdVpX5kPA5AMNLzIyrbXN5eqrr3bad7T7Nv+hQ4e6Z4BmlufCPDZt2qRt27Y57TBmIbwwIBPskQF3+ud5E5577jmhLR8zZoxrZy8AtkZSm5trEPwTSOAYJBAA7zEIL2gaSCCQwLFJAASMO/8L3q61aKxNkWircz2GP102qM15boV+0m+KMkbdq75lL6tn2TKnuc2rWKmeVauUU9qQhNkVyitdo7zSdcopXuugN6uwSdlFK5VbSlyhnJJGZRUtd+CbU9ao7Cl16la22MFvt4kL9J0BNcq8eIYKb31Mz65Yp/f279SuyC53whp+gEOtbUmzizYHvGh8iWimOXUtTIxGFMHcgc1u4ajCETR/7IZHC9h+8hSgz5e5pUC/F3xpu5Ly2AR9hK29ebVvWjPooRuucVVmXhrQ+Jr2EpgCbmhvkNjU1KQ//uM/duBFe+CUPoAZYJLAxiu0wPzMjvkD5VaHFJtZXKDhHg3IIw8vDQAvbskYk8CGKDSOaI2ZA2NRRn1ADOjGXALYI5iW04CXugal2BgDz2h3LR8TCaCZ9RusW2prByptPpMmTXKmCmhCySdQn2vWz7yIpmnGvIB5056I71rWjf9jwNHMRdB+vvrqq87UgZcOtLfID4C95JJLXF3GoC+Al3XYeKT0g5cM8nmBIVCXzWaYgtAGeCXYWsxmm7myVubN86IPtMREzCeYC88J+OZXAKCdvhmPzwpmG6Z9BnzLy8vdi84555yju+++WytWrHDwbLKhrc3PXQT/BBI4Sgmc5v2R9b5sOr4+yp6DZoEETmoJ2P+ZE32Rto7O0s9ufXyNodv1fuCFpBLehrWYB5ORRNwdAzx/5bvqcu5lyrywRj2LX1HvykZnjpBT0eQANq+4URZzSshrSgLuSuWUrUjGRgG4OeXLD4jZVU3qUbZcWWX1yq+qV1bRXP3kwln68bAb1e2CK/XTmc9q4bq3tTPs+foNhePObldsYIti3rBX8fAeKYa7q2a1hPYqFMdHsNQSiTuzjHAMaHBuhb0/sSwcP8OxsDOJ4Pw2r8DbrOcdccHz4Ajl5HOhjfe9/9k9EF/PQAYR6ACQgDRSQIeUgNcBs681l2UGU5TTFrACeP/f//t/DkYBUAJQafCGVg/gBZT+53/+R/xkTrAx6QegBvqogyaRufEzONpWAMl+Iv/xj3+sr33ta84UAJimHpuqgDbGQLsIlJkmkU1eANvjjz/uxjTQZB3YsP7Zn/2Z+/nevC/ccMMNDmDRhNK3vz6AiXwMfCnHjIL+AV4CdeiLNTE/xiHvtttuc2Ox0Yt2BtGMg0mEmTowHv0D6sDkN77xDXdyGZu+2ETHi8WECROcdtYNKAktLHOgX8ZkbCJaWPIxt+CeefCiwKYyOw2NPhiPcjS82BljhmA2uP/yL//itMdobpEr7UgBWza88asAmmIC5hKYV2DSgAaa+RCwkcb2t3///u7Z/e///q97AWCurJexgxBI4HhIIADe4yHFoI9TUAIGiCf60m0dnaWf3fr4uksBr91Ahu5QhxZ3fDA/eDe9scudjtbtguluU1pB5Qp1L6pVBgBb0g67QC/3RLS5TqNLnQNAt0455RaXK6OkXjkVK5RT0eDy8yqWqKD8FWVOeExnXXCPvt/vSg0vr9FdD9eqfv1v9cn+5JxjCbW17pNiLQq37mw/fU1taiMvEQFXFU5IEfa3xZLsyjoRtUtjPuA12O3kOVD/cwzACBHYQRsHdBisAiEEgBe7TqDp+eefPwDUaAvM0Q7NHqCENtACMEN/BolobQsLC91GMH4ypx1QbGCKNwFMGrAh5id8YJmNb4A0P4vTH30NHz7c5QHZ9MEcLKA9RVsKHBqEscEKMwo2ehFMI8w1ZhtokAFUA96bbrrJaVHRhBKQBWMQuWYOfkjDVAH5YKNLMMinHjKiX1KAF1BnjgTbKAfcA7zf+c53Un6L0XbjueI///M/3SYvnhEBjSzeHdj4x+Y9+iWYhhd5IhMC8uK5YLIB8BKYNxvSAFK04GYWQT4B4GUtvCQwP/rnBQNTDOrQP3mMYan1S4q2GghGC8/41LO6lCNDTFeQO+Yk1CfP5kydIAQSOBYJHAR4j6XboG0ggZNdAgYmJ/o6bR3p6We/Lr6OwRH3tWw3UY5bCzmQjCfCAnjf3B7X1fcv0ekDblXB5Jed7W5m6XJlljYou7Qdch3sljYot6zRxbxytLvpwGsa3nbozatsVHZpg7pPXuZgud/Utepb3qDscXOVO/Zp/fjcO3VW/+s04YqH9fTC1/X6ByFtb5ZaYvjnjSuWiCoSa1M41qKEm3FICbUpHGl20O59saPx9EEvpxEnPC2o2TAfSuKIyIvtlsCHanO05QALkQCMApVoSgE1UgKmBwDW3/3d36W0sgAd7QxUDFgxJUAbC1AhD8CSOkAXP9UTgE4As7Ky0mkQDXToAy8G2KFixgAkEkw7O2/ePHfP2GgKgTI0iwRgELgCyPACQBnQamvDDhjTBXwJMw5j0oYNdKyL+rYGtK7XXHONA9PXXnvN9U+/RNZi16Q2d+AdLSheGbBhpYyxkSM2twam/JTPSwEeLyinPRENL5ptwBIzCsroEy8KuHgzW1jmjD9iIJgNfsAigXlhXoDsuQZKLZ9nwfq6du3q8vgHLw1oa/G4YMBLO+Jll9mCcyoAACAASURBVF3mngEyNpOEG2+80fWBrbLJFHlzjdzQIrMO1s21mTTwskSfpsnmmjqE0aNHO1thtM3kmyxTkwwuAgkcpQQC4D1KwQXNTnUJGCCe6HKwdaSnn/262gEuORZTSANe7Hg/bJYemveWvtv7GmWPeUoFpbgXa1RGyfJOgRfYPRzgzatc7rw34LUhs7jRbXjLKlyt/OJ16le2Qb2KGtS/ZIlyLnpEp/e9UZlDr1fpzb/Us3Vv6a2d0o6w1JyQmt1xx22KxFsVCnOULlDWKsXaHMDjao0vb+x6I2h9PYMFd2+gcIDEAeIU4KZff37Ay9zwcwv84d0AIEN7+uyzz7qfroFFXHWZTSxrBGZpB8AAPwQ0mNi+ohHG/AGQw26WtvjxJeCRgJ/o0cLizgqwA+LYWIZ2F/MJ/zjm2QAzACAYMEJzCxxix4utKZvA2FBFH2hFgTu0s4AW8Ad0nnXWWW5czAQYDzDGnhSTAVykmYYboEYGaJXNzhhwJbJWuyY1UANogV3mgw9cNLiMwVhmNsA8AEa0rciFvgz+mCvAy4sFkEhgkx7zw4sEXjJ4Jo899pjzrsALA1pwA1JkgqkAYEuwlxaut27d6rSzyJUANOMrmXlxhLSZghhwXn/99e6FBM26aWgxE0FzjxsyoB1NP8+DFwaebVVVleubzwTj4aUBjTPwT8B8hXasgXU988wzTptNn9Rh7A7/f7jWwT+BBI5MAsGmtSOTV1A7kEBSAgaIgUCOVgJ+oHN9HAC8bU472hxLOKhcuG6Pzh50g84eMUs9SxaoZ1WjMorqnGYW7axF0+jihszFsuXK7TTWKadsqTKLlriNbz0r1iu3dIO6jV+rHuNfU17xNuUXef55e5csU6/JL+knw+/Vd/pcqV4T79FVNa9q6daP9fbusHbGpJ2hiFrQ7qG3joa8E9k4pAITjWhIQG80HlEoEVOb8O+bcMCbUnOnQa6Ze/hTv8y4/qwCkGGgAWhhO4ttJfadACLXgBgbptAKApCAHsF+tgdyCIAUoAoAcTgBXgfoB7tUfjrHBy6AiEYQGCQPrSibqQA96uMqCxdfAJDBIOAFyPntXpkD93gZ4Gdxfq43oOXQA7TB1KEf5sUc0Rrj7xbABYr5yR3NJeCHDS8mFAa0mCgA7rgAIxicWjl9Ey0fuQC52NEC8syHMZgT8G/gh10sawH8kAWypx+0otg1oyG3usA9LwuAPRvDKCNiEsILA2PZiwFzNJdwXNMvc2J+wCpj8iwsANi4dKMN5QTkxJzYtIYWGkDlnj4AX15+0AgjP+aELTHPjc1v+PulLoG+gW/kavDOCwRaaQ7H4GWE9TAfwJfPUQC8TnTBP8dJAgHwHidBBt2cahIIgPd4PHEDONcXIkUF6uDQA14Qam9ceu29hEZUPKjTB96h/MKX1KuyThnFy1ImC+nAa5vTOoddD4QLymuVX1rnTCCyS1Ypp2Sdcko2Kqt4kzKLNiq7eIN6Vm1UfkWTcksWq3f5q+pV9pzOunCGvtlnmgZX3K+7nqzV6neb9VFI7hhi7HadqSSnxmGe4Uw0worHQorGQ4oIUw1sfONu75oDXtbuA16DXOp4CO35sjB5fZawy7Mw4AV2CGxSwt8tmknMAEjRntrBCUCNAQogZMCGlhDAIqB15PQuNLr4eEWDy8/WQBPjAHgEtIy48QKsACY0wqZtpByNKCCEdpaDIwzMGNNsX9EmshGN9pg+YBtqwOgGSYI5/TBvNI2YVBDNRAI3ZvTP3IisCy0kdr/0RR7tLSKD9EgbAhu2kBlgS3s0oZhzMF/qrFq1yo1tm8GQBX0hG+AWjSlwTh4BmQLdzI/1Ae1ok9H0Mn/aEQm09YMnfdAecKYdwGrzRs48Z56NgTtyJbB2NO9AOO2Zjz0zXkaQMZp0tNX0ibYa2dpnibXhUQPzGJ4hY2JWAdyyARGXcmis7WUCudrYbgLBP4EEjlECAfAeowCD5oEEAgkcvQQM4FwPKeCNODjEhpevd6wO390r3TxnuX7Q73rlTXhS+aWLlF2yzDtQony5stOiAa+l7eDr2fd6dr7L1atihXqWN7rDKXoU1iqjiEMqViunao0yy1fqzPGLlFHe4E5nyypdouzyRcopf1XZJS8pe/IvdebwO9Rl6PUaNeUBPfDSRm36IKrdIWx7PXZ3p7Ch7Y0DvG2KxdsUTaDfZW1xJeAh4gHAC9xGFVPEF7nHlCHNzuHoRX/QlgYpVALsLAAwBnqWB5gAL37w84MK19aHpdYv7QhAH+0NoMizPmhDPatj9V3DpDcI2hLS2xtwUmZAzjXARfCXc29aaVeY9o+NQTbj2FpMVh2lJhfrymTEPdfWxvLp18ahrb0sUJ+5kuevw7V/Df75k28ypD3X1LdAX/5AOTIi2NqsDn3529pLh82bNlbX+vTPy+Zha6Yv8vzrS28PfFs76zNIAwkciwQC4D0W6QVtAwkEEjgmCfiBF0UShzcoeiDwtiWknRHp8UVv6Qe9Llf2mIeVX/iKp5nFuwKnqCWBF/dixOxkPDjwNiqruF7ZJcmNbBUNyiqvVY+yZepevkQZlcvUo3KJMqcsU0bFEnUvW6TsiqXKKl+i7LKl6lm5VLmTXlD2pY/pnOH3qNuQmzXm8p/rhbq3nd3xvqjUFpMi0bgzZfB8DLcoGmtVPIHWE48UyZ17SeCFfBOKCldlAC+pF8nzoM5RL5fJ22N6AJ00BjSIfggxCKKJwY/Bmb8by/PDUHq5v1/K/G06uvfnGSz5+/fDFXX9IMW9lXc0b5uLH66oz73Ny9+f1Sc1OVnKWP5gfdq4/rbU85fbWOTZfK3c7v192xytHz/sWl+UpY9pfVgd6zs97agdbWwtzM3mZ3nWh41h96TWn83X6lhqfaWPYeVBGkjgWCUQAO+xSjBof4JLwMihs/QEX94JNH3nxYATy+L4n0UTFXUeEGBC9HEL13yonqNvc3DZv3yRsiYtcGBbMHW1g16ODM6prE9qY9H+1jtXY6T5FY3KQ1Nb6kU0vGxq81yXrXA2wMAuMJtRsUgZVQuVMWW+upa/rB5Vryp76mJlT1mq7IplDooB6YLKlepVulq5E5YpZ/zLDnzPHnqHckbfpCvve15LN36g7VFpf0LaH02oOWQ+bOOKh1vVum+3YqE2z/ewqXoTEacFNshtV//i4iGp3bWPqlP3nkAP+Es4VYMspua/5t6A8LOcNmP6x7V7f176+IdTJ71NcB9IIJCAdNqh/mMFQgokcHJLwOihs/TkXv2XaXUpN10+6EUrim6TH1o3vteiMZc/qK5D7lCfwpeUX7hYmUXLlFXWIGC3B8cMl9d60Jv0tQvsupjauOY3acBlGbDb6PqgLZpcB7yVCxzwZlbNlxcXKrtysXLKl3naYzxEFK9Q38rNKihap9zC5covWqTMsb9UlxH3qvuFt6nfxDt184MLVb9lu3bH5NaAT95QKKJQ837FIy3O/Voksk8JDrLAhIMNbkkbB2cOYZBrKYdzuIhj3wN/kv4yPcsTZS5+qDWQtLlT5s+za0up57+2dkEaSCCQwJdTAg547T9txyk/2/CHNQCCo3mEHcvUe6unLAjHKoHOPpfHO/9Y53mitj+4HO3zfdSrM5BLdoCW14FGPJqye2WjF1aknHZ2+4NL1WXg9cob87j6AbWl9e7wCM8vb50yy5Y54M2tqlNWOe7L8LcLpNYmtb0NzqUZ3hzY6GYp0OzMIZyWF03vUmVWLhP9OK0u9rtltcotY4PbcuUW1yl7cr26j6tTxqRG5ZWtcscc965arryS+eo+BvveGeo+8h4NLpqlmmc36J0d0u42aX8rP5fHFE+0KhzdxTEA/AiPoYD7O8ufW5TcHf/JBXSRRlsy5fkE4WglkP43+FD3jJNe52jH7qgv+/90PMc4lvkFbQMJnEwSOKRJg3fOewC8J9NDP7nWcnAg64QaOqOJg+SfXFI7/NUcSr6H31OHNdOBNyFFYwmh3cSrAWDHRq+2WMhpSJ9atFXdzr9OPUbM0YApmBWsELCbzcazCk/Tm11R50C1R8ki5VYAustSp6uZuzIDXbP1BX496LUUrW+jsstXKKukIWXrW1DmbXIrKPGgF9OInlNWq+eUNU5TnFns+QbGNrjH+Fd0zoVPKPfSR3TWeTdpRHG1Fq35rfYlAN+ImiOtzl9DTCG3Sa011KIofoiRCWL3rDq80zm4d7KChCnA5RfyoSAIRyOBLwtU+iHX5pSel35/NOsN2gQSONUl4APeQ32x8Yc2PQZ/bI/9A3QouR+q/NhncGL3cCj5HKq8o8/0wdqc2NLqfPYHWzNl6XLinvxjDIcC3nir4vH9aovsd3hXt3G7+o25U13Ov1/9y5c5Dwt4VshKbjzLLKn1NLEVtcosWew0tWhocyoxdagVMIzpApvcsMNFa+vF+qQv30bhniy7ZM0BMacYl2VNyi9ZoYKyBnfaGy7NskoWKLN0kboXLtA5kxYpo7hR+VUbVFCxUXml65Q1oV55Exao94Tn1WPY3coceo2uuOsprXx7h/ZwhKykPZGY02AjUcQRM9H69QxOTvYcbCObpxE+xidwSjY3gPQv3vIspcx/bXXJC0IggUACJ54EUsBrmlxLP60Z877w2ncNe390j8uX3oknt+M440OBxqHKj+NUTsiuDiWfQ5V7n+t2oDtU/RNSSIcx6UOtO11Odk+7Ywg+4HWwF0+688J+ErOGyF4pvleR2F6n4X39o6gKr3tKZ/a/Uz0nvqq8knrnSgwtrNPalnkb1TBlIGaUetBrwAvsYvbgPDlUcPDEEi+Wogle7tn0ArzFa1MR37zAa27JauUVN7oxC8rqlV9Zq9zKRS7mVHqmFFkVTcqqWKPM4jXqMWmlCkrWKXPCMvWcvEj9Sueq24h79KPzrtQFU2fqvqeb9Ks9nkEDRg2YOyBV5BANR+R8lrF5z23gA249lS9Oy/DiG0nEnU8Hg7IAxD79OexMJuQfzH7Xeuqsvb+cOhYt/0hSm0v6WGZD3FFfNl562lHdIC+QQCABTwLuaGFnTxbHD2J7BGz585sOuLjTsRiLh5wtWvp/uuC+/Q/goWUR+5SM02Xe6X3i025xDj3ekcztRKjr2Zjbi1p66v9Md3R9cNl21PeJIJMjmWNHa2z/TOIL98DY/v/f5bu/Ge2be/j8HVHwAS8OufBKRnRuVTFmje6T4nsU1z61xcPOPdldj6x0wJs7dq6zpWXjmRcbnV/erFJsdpeqYMpyZZQuUmZZu6Y3g41pzvVY0t63bJHyyhYor3RJ0j63UTkljXIa3WJPy5tTulYulq30ykrrXd28ilqn2c0EmpMaZANqTCW8k94a1buySZkT56ugeKGGXrVcPcY8qm/kXa/v9rlWo8rnaH7Tdu2ISLvCEi7YgN7Wtt2KhndIsZ1SfJeUwCdps/NaEY4n1JrwXJ55svIOP7D/+375W15nqb/uyXjd0eeRPGDSgJfU3GaZnAw2Ld/q41qLaOX+vqwOqdU7VEpdxsCtl38s2tm9jWf9WxltLI/U5n4yPsdgTYEEjocE3KY1958maqfEeMdfchpQ1NMjuKMy7T9WzB2PGVIo0axQvNX9AY4l4t4X1amYsuZjip7rpVjiaNPk+IDCKSv/zmXHUa4Hi4eWO/Kl/0M85xNV/m5dRyI/77QwNpIh10gsrEgs6uxukZHzIHDAJtek6Wlnf61SwGty9mTtuNmxc8hpeNGDtsVanXuyX85/S+f0v0UFY59VfslS5VeudMCLlhdb3O6FS9Rt0qvKLl8iDovIKF2qjJJa58nB/PW6TW4lS5MHSSxQbvli5ZYvSx5DnITekiZll3oxq8Sz5XUb3ZLmEAAtdsPOD3CFB9AAL/CL7bAD75Jlyin1NrtlYvIwab56V9Wrd+Uydb3oMZ096E79b49SlV7/jLb8Rtobk3a34omXYyaa1Rb6SKHwh4pEP3Fa7lC8TaFEQq1xqYX3AQ64SIOwdlG3nwJmf79JeU5231735LwCAtNDOqTiY5eDFDh0AYgk4nOYiG9bImX+SBvzzWv9+UGUtpTbs+ks5Tn4gdeeC/X98+Da6tE3c2Fe/n5p29F609cf3AcSOFUlcFosllAMXznJXzT58mqNt7qjL/cmmtUizn5POLuyeETuKEx+TmtTi/j6aU2Wh3C3w8kpaSmO19s4vvFkTeNRtR0ihhUXMZTwXMk3R0JOi8O906En4u7nSX6iTI/W1p/SzosJT7Yns3wP8bkxubbLxGSTTIExNIecHhSPucg1MV3WHd27z657vvF2WSPv9HiIeX5ZP//+z1VH1xguWb7J2LvnB/bkEQmJuFqjnozdHwqO0uWPRSzs2aQm5e2hh8+fLBn83XEFnvMxPBfwC5I7gMJZS3D8bpui8X3uBdy5J3snolGTHlSPwdUqKHlF3YoWqKBqtTKLmpRZ6PnczZj0qnpN9ex0c8ub1P/KzcrBZ275KmVMXu6ZJ+CHt6pWGZULlFm+0Nn7AqxZpXUueja+3iEWZgKRAuaK5cqsSG5sK/Xcm2FSkR5z2fSW7M877Q3PErXKr6xTv6n16jLqEeWPfUxn9L9FWcNv0fQn1uhXu6TdYWlPS0jhBBvaWhVN7Beb21qjzdrTtkdt6LyTR/byfsHfcGcG4b7J0DB6v9D5X+jQoHvHWiQ9m6VY8BjNUo7q2zP5hXNUbQ+/UToApgMlx/Hu2bPHwaVBL0BJIKWcPrjmGGDrb/v27a4O/e3YscMdm0wGYGpt7Z52tDew5shfg1XaEzh22YLlGcwa6HIUMP0Du/RFP3Y0MffUI1h7S63fIA0kcKpL4DRcOvJ3LxaWImGpJYJeIaGdimqHosJpzl5+PuNY+IgUjnr7g/coqt0Ka6+kfUnHOkHq2eOly4GNKTjOJ58UmZHikJ6UyJ5ru/an2Pb5++O6o5he71S5R7bEdJmYDJEDEVnvi3sp95Q7+Se88pZkavX9aSD/jj/XJsO9sYSTceoAWr7EAa4EENtuHc1Lhnfng94U91C3xWk1gV7P16y5mo07F15Ouynpo33SZTe8qDN73aTcyc8qF/vZkib1mLhCvSvWK7+0Ud3Gv6y+UxuUU1Src8YtVlZhkzImNTnQzS9ZrZzJq5RfvlrZU+qUUbXY+d/FDVlqU5tzY4Z3By+y8c2LdW7DG7CbWY4PX08DnIO5g4tmXmGp5/4s5Q0idQJcnQqq6nXu5U3KL56nnIlPqsuwu/W9XtM0qqJaLy73TmtD44tc94fb1JZoc94ckENLrMXpgFPKCgSdBFjMegx444rIi95hxUiZF5ZIghc+a3LyAq99wQOq/ggMGhACvGeccYb69eun9957z8Eo7YBQIPOjjz7S22+/7eDyww8/TGlXgWHgEzClbwNi2gCjRMAYkGYMq0/fBrxcA8yUU/eDDz5w14xNe0DZYJm6mzZtUpcuXTR48GBXDgAzPnVICQbllrrM4J9AAoEEdNqu/a1O+/r2u9v17sfNem9PVO+2xLRu526t3bNPG3a3aMuOiN7+WHrnI+mdj6U3d8S0eW+LNu1r1tY9Ldq2u01v7Go7RdOwtu2K6o2d0YOmb+6KaevOiF7fEdGbexMu5XrbJ2EXX0+mdu9PrR0pkfG8yNjhU1Tu3ufNrR8ZpMU3doVFfGtPVK/vDGnrJ63atqOtPW5vdXlv7PCeiaU8E39Mf67tsucZnPjyd7LZGfJk5E93eJ81ZMFnzi8H7/MYdflv7084Oby5V9r0wV5t34V/WKfqUiISdpp0297mHY1rm7GSgNYh8KIh5oAFT3PJBUdPAHpJD7Sa/ehrOqv3Deo2+mH1ndqonmVrlT1plXqWrFNByQrlFS9R5vh5ypu8RH3LVqp3SZN6l61RzqQGnTtto7ImrnTeGDLK671jhCuWKhOzhIo6Z+OLH17n3QHgrVjqc23GxjYgtt3cgYMr2u2I20HXq9cgtLxoi9H04kWCFADG5KHb5PnqeyXu1V5R5sTH1ePSan2v/xU6e/DlKrzhIS3b+KHe35tQc9JmtzXi2Xty6ATyDbWG3fkTaHkjoaji0STFOvhlUxumEQa7eIPg17moS7lOMvIX8FWYevCf+diAn8Etg3GNNpQIKNbU1OhP/uRPXEo5+UDqf//3f+u0005z8Xd+53f0ta99Tf/8z/+sG2+80YGxTRzoBHYBWq7XrFmjK6+8Ul//+tf1B3/wB679H/3RH+m8885zbT/++GNr6sAXeP3KV77i6v3N3/yNS//1X/9Vw4YN09KlS11dgJh1EK+99lr9/d//vZ555hlXRp4feMkkLwiBBAIJHCiB0/Dk+Nu9LSqcdoP6jSpS1tBCdRtRou5jKpRffrUyxk1V1pjL1fPia9Rn9HUuFlxytbLHXamsCZcpa8xU5YyZqtxLO07zxkxT7thpOinTMZcp79IrlDvmioOmOZdervwxV7p6ORdfpp4TrlbuJZe7NtwT8y6+zOWRb5F26f0z1oHxspNXvofxuckdc5ks5lw6LXWdl8wvGHeFuCZynT/2cmVfPEU5o6uUe+k0FVzqPTtLkbfFjp7rgbLnWZzo8vfkZ/JKpR18rvPGXpn6HOddepX4fHYfVamsi6fo3KIbde6EKzXz0ZcUMlcDCc90hFt0TyngZUMs38fEFPcAX20uYtJg+c4cWJ6GF4MgTBrQxi9Y/rH6j75X3UY8oF4ly9S3dJ36lG1U1oQVKihcrkHTGnXWiEf1/f6z1GPUMyoY96oKxi/QeVWNyi2sVc+y1cooanCHVnQvXaoeZYBufTJy8AQuzJY5O2BsgdkEh7bXM0toVG7pSheBXe8AC0DXwLfdtIGyvIomd9iFAa9pe3uULFHXwvnKqlqivKlL1PuKxTr3qoXKmPiwfjTkVp057Dp1Of8y3fGLpWp6fY/e3xFXW1TiNOLWvRxJjAY8qZ11svRg1/kwxo9x1NuQaNCLKZoHvGHx6nAqAa//aw8oNbtcTAFGjhyp008/XevWrXMwbJrSv/u7v9MPf/hDzZo1Sy+++KJuuukmnXXWWQJ+8/PzhbYX0EQTSwB677rrLv3Lv/yLg1b6PP/88zV58mTl5ubq3/7t31z+7NmzBfQC1cxlyJAhLv+pp57S7bffrjlz5mjcuHEOgr/1rW+5cQDeXbv4vVV69tln9dWvflVXXHGF0zCTRz9+qLc1uAbBP4EEAgk4CZz26z079c6efRo0rkrnF96gkVfNVv/LZqrg8hnKqLpb+ZdNV6+p1epfMUcDyh/UgLKHdG7lQ+o99QH1nDZbvafMcrFPVcdp78oa9aqq0ama9p0y262fFFn1qpipgopqFZTNcDLpM2WmiP2n1Kjv1ANjn+R972Qd0vSIbE9d+Var99SOY9+pM9Vn2kzlltyt/LJ7xf15V8xxaUH5fepdfr/6TatRvypP/pba8yB1sq6qVq+qavVOplwfGE9k+VerzxRPfp9OvfWbHPhs+j+P3v/nag246iH1u3yW+k+doW4XXq6f/fxl5y8X/mJ/gGl3U+YMwKxzteUDXio7wk1aDHOSmIGw47m4ojEMrbz9BM0xadM7YU28/DF1H16t3AnzVDBpufqWvqZexStVMHmpehe+pLOGzFD2iDnqN+Zx/ajn7cod/YgKJjyjvMkvihPRek1Z6fz3pg6YKGtURnlDMnLM8FJlsfEN4E1CLyet5Zcud2YTHEKRU9rgtLUOdp1bs0bPvVlyYxuQm1fZ6KKn1eXkN8+2mM105127Tl1LFuqsyfN0TvF8dSudr74/rdd519Spx+THdPrQ2/XNgkoNnHSXHnphvTb/ulX7sCXBKiQutYVDnmWubRTEVCEU9lxdJCEYbR868nbgjWIV7DS9TvRfyJehPeDPdnDWnq7tBA4BVdKNGzfqG9/4hioqKhyA+jWl//AP/+DKgGLyLVx00UX6vd/7PQfC5FFGvPvuu/X7v//7TjN8zz33OBD2jw0Q33///Vq7dm3K/ID2vXr1chDNtR9Ub7jhBgfChYWFNrRbC2YXAHReXp4ztaAQrbR/LH8/qcbBRSCBU1wCp+1SXJ8kpIKLy3VeyZ2acM8Cjb5nqS6Zs0LDq2s1es5yXTJ7uSZUN2nijJWaPH2tJlav07iZ6zS2hrhG4w4Sx85crZM7rtTYmU0aO7PjdPys1RpXs1Ljala5emOqmzT4llc1+t56XTqjUWOrV2jczCaNr1np4oRZq5L1aXNg9MZp0pia9nhyy/bgn50xNSs1dhZxhcb54vjZTbI48OaXdMHPFmrczEZNmrNK42tW6NLp9Rpb3ZC89+Ru8if1y91k7k9PHvknP7M1jRqbHt1nms91U0oeB8igukljqldowgOrdMmMWg2/9UXlTrpTd/+y1tlHo+UFpo4GeLFBNeB1HIemOI79KltkEw6oP9ovXXfPXJ3e7wb1K35ZfUvqlTthmQZNXa3exa/qjPPv1jmDb9Xcpqieq92ni0p/rt4X3qWuQ29RzrjZ6ln6rHpMekG5xcuUV9bk7HnZ0JZV2uROWAN80fq6TWzlHvgCvXhyyC8Fej3wNW2tQWx6SnnqdDd30AV+gu3Qi3r9ZOzLysck47ImZVYsV/fyOp1VuEg/mfyKcqcsVf8rlii38Jf68fk364d9pmj8ZXM0t+4dvbdd2tPqDhhWFMvncIvzmIHg4m7DGhuRY54v46TrOLwzsDGTDW8WTwXg9X/HpwMwkIrZAiAKtBo0ApD/9E//pO9+97sOjAFIwJfyhx9+2AEv4EsgHxvfHj166E//9E8dCFPP+qMvtK/0QUq0vsgzDS99cU87bIN/+ctfClOIoqIil08egf4wa6AMzbP1bXOnjv/aNQr+CSQQSECnsfnsw5hUcOk09SmbrokzV+miWZs08sHNGvbABg2bvVYjZq3W6Oq1umjGel0yfaMumr5Zo2Zs1cjqrRo1c9spHLdoVM0Gjap57ZDpyJmvaeTM9SLNuPJF+FziRQAAIABJREFUDb1vtUZUr9OIGWtdHFm9TqNmrteF9DVzvYuUe21o9+k4wtXdcsrLf+Qs5H9gvHDWBhHzrntV595e52SMbJEzMkfGo2dtSMnaZO5PkXl6v/77ka79iSz/Ld7nqmatRqbH5OeN9fo/e+4ziwyr17t4Yc06XVjdpEG3vKLuE+/SnU/WuQ1seGsBptDsmkmDd8Uudm8nu4Mt/nEXaPwwa4h4X9Y0pA/PFsJttYom2KgVdcCLkvPB51bqjHOvVO9Jj2tg5RKn6R04ZZlyxj+q0wdcrbJbntEH+70Nd5+0SPf9YrHGXzlT3+tdqMyxP1NB6RPKmTjXmUD0LG1SQekqZ6bA5jPAN7O0wXlicLa9bkMbdr24MkseVlGGqYNn/pBTjua2PaIJ9uJy5ZR6kOvcmCWhl6OQuecAi9yipd7RxaWNyihrUmblKmVVrVb3suXCx29G8Vz1rXpZGWNm6ds9p+rswVdqyp3Pa9FrH+tXu/CUIzUnQRZ3ZmG8ZGC+EGqWuI6GPPCNAVveZjV7EXGiP6wvQtPIpqeH1fhLUQkw9IMgGt6xY8c67epLL73kygxOmfA//uM/6ic/+YkDTLSz5knhoYcecpBcVlbmwJW6jz/+uMvDPMLqAa/0h+kCATMKxgd2KbO59OzZ07WlDrBL2Llzp+6880794R/+oW6++WaX5y/HzAJQp8zmzPoI1q+lqcbBRSCBU1wCp+1XXL8NS30nXaeCslm6dMYGXVDzpvrfv0kDZm/VoNkbNHjWegFXI2dudJA7csYbGl79lovDql/X0OrX1Vk6fOYbGjbzDZ2Uac02DZ+5VcNqNmt4zeYO06HVG13+sJmbNHTmJo2YvVVnTHleQ2ZscNdDZ2zWsOotGjFzqy6o2aaRNdtc6vW71ZUNnbnFpcNqtibHY0yL205e+R7qc1OzTcjGH01uyHL4rG3KuX6Z+t+5WsiZsguqt7gUeSN37jtKyaNf6y89PSnkf7DPr/tce59Jt9akPJCj93n05DeyZpMueXCLBt22SF3G3qk7nqh3HjMcp+LOMAm9Hljxhey5y+KeMge7fuBNHqbiCqlOmSvH2wBa3oj2RtqcLe/8VW+r39jbdPawO9W/6HmdVz5ffUue04/Pv0V5l9yglb9q1sct0n68y8TkfNdu/c0e3fvEfOWPu1o/GX6tsi56SPnjXlKfwsXqXVynXsWNKihtcuCbWdyoHE5Oq1yhrMp64cUho8IzccCNmd+VmR92uTbgzStvUHaJt0nNgBczh6wyz7a3oGS5ehbXqxda5pIV6jG5UVmlq5RT+Zoyy1cqu3KluhcvVkbxPBVUzFW/qufV5aJ79c0+05R1yY267RdzteKND/RR2AN7jine1Yz5B2Ygbc6lm2Ic4RZyZxbzAoEphLlMPvzvv3TQtfvD7+GLrmlAyDwAS2xiBwwY4DS5bDQDEAFRq4dJQ1ZWlgNWIBZAxhzhnHPOcRvSXnjhBbck7HGBXzaoYYNLe+oSbSz65Z4yABX4ZQ5cDxo0yGlrt2zZojfeeMNtenv11VfVt29fZWRk6J133nH90J429DVv3jz91V/9lbMPJp+527zTU9c4+CeQQCABndaquLZHpT7jrld+yRxdVL1F5894R4Nmv6sBc97S+bO3asjs1zS85jUHvYAC8ArwDpv5lgOvIbM82Ds1060aMsuDz47SIdWbXTmQMLhmi4OwMy9/WedPB4S3avCMzRriIGybhjkAAeK2ujikxsvrKCXPxVmMf6rKf6snA5OFT17DZ72uYbPfUPYNtep1W5OTLXIdVu3JfMSsN5zcubd8f8r1YAd9npx5Nv7ncPLIP/3ze+A6TQ6fSnnRnblVI+dsc79aDL5jibqOv1N3P7XcuYlzjJr0ApBiVmengG6RbVQdAa93VK5zlZiEMmAXSItF2zz/vPKOxNkdDun95oTGXFGjH/S/Uv0mP67hVyzQOaPu13/kFOvup+r0SVxqjjF6XNHQfsVj+L+Oa0cspoUb3tZl9z6t3mPvcYc/ZIyYrb6TnlfPifOUP3mReuOjt3SFuk+uVUZZo9P0diurVVcOsqisU2ZVrXpULlEPZ+O71Gl6/dAL8HoHTix3G93MjMHb2NYoNLxc5xfXO+DtWdSgfI4tLm5yLtayOd4YLXPxcuVXrXAHWXQvekWZJS8pt3Kuuk9+XD+64E6dft4UDS66RT97dLHW/HqvdnMgBRrfcEyhCJsAsYdulYPeSKuibcRQyifZobWAHtimn2Do1O9md3LQ9Iv9lvWvD1AkAJmE1157zbkj69q1q4BNA0VrgycEvDJMnz7dmRfce++9zvzgL/7iLzR16lS3SQ3YBHiBUzarPffccylwZqNZXV2d2KT26KOPOlMIbHtxPYbrMgt4acA8gY1wI0aMUEFBgTONQLuL6YLNy0waaLdy5UpnbjFw4EDnMcLqUGbzt/6DNJBAIAFPAqfhk3FHEnjzSh7UhdWva9CMX2nQrA80cPY7GgxQAbyz1jvoRZM5fKanVURzi9ZycM2GUziyfg9mO0sBYSsbOnubAN6BMzY6EDbgRZZAlSfTrU5rDlShPQe8Bs/sJK05leW/KSWbITVviOjJ6XV3PXTWm8q+vl69bl3lyTX5a8Tg6Vt1way3NGSGJ1/ADTmnp8j95JZ/8rObhPsUxDu4P1CeJld/imwunP2GRlW/pqG3A7y3O9Bkz7oHvGi42C5l9/zR8QAK9HDYcUBhx8BLkwR2qBxr7rwLhNUcD2lnTLrirqf04/6XqX/hLzSg+AmdMfAGjZg6W6vf36896JNxMJ5okSJ7FAvtVGtop1rU5swu+GXrl4tfV/ENv1TGkOvU9fxb1Wf8YxpYOk+9ixcod9JC9ala6bwwdCuuV4/yRmVVNSljSpO6VS1X1/J2F2Z+2PVreD2vDp7NroPe5OEUBryYO+SV1Cu/uEF5JQ3Kc9AL+HqnvWUXNyiXNs4UolZ5lXXqdXmjel5e77kyu3SOzhx0o7oPu16TrntMTyzapi0fekcwtwC/4ZA4Al5sBHSbATk9CH9vnlsz3L8BSJ1Dkmlyjzb98nzV+oEXQGxqatL3vvc9YVLw7rvvOlMD06AyazS8f/mXf+k8NeBiDADFjACtMO0NMtlEBqhi8/v888+7BQOne/fudRpY3Jt985vfdF4X6GPGjBkp/71Upj/6nTRpkvOvi83u6NGj9eMf/9jNAUgmmJaY682bNzuPEd26ddO2bdsOeH42L9co+CeQQCCBlAROYxPDzkhSw1v6UDvwzn5P5896x0EZ2t3hs9ZqRA3Qu9H9fG8/8QK8Q2s2nLoxaarg5NDJNSYPVj581hadddlLOn/6a07DayYNvEQgU/cykdL4ehpJ7yd7NJH8zJ6ensLyn7lJaNA9mSS14u6ndzTunnY85/pa9b61KSlXT55Dpm8WGt6hMzBn8H6a7yi1n+47lrs9hxNY/u7zeqBJiLfWLSn5+WWbfo3MRs/aptHV6zXs9kXqNv5W3fPUUmdT6oFtZ8DbbtubRsMOj0Fk3Ig6V6LGWYmY4omwogn88XISZMhB66znVihr2LXqffH9yht5j84ecLWerH1PnFuFpjMSRaO5S4rtlxLNLo3EWz33ZthKRqVt20N6ZN4aTbxytvIuuNmBb+4lv9DgKYvVt3yxelbUOXtbzBsyKlbqnNJGnVO+Ut3KvY1mHFZB9KDX0+imQLe0PuXKzK/dBXgxa7CY2vyGGzPAtwSNb4N6lq5wWuDc4uUuL6u4Xt0nL1P3olpllyxTQfFC9Zr8krqPnKMf9L9R2aNu1bSfvaBXV72vX+1OeIetRDkJDw25QW9IiWiri3jMMO2tvYx0nKa+M9Iu7AEdKk1r9jneGtAbCJKi5a2vr3deGPr06eO0rtjWorE1DTBuydi0tmjRIq1atUpoeAHX73znOynIpC4a3srKSgetuCUjAKfA85tvvum0vBwYMWbMGGf2cMsttzjgNXdmuC773d/93ZREaMecX375Zf3nf/6nM59YuHBhqpz5r1692gExbtPwNGFrs7WmKgcXgQQCCaQkcBpfLnsc8F6rvLIH3AaoQdVodt/V4BpMFjY70B1RA/CuTWp526HXQC5IPRvdjuQA5Fo+110un5sE3s3OthR7UQ92PRtd/zVlFg1GDkw7H9fGPJlTD3g//SJgLxAe8K5IvUgAaUOmb0oCr2c/bfI9VHqg3A0UTxb522fU1pWeGuB7LxbAL/LqHHjR33o2jAcocZM3KZMG/hSRlwxcesDrQa9TCLvMhPM+AMByvG5bgoPNpeVbtmv45J8pZ/D16trvKpVe96Te2yftwl+tI+aoWvZsV7x1lxQFer0NXfvbQtoX4bBez9PB7pi07q2duu/hZRo++W71OP9GZV5wrwrGPaaeRS+pV+lCZWPOUFLvbGuzKtcqs2KNM3Uw4OXYYQNdS9Hgetc+37zlnkmD8wSBNwjnCq1e7vjisnq3yQ2tL/a9+AzOmbxMeUXLPfgtWaHMwgbnQxhfwH0q17h6vYqXKH/yi+oy7H79sP81Glw8XTfMflV1mz/Wb5u9NWLesad5v6JofN2z4fkQeVbEg0GrPaH09GBt/GXp7T6/e4NB/4jkAbH/8z//40wIsJP1a4C5ZtMaWlZAmACIPvLIIyktL7ALYLKh7b777nP5EydOdPXp32xr6Yt6V111lQNe+qAvm1f//v2d1wf6IdCOQBtck+H395prrnH3jEnEBOPMM88UGl7sfumLSBsL1AtCIIFAAu0SOI3/IABv7wnXKad8tkbVbNKg6jc0ZNav3M/CI9ioltyx7Wl4sedtB15PM+Zpzk696ySMztwkNqV1Fi+YtSVVxvXZl8/V4OnIcbOGz9ioC6o3acTMzR1Gyjrr1wO0U1X2tm5Ptu4lgY2DvCCwORD70potyrt+mfrd1uhtCqze5G1Sm77B/RR/MLnb8zi55W+fyw3Jz5ilHXzmknL1vxSw4a8j4PVMGoAdnIh5POu+ht2NB0FcehaVnwZeV9f+RnHDz+54GIiy6z2scJTTIVu0Px7TJ21S+TWz1C2vUAMuuF7L1+4Wfnqbo1I47h2x27J3l+KhFsXamhVta3bmEbGoaZmj2h3eq5ZE3MHvjhapdu0Huun+uTr30v9Tl/N+qh4j71KvyU+of9UC9amqdRvS8N2bU74q6cVhuTtu2HleKGt0JgimpbXUmSX4zBnM3y+wa5E8g14gGX+/HI2MnW9BSaPb2Na7fJV6la9yB2dweEbmZGyNVyq/Ai8TdcorfEVZ459Q91HTddaQG3Xh1Ad152N1Wv3OPmffi2Mrd4x5Iqq2WKs8T8np4NsOv85FnANheyDtqQdXPE8D5vZ2ncNze/vP68rAkvEMCElff/1150osMzPTaWxtPgayHBTBQROYJgComClge/uDH/zAmRqgvbWArS5mD8S5c+e6upThocHG/+lPf+qgGI8O2PdawP73j//4j1NeHABkNrXRFndpmDsAvATmRn8rVqwQp7Fhw/ub3/zGlQXAaxIN0kACHUsgBby9Jl6j7MqZwsXToJnbNGT2m+5nTTwz4KoJ6MVTQwp2nS2v9xO8p03jJ/lTMW5NmXh49s0H3gMIQC5ABojhPQDgRct4OMBr4OXqJr1BHHCdNIM4ZWXP+juQC7A7atZW5V2/JAm8m1MvFsMc8G5L3ZuMD5Z2NIbLO6Hlb7LbqGEzgV1/7AB6/S91SQ8Xo2e9odEzfDa8Ty/91KY1wNZBLP+YiwDL4++Sj3BdFb+mkQwHvN7mNb7UY4mowomQWmMRB6n31jyjrpkX6q57X9SuPYCuB7OAcSjcrEg4edxxQopGkj9bJ01ZAT4ic2yLJtQcluvzwz3S80s3q+ja2Tpvwu3qOvQm9Rh9v/ILn1FB8XzlFC11mlgAlRPagF0DXg6jAHTNDMFdJ2EXswbqGfCmp5g4oCl25g/ly5VXvsLBbD4nuhXWuejsfYsalDmxVnkVq5WJ1re0QfmVK1VQ2aDcokXKmfyCCoqe1umDbtE5w2/WJZc/pJ/P26DXt8e0V3KeNDiuOOpeSsJucxsb3DB7cNH57gWu0GK2u9ByjytpbuIBVsxzJef6aTePMDOJT6ftNsMGnx1/NX02ucCkRUwR2CD2X//1X24TmLe2dk8N2OSiRbXNYsAua2YTGhDKaWhof8mjDA0upgldunQRp6YByqZlBZiLi4tduyeeeMLBrEFv7969XT7jU8/cmH300UfuOOK//du/dRBNufWHFwc21E2YMOEAeKYO8wlCIIFAAp+WgDNp4Oe/gkk/VWbVDF0we50G1mzWkNls4sFP50a3KQX/sWh7Hbgl3WOZVq1TGOgARE66ug54DPw/naL1di6ykja6F9S8rrMvmyfsSJ38nIZ3g5MtLp6QsT96Lxho1DuLnku0k06ux/jZ4SUDF3AGvACwaXQPBN4D5e2Xvfu8dyp3ex4nuvxtHe3pYYNv9RZdOOtNjarerMF3LNM54+/UXU/XOqDytLft3hgc0zp45UAEdqEl/xiR2nXy0juCOKk5xEUDbhvokGYJz+QBXw1Ab4Rjhhc3atqU27Rl08dqTe2YiygaZyc8WmaOJvaG8bbQpQZyk3DToTwuhSMJhWLeD/0cYfzGRy166MVGjZ5yvzJGXK8eo+5V3oQnVFA0VzmFC5VZwrHEy50trh94gd2cIjakefALBJsNL1DraXMb5B1NzPHEXvROfWtUZvkKpz3GQ4SDaXwCl2C3W6ee5Y3qVdGgvPJ6dS1c6Fym5UxZoe6ltc7kwh1ljLZ40nzlF76s7hc9pDMG/p+yR/2fqm5/Wi+vfE9v75G2h6W2eNSdYheNtSoSbfFF7lsd8HrQ225X7Z4Bj8U9jzTgPST4fjHAa3BNasALyLI5jI1k5l3Bg3ivDja82dnZDkKxtwVuAVI2qeGpAXteoNkA8/3333eb1+gP21tsc6+++mqnneW0NLS/gDI+dA2i+QCipcVLw9NPPy1geP78+c4zxCWXXKKvfe1rzvYXOGYcxieYCcWNN96YMsWwedgaXMXgn0ACgQRSEvAB7zXKrJyp4XMOBN5R1Qa8fCHi/srcZyXtTR2YtH9Zdg5mJ2OdJLSmtHxJmaQg2LNzBHJNA+sB7yufIfAevpyHzdooi/bcDtRydtwXbaz+F52iNe8I9j8NvNtSJiPtwOu9aKRDrv/+i17f5z/+gQDfqTkN2l6Ad/Yb7qV48O11SeCtcxpe0+qSOvg1sDUNr0Gu5Sf/JHm3ntmDR7g4jk0CapKVARa8L2CLCsxu2/qW1q7eqtb9joZd/Sj2uqLcA95QGE2u1xUnjhEi4YTi/ApPgQ2Z1AKHQjhsTKg14dm/bv5tq+58eIn6j/+ZzhoE+E5X/sRfKrd4nrJLFyqnbKnvoIkGZRUtV2ZhvXJK2mHWgNc2qLm0hIMpGpL1kgBc3g68mRWeSzRnH1yBPXCdGyunjFPflip/Wq26ly1yxxNnVTY4v709Slcoo5jT41aqoKxBuRNfUd6Ep5V76WydM+Qm5Y++WdfNnK+mN/c6czZcmLW4GHFHFbeGWtQW2u8iHh68zW6elhdgNODFLMT59Y3FPZMTZ0fqaYWB5JR2mGfgtMQ8y+QLDG8gxM8pGAwyHGuw+1tvvdVBKFpbMxegDuW4Gfv2t7/tNLXWDvtaYBXPCgAx7sYI5NF++/bt+sUvfuF8637lK1/RV7/6VQfHgCtHAaMF5lQ20wxjt1taWurm8K1vfUv//u//7o4m5prNcdjw/va3v3V9MwZzp+20adOcbe+DDz7oxmdsg2GbqysI/gkkEEggJYHT+OO1Jy7ljb9OWZUPasTszRpYvVXD57zp7EtHzljvNLyAkOeLFNdP3k+hngbswC/IjuDjYHkHAla7HWtnbdhEZ5E67XDSPg8rJ7V+bBy7bwe99jpWdlRpUiYdtfU0vB4MG/AOvh8zh23uAAqDGtOs2b2XHnx+7QcgeL6Abfx2uXgaTOvTLxv89w6ZhXs0D3yREafntUfveXh9JuEY7X/S769fvjbu557ay0XqWXswDvASc2+oVZ/bzEuDZ7PqHfqxWUOr12nYzPUumnyOdP5+eXry8MY/HPmnt/XfdzYP+xxb2lm9w823eVp925jH58ryrA6p5bl61Rs1ejYnza3X+XcAvHfpnqdqnQcEQxmDTA9ekzaeyUKrk/pr1MkFX/IWgF2++D1g8OwZW1tDamkOOw0t9agTDrcpFjPQOohWka4BrzBGv2FUvBJH88aTUXEHg/ui0vY2acm6jzTtjqfV86Ib1HXI9coZM1O9i59Uv/J5Kih5RdmTFyqjsNad1JY3ZaMySte4mFO1RhklmCg0KLdkiXpV1qugnHreoRRmwpBd2iBAOGXa4I4h9jxA5FVgwrBMeRVLlFuxUNnlC5RTsVA5FYuVXbHMmUJwUltGGUckrxG+fHtVrXOb2vqW1Wpg5SLlj31UZw68Xd2H3qpBk+7TIy9v0mtvt2hXWNof8w7p2N8WUXPrfnecs4f7bNritLaQs6EGrmK8ICA7M/8lTWrhXb57gzDbXgqT13GAF8fKSeBtf7T2iD+z1ECXzwdrIOLPFm0sJ6ThH5c6Bo6NjY3OGwImBga0tKGOHRCBhpdy8mlHxP4WbS+eFJYsWaKlS5c6m1tsfjFzcC9skUjKjy8b5rDJxWtEbW2t8wqBTfD69etT/nrRMNsYjA08cygF7tRssxuCM5C3/wefmTCDjgMJnIAScMC7LybljbtRWZU/dz8DA7wjksB74XQDXs/JP54bzk9+GXo2vQcHMvuC7Cy1L+70tLP66VDAlzBmF7Snjb+ca+vHyq2OgZ6VH780Xfu5OWXDiwnD8QdeT+Puad47hhQPVLx5mXzwDewHXtaPjNxx0TO2pY6ONrnZC4LXxjtMww9Fx09+7c/ssPo8JPDWqc9tK31eGrZoyIz17sXusID3U/0fOL/2Fw7vs2fg7IfEjuSf3s6ei6WdrZ3n4Y+d1TvcfJsn9Rkb7wvegRu8QNlY7WYf1m878G7SqJlrHfB2mXCX7nuy1tnAGseQuusEwAP4eNrVVP5h/NFMB177aZkvdyL3RKtHHnBg5eRb/NRwTATgjcTagTcSkrD7bWtWLMSmo6iD3l0tcae9/igkPbtkiyZdPVuZw6/VOUNuVObo6Tqv/AUNumyZ8kqXKuP/s3ce4HFVV9eeEAKhhnyBP4EvJEACSUgIhA4u6sW9qXfZslxVZkaSC5jem3GRpks2LRBqKDHusnpvljst8IWEUIyb2oy0/mftM0cayzamGGRg5nmOz9w6d8691rx337XXzi5H2LwmjDQ2YFh2NYZlUedboxwbcjYhNGu9gK9Eeb1FKHQEWPeEX+320F+5La8MYfmbFOSa1yPMvEFaqHmTePVSEkHoZbU2tnBzE8YVNElxi8Dpr2Fc1hokLtiEsTOew+Wj7kFw3P2Yml+C5za+g/c7gN09AOGeDheffPqhgO6BAx+it1dFzKUAiFeS0snwN5+w69ajI74KvCTCe5ALhFvBLt0DWASC4y4XxyFn5Wuboa8DfW3wOqEt2UUXXQRah+mX1tESTvni9aUty7ithkzf/RB29frsNQjrfbLnfK7Hz9Uv7vtw63I5l3F9/eLxU7/L6HFeXp4s15/Jdbhcv3hs/pd/BPwjMDAC3zrg1T+4/DHmewVpqtc/2hoa9Dp6G91zvgY3Pe/Y9UMFvEeL8KrxGhgbBTQaZPn91Vj6Rni3yTwu43oSCe6/qfCVtBwMgcduLD/Hfg8BUjX+AxHe4xt4v9Gx8rkB1J/7bQReAoC2fOKPOuGBP/r6x15DDXvO11Cilw/8+VPvyAjypN3tAdhYqrWb/r37pVgDZRGUQXR6I6CfdEFKFv9rP2B5tgoxWYtx/cSbMTzuAYxMcWB0ziuYOK8M101/BQHZpQgvqJM25qZmAeHg3EqE5FZi9LwGVVTCpKq6sbJbuKlWGt9TDqF7wq+OAusiFCGmMpE1UNpA4OU0k+gGNMLVCMgqxw2ZaxEypwxRC5sRlVeNkKmvInzqa4jP34hhUUUITlyGkMQHkXXn31C2eTc+dAOfMuANRnz3UtgBT98+dHcz3Y06BqDzQJcEwiUYTo51Ux4yuPnIF5T2QRwyIBYZXj3E4JPxDUzzeuB1oaGTcgY6Htx33339CWAabrkOrxtec4RPTrNxuX5xGaO6vlDsC6Fcl5FdJrbp65bbcJr78r1G9T45j1FlHitLIPPF4yAYU85APfCrr74q87ku98d1+dLXu0z4//GPgH8E+kfgOwG8BDX9Az4AdDriNrCM6+jl+rG83u7Y9d808CrQ1RFD/T00yOhez9ffX6/PaUYlddRwQM5A2FURY66jI+J6e0m48y7X+x6S3g+8/df+lxl/3+uD5/Z4j/Dyx1zDiAZa9myH+6H3BQqu4/uSbcSpAPKIXhaTGegK0euWcsY93fvR3bNfFbDo7UF3bx863OrR/94eSMR36/u9WPZUBcZOvRdXjFmI4YlLMC7nBYzPfx2Bs18WjW9QznpJOgsyVmLMwjaEmhsxbGa5lBFWoFuPcFO9WIzRZoxNT4eaWO1NaYFDTTUCvozkEmzp88sSxoRdAV7KJMxlCPS20LwqRM6rleS5gBkbED67DJPMDZhsbEBk5jrE5W/C+KyXEJLuwlUTbsfI2Ftxf/FatP+7Cx8wyC02Zj3weN0sGLX1uFmuWEVnOU59vZSTsHeLltfLtl59rzeo7zOu3Ad1wbyR0BF/3/Pydb7nOScg8hoiJHL6ww8/xLBhw0TWQA0u4ZQvXk+EWC1nINRyG72dvg45rUGW++M0t2HPSDCX6f1xPqGV++ZLwy6vUy7Tn8Ft+Nm+Ly5jFJpyhtjY2P4oM9fjMu5Lv3gc/pd/BPwjcPAIfGuB98v8uHMbDWy6/7L7OfJ2Qwm8vnCvjkODbP/xegFRA6+er9emO3LPAAAgAElEQVTz7bmM46Sj4fomgfO4jOvq7YesP06Ad/D31yCpey110Ot9fdffFzsnA8enzvXxCLy+f7IICvxhZ+OPugYHrsP3bL4/9r7r+u5H1kefyE6JIyK48MpKlZMDgYRaVSKfKtTQ2bUX+/bvFn0wec+rSsWn3cBHXUD9zj1Y8MDTuDIyB5ePXoDIGS5EzX8VkTkvITz3dRWNzavCsNllGJnTgFHztiA4W5URjjTSR7e2P6JLCGbSWZi5FoRcAq9ycKgRaUSwkbKFWgHm/sQ4RncFeEsRmLdRWpBpAyIKKjCqQEFv6NwKRM6tw+i5jRidVYvxphpEZq3FqOzXMCb7BdyQsBh/jMxDrHEpVq5qxke9wO4+gBZmlDn0j00fwZeJfftV6+sQjS+hV+4e9OCw19reXp4vNzx93WDJD5aJ/rqB1/da4DnnNK8JAiWBlC8CKEGXPZcRPqnn1ZXQNEzqa80XRH1vqPT1p69BfhYb1+E2Gor5mfwcztOwzHX0vrhM74O9jg7r7TQM6+Pn+myDX4O/++Dl/mn/CHzfRsAPvId5zKuh5Mv13zTwHgzxA8f8OYHXG6X1BV0NsoNhVwMvP0OvP/B5Xwy0jtl2fuD9Sjcd3ybg1QChYYB/rH1/1PnedxmX62nf9fQfecKb5jJCbzclqb19YnXGZcpUrQfdPXu9j/MV+IoW2d2FXg+jfweE5yh3+LRLaWCZ2Db31hW4ekwerhh7EyJmuhC7aC3Cs19FYPZaUJ4wZmE7QkxN4uZAl4ZIU4147vK90u5WI4yaX2+jNZlKZFPASz1wiLEWIbmNCM1pkgQ1BcWEXg286xFoWosgM+3TNiAoexPCc6oxJq8JY81tiMhtQEhOFYJzShGasx6jzGsx1vwPBGeuwA3xD+GGmNswdaET61o+EKu5fbRto0Mc5R1dTGo7ADf2wI1Ppec0xwTuHsggcnA5sGQxgV4CoEeA1w1Cr3LQ0Ofj6+gPd975Ofpa4fXBF/vBoMn5hFANlnpfGi45n8t95+tl+jO4TH+WXk8+8DP+0dvofehVuW8u42cyGjz45bvd4G0Hr+uf9o/A93EEhhx4vyr4DI6UaRAb+CFX4KfX0/1X/dwjbz8EwHtYm7CDAVSPS78UQYOit9fLfcdN6Xa3IMa1BTGS5ObVCbOKmY0JbsdBlFd/j/4bFzX+35SGt388+z//4HEfiOwOvi44rW8cfOzsjrgftV99/er+yNfh4OM4/PTA+T7+I7z6B52970vP1/MGT+v5g3vuRbOYsBmjmL0qkkl/XxqZdXbvl6IMKsrbiT73fsDTAfTRxWEg+ks8pn/vAQ/AJOCWNz+F64UaTJ51P/4yLh9/GrsIkXOfQtxtlYgwb8CNM9ZgxBxqbyvFlUHLEkLN5ZBSxaYyBBpLEVZQidB8Jrypam5cX5oUsqhDSE4LQrLbVZ/bKFFg0fGaSxGctx7hC8sRWrAeIfkbEJpXimDjJtEVS1KduQbhBTUSFQ7NL0MYE+JyN2CMaT0is17FiNQSXD3pftw4+Tbk3/8cWt7plnLOH+7vxge7P0Y3utDVtxddfZ+ip3ev+Pn2ejoBd5fooCURsIdVQLQjA4HNIzcSLHihZBIKOAefm2M1PfhaGbxfDbqcr2FVR3A1DOttNODq6aP1h/tszvPdL9/rxs/ne9/tDveewMuXjhhzHd0Gb3+0Y/Qv94/A92kE/MB7FMD44kAxGGy+ZpcGr6XYoWB1MOBooO0HNA2K3up41OuqdQaO/0jAK2V7/cCrIqt6HI94HQ2M56HniNppr1b6qPvxA6/+UR/8B/pw8znv87zUQ/peeVSvoVfBrpIscHmvRCL5+Jvhyh7ATahj9bYuoI+WVPskuY1ljz/df0A0vowFf+oB6t/4FHdaX8HwmAX4y8RbMCLNinHmVzHatAZj8jchsqACIaZSicAGGzeC4MkWZNqIEdlrET6vYgB685Q9WRj9ePOYxFaD4OxmhOS0DQAvpQ6mKq+0YSMCjGsQmLdeoDe0gPvfJPunjVlgnooEDzetw7CctbKN6H0JwtnlGG8uR/jMlzB65lO4ZsJtGBl1E5Y8tg5v7+4D63t83N2JfX1d6OzrQFdvB9x9nRDg9XQDbNT6aos3celQVdsYOVfYqwQSn+c8Hct19PWirxHdD/4MPV8DqV7OacKmfn3W/g4HoIPXH7wfPc1t9Uu/18ekoZfL9Ty9rt6/nvb3/hHwj4AageMeeOmZqqN12gSfFbQ4L9ZG54BtiC/eIe+Ti7cjwdqKVOcWJNnawMplhIxEZzsSXO2Ic7X3a3jpjZvs2P6VHgcfHoYHA87XC7yHQtTBoKuPUQPvwT0LYzC6qMaC1cgY8UuQEtKt4swQ59oqGt4Yx9Z+uyqBNGu7jDH3pz9jSPpDQFGNv75mwu7+el0ajvad9TVLj+X+a9ExUMr30BuQQ90v9D7UZx16fR3tGD5r+bcpwnvs/2grna7EcqntZDCyF2BQkk0DsJY3SJKVRHa9Hr2ebgE8+tMycYuJWIzSuT2qWtt+N/BxJ/BJH7Cm6X1kLHLi2skLMTzhIYye/biU/g2Y8zJCzBsRmLsOQcb1CMhdL5KEsYsaBEADcjciyEjQrRTIpU2ZSlRjEYpqBOfUiayBnrsicaDMwevlG2TaJPsKNm+UaC8jvoTf/pa/FgF5axBgXocA8wZJdBOdsKkO4cZG0RQHZq7G6OzXMT7reVwf8wAuC89GdPZDeGZjOz7wAB/2qNj33h4PDlCH2tsj5ZzR1y1R8L5u3gzwxoCJbrwNUAUpOrsoZ1BSAt/zejhA5HINcbr33cb/3j8C/hHwj8DnGYHjHnj5Y69/sPV7DTNx9nZ51M7H7QnOrbJeiqNdfHnpzcuWaGtDnKOt32WA23A/iTbC3YBvrf6Mr94fCiQ8Xg02x9qH98sB74Arg64Ax+MT+HG0QvkrE3hbvUlryoOZXr9qPSVn8PU//urj9iXBeYiBV1+L+vtrOI2zbQabnHsm/9nb+psGX7kRY2RYfwff3hsxlmvVW0RD7VuBM/ehIPpLjpt3/37gJdYqiyzCltREYEU3Shpoz+tNTlPQqx4dy0Kvk4NKuvJGLr3VxcSFgJpQyiN6gf1sAN7vBJ5aux1TZj2CyyONuC76fkloG12wHuMXlmPMggoE5azDiLlrwCguo60Cr15dr7Yzo6WZlCw2K8cG7dt7UO+VPgw4N5SC4KuS2dYjIJ9tLYIK1iEon4UrShGUVy4ljUNMdeLhS4AeRfjN2Yjwua9gUv4rGJ/zBK6afBtujL0Vs+5YidK2/+Ld3cqtggKPvT2Udqgbic4Ouh10Ab0dSgpC6GUiYA8TtAail/qHajDM6qimXu7v/SPgHwH/CHyVETjugZfAwB/6BKuCNBWFVNpH6hhjXdsxhT/eJapqGSs/JVlakUarLVuLwBvBjdFdHeElnMhj+e9UhPezwUeDjboRaO0v1pHo2ok45y4FXRwzRwuSna3SEp2EK2o7qd31KSnt1Z4ygq4BT86RF6I0/H0jvYbE/s/+ZiO8OmKuoZVPDqSynnM7EpwcM6WN1YDKym6MoCc7+eRC3cxpxwzfXo8doZlPNJKLeUO3WZ5g8Fxy7Hkzp9f7sr2+Lrg9j/V4d2n4Kn/sDt22F+CjdrZeT7+lFqGX1YcJvYdrVEv4LmcSV49HebPyUXdPT5dUeSP4cn6Hx4M9XX3Y3a2qWu74bx+WP7kRYcl3YljSUvwlzoaAzGcwxrwao8zrEZK9DkFz1qskttxqZV2W24Cw3AaJvLIPzWXCWqWAakj+Oump0aUGWBWpqFQev15YJjhT2yt2ZXmlCBA5Q6lX4rBJ9LvUCtPGTCfIEbBHL6hHcO4GjM4rxbj89QiZ/TxCZz+D4MzHcOWkezF++mIsfbIKlVv34r29EI1vB0vtspgafRg8B7xOF6xK0Y2uzn0yzgzv0r7M90Xg9X0dKdqr1xm8vp7v7/0j4B8B/wgcbgS+NcCbaGuHhl3CL6cTXdsQ7dqGKOdW8NE74SDNsQWpljak2tuQbPdGK11tAruxxUrSwB93yhnYjv0j+aGK8H4O4CVo2VVLsbVKRJewG+PcCVbOIlAp2G0W8GWkl2POKHC8bQfi7TulUpwA3TGKMH5ZUOvf7jgDXg2+hFcCL2U3BEkFt7w2WqVxbCXq239DcXg/Zf09uT2bBlT2fuA93J+0LzLvYOBVdKucsgi0umnoJY7JY3gfEGYEmM4O3T0edHXTfkrZXbkJve5OkTkw8a3T3SMOB4yCsv1nP9D4didS569EQOpSXBf3CG5ItCJyzvOIuakMY81lCJ27SSKskcZ6gV0FvM0IMzXKNCGWEdrA/FUIzluLEPN6hJo2Ijy3DOG5FYgwKssz2pZp6zJdlCLQTPhVNmbaw1cS5nRyHOE3vwKBjO7mlwv0Bs5Zjci8jZiwoByRpnUIm/0C/jTqTlw56iakz38Cz5X+Czv+C7AgxyduZWXGmC7Bt98PQyiXdxOUQysvX56xI8HrkeZ/1jZf5Arwr+sfAf8IfH9GYMiBdyACdmRgY/SQYJri3CagG1fUqiK+xdsFdqNKtiOaAOBU0d10Wzt0SWRG1OIcLSJpiHa2YbCk4dsPvIPGTQOg7n0fXXuBN8XeAjZCU5R9O6bYdyLavkWkH0mOJiTZ65Foa0CitQlpzu1Ism1HnFVBb4JzBxJdO5RMxNrSr68e/Ghfg9rX3g/6nhIFFd32Vjm2r1vDyxsCdVNAPTR15VsQY22XFm3ZLNebyG34hIHSGmsTeE0m2pulJxTHOXcgzrVdGqdV2yoyHY4rdezxlhaJ6vIJhkhJbG1y/r7q+GqA5n6+lxHefmMyHdJVwCtARigj+PpEHgdDL4OUkspGuy5qgD3K51UXZOjpPiCFKzy9Xeh2d2H3gQPy2J8guIcetwCeWrMLMxY9gdCk+xCQsBgR0x9HSMazCJnxGiYW1GKMmbKCWvHsFc2uif689QjLr1Z63PzVhwBvRE4FInKqEJFTLY3bS1TY698rPr4mFrNQNmjBxnKoxhLF5RLxFdBlIp1UcSuVwhbh+ZUCwSPmbhCpwwTj6xgz6xlcGnE7Lg4yY9Ydz2JV0yd4cw/wUZ+K+B7gd2UhBtqVMQnQ04OejgP9YfKDxtfHcUD/DHM5m0hOvC4Gvtvo9fy9fwT8I+Afgc8ageMeePWjcg0WjOzGW9r6I7xRzm2IKtmBKEaAi7cjsagFUwnHRc1gFJNwEetqFeCNcSkAIZgkWlUJ3WOv4/2mI7yfB3gpB6FcoVVAN8XeBDZORzt2YIrrDZGGMLGPwJtib5DlhOI0xzYkWbchvmibQG+CY5cAGeEtxtZ0EPB+Vfj6UtsPMfAOljT4yhLU+y3yJIKOF9HWZkQV1iPOpiLoIhk5CvDy+o+xtPYDLxMyNfAeCw319xt4GbHV6lyfP5OaanU415d+vVFevYjASxUwe87rf8mE0rIyqU1hMY3OlLuv3o7RXgLhm//theWpckTNWIzAmPsQnubCuDl/x6is1YjI2iARW3ryhubRh7caAaZKjMjdhMD8TQgsKAVdF2grFm6uQKSxHKMpR8itxKjsKmkE337oZYJbbr0qXCFFLapA+YKGXkZ8CbncX0RBlViZRc6rFrnDjbPXiQvE2JtbBZYjszcicu7rmGReg5DpT+CysXcgIOUB3GZfj43tu7H9Q4/olzkC+7s9SvrRfQA9nXu9cpKBktAcO0ItHQi05yznadjlvMNZd/WPuf+NfwT8I+Afgc8YgeMGeD8r0ssoF4FXw4XuGa2d7NqGKcXbQRcBAlOytQ3pls39EV4+Qo4rHiRpEB/Z7UixU9JwrBPXvmHg1cA3qNfgpSDyUOBNs3mB17kT0SVvIa54hzhZJNkbkWyrR7q9GdOcm5Fk2YxEy1YkWFSUN862Q+QPMQ4mA7YcpOHVNydfClz7NbiDAP5o8/X37l9Pjb+OOH/dEV7egMlTAvs20e4y+s2WVLxTGqO9OsIbY2tBjKVRbsJSXNTwMplyG2IofXB4W7/fsYq4RhUyEtyOFOcWkTAkWpX+mjdzfC+f3f/dv+DYiRZbXRs8Z9+3CC+ZlOCpWz+w8o00r+RB25FJT7xVCVdqewIsofkIwEtXB+X3gK7ufVKiWEE2xNGgD13ocO8XuQOhsHHHHtyx/HVMmWXHyMRCDE9egZEZzyM4e7VICyLmVyJkXjlGmjdiWO4GBJirEJhHEK5FeF4dIvJqMcpYpYA3uwxjchT4js6pRmRuNSJya6RsMcsVS8lic7UAtG/CW4iJ5YpVC8ktR3h+tZRFpgZ4zE2NCMqrxA1z1yNiXi1Gz2vAmLwqjM2vwGjT66LvvSbuEVwTdQeiTcux/JkK1O78CLspcWBJ5s4OFfHu3gt31x6J9hJw2XQE90jAq5ez90d4P+NX3b/IPwL+ETjsCAw58Go4EuD1esoOhl+doKN/7NOKdyDZvgNRRZsR7dqOKMdmpLg2I6WwETOsLchYVo/pBAOrkjNEudrA6C4hjfCQZG1HsmUrkqwKovUxHJv++AJe0ZJ6gUhHeZWOl8lTmxHl2IFo506lM7U2IWl5NVKXlSPDUoGZjjqZTrQ0yljS6o36aYmwUxbBiHkRo4/tiLVsQZx1q8CwPpeMGDNhUMsMZHw1oLL/CqDWv63eX/++vmHg9TooULvLJwfanSHG2ga2KcsbkMhIubMF6Y5GpNsaMNXZhFQnr8UWxDjaEe21fIuxb1PjRxcRSk5szYh+pBwZjlbMLNmC9MImJCypRZqlBen2ViQUMRLforTqYidHaPUWB+k/5yrBU2uLpfcZq+9zhFcB62GAl38qfYGXSW390DsAvGq1gQgxN+l/9ROwivJKoQqvfEKS2cTHlZHMTxn7REfnHuztYCkHpX9d3/QRcu95EcPj7sOIlOUImP4Egua+hGDjKoTmrUPYvFKEza9GgLEOgcYmqdpGmUO4qRYRxkpE5JaB0dfRuZsQaVQtwsiENm8yGyO75hoEGSu9FdwYwa1BWH6t9LogBv1+CcDSmysRmFsmkV7KKUZml8q2lDewoEWYeQPGLliPsfmvYViaBdfG3YcrxuYhOW85nvhHA978sAv7PKqwB+3LunsOSIIfq4Z1ddLPuFtKDyuXC1XBjGCrmkogpLqEmmpJHPSRmvSPO9/osde99wbloHX8E/4R8I/A924Ehhx4GWFVUdbNSnrgahU4FX9dh9LspjjakOpoRqKlXmQKqY4dSCh6A7FFbyJ55VuYvKQKmdZaTHtoDfKWlSH30U3IWFyBaSVbMKVkM8YWt2CSq1UimJQ7ZFjbkWbdDFqYfdUIWT94+UDEAOARvr5eH14d7R58HBp8mIymHRaYgc9p6kxjbcpXdxKT/1bsQLq1BWmLK2G0N2Hu0o0wFb2OPNdqTLevRpJlNWaUVCCtqAyp1kYkL2/ELOcWpC1tRErhFiRZdiDJ+hbi7W8hxr5Lou0xzhbEOBsR46pHnLPRm6xFCy5am+l2DKB3iIGX0o7EYtqOtYubAiUH8dYm9X0JuSs2I2F5BdIfXYfswg0wFpUi/eE1mOrgzVgbou2tiHa2I8q6BdEWJmZul4TLVEsN5pY0INtRiZlL1mPuo2UwWxuQY23CtKU1yLA1IsPZiKnWaiQtr0TMsjqkrtwlkeKJy1uRWvIGEqxe+zjbNtFhS/KhuG1w3HltqsRFQi+vn+9bhJd/7TUTsT/kJTO9Qt5BsoZD1v3MGYP3oVdW88XD19Mjj+t73L1SuGJ3F/CfDuDFTdsw89ZiBKXch2ti7kdw5hMYn78GAbNeQXBOGcLzWxFqakOIsRmhxgaRJrBqW5i5FCHGdQg2rkaoeS3C8tYhNI/2Y5ukkhujtEF51SJroJ6XVmRBBOC8aongikVZ/iYE5ZciKJ/bqO10lbcwo9cSbV6VOD6MMK/HyLwNCC7YiIh5GxGZvxqjcv+O8NlP4IqJt+O6KQthvO+vWF3/Nv65pw8fuYE97l6VJ8jiFJ5u8TJ29xyAkoDwZqBHyhjzNBBymePGXiQkDL7LMHrHlhO6cZZ+z8xD2cK7nu8ifRr8vX8E/CPwvRiB4wp4GQ2MGQS8BLr4okaku1oxraQVybZmJBS1I6nobSRb35KkqmnWOqTf93eEpN6FERPyERhzKybl/xUZzmZMWdGOcSu2YqJL+cpmWFuRYWkRycOx0EAOBs2DYXfogZdRQ/W4fCdiHTtFs0vdLhsfpSc9vgtxlgZMXVKJrCWVyH2kDNG5DoQl3YQbY7JxTep8XD/jTkxc5ETSAy9ghqUa0y0NmLG8HpnLm5Fu2YYkyy4kWt5GnPVtxBJ6ndsR7WxBdHE9Yly1iHPWDwLenV7o/XYDLwGRutzkx3YgzroFsYVtUvRELPEcTYiz1iDV1YAZzhqYCtci9eZiBCfdihsSb8eYW18UJ4xoRzOYTBlDOLXvQLK1HelFDZhtr0Hm0tWIv/kxBKXfhxFxd+PGmHswPHEJAmbYkfTAWonCz7FXIcNShWRbo+ipox3bJBFRbip4U2fbghSrAl66bPDmhzeT4hbhVImL32fgPR7+yvd5etHr9sjjfSa3uXu70NXbjX0eN/b1Aq3/3IPHVrUhdf4K3BB9L0am2BCS+TeMyl4H5eDQhIDZVQjKUt691N8G5Kz2lhReJwltdHEIMq+TCm4sSMFiFmI/5k1ik2jvIbCrgVdphJnMJgUwTNXQwDvCVIoR+aUImFeGgHkVCMgrQ4BJ6X/HzCsTq7UpBf9AWKYDV4xbgMCERbh5+UtY3/oB3vkU+HgPC1Wo8rpKKuJGj5uJflQ2U+rQPcCuoCPGgDeyMnkYAFlZUU+yF+jlGyKyjswPyE+42P/yj4B/BL4/I3CcAe9mqe4VS2mD99FsEjPYi1rl8Xk6bcWKGpFo2Yxpxe9gqm0HZthbkf7QKvx5bDYMZ/wehhN/BcNpl+KKSTdjhqMRsSu2YcKK7ZjsopayRR4pTytqFOuy74SkoT+yfLB+U4+f7vs1olor6gUfSegrrEKOrRZz7l2FkOh7cdb5o2H48WUwnHgJDKdfAcNPr8GPfh2Mi0NmYcr8J5FtrUPaI6VIeqQc01hi2LpTRdwtbwnwxrp2SZQ+ytlwMPBKNJbR3e8Q8FpakfzYLpF0xC6nLd42pDvbJekv0VqNjOIGTFu6DqNmPIhfXjEJhlMvheH0y/E/I2ZiekkrYu3NiCtRkfcU+1akLW9CxrJqpD34Dwyfei/O+PMkGH52Iww/HQ7DT0bCcE4YDL8cg9+OyUNIViFmLl2DuY5qTHe1ILawSarhJRTvAh0ieLNI4E22sfLgdrGV8wPv8fXHXR7Re0vV0r/X7elEP/R63DjQq4o67O4D6t/y4G5HGUalL8H1U+5DUGoJRs9Zg7A5pRifV41x86ulcAUrtoXPK5fqaSHzyiRKS//dEBajMCmLMjo2hBnrEOqtyiaRWylWUS4R4JC8Q/uBdbz7MVUjIKdcosIhBbUIzqtFgLEGgTnVUvZ4TH69SCpCZ7+CUXNfwPjsZxCQvATXTroFsblW3O/agC3/7BSop3fvx/t7xLNYEvuY6OctVNHdtR/dXR0qoU2q2A0k/ulkQQ2vYiXHiX7g5fn2pWC+97/8I+Afge/jCAw58A7WFOoIqdLxevWINPC3tyPO1ooERmedWzDdtR1Jj9ZiwWNbMSHXhlMuHAHDj8/HyWddCMMJ5wqcZa9gwtobmFTyT0lsYwQ51VaPNEuj6HcTrTu8BRcOhsVDo7ZfZDmjur7t65U0HOlYNejqnlIGJvZp4FHyBvoSNyLdXo05j65DYOydOPmcUPzwzGH409WJCJmQj4CoW/H7gFk4+efDYDjjL/hDeC5mPLQaM5aVIr2wEtMchLsdSLQS+hTw0teXmuloV7NEd5WkYbO3otgxlDMQ9odQ0qCu0S0SMaVtW4pjJ1LtW1XCpL0J8cvKEfvwWlww1gzDT6+C4YxLYfjRhfjh/7sWZ1+XirkrlDVZ4mM7lcPIsgZkFtYj89FSDEu5B4YLQ2E4+1r8/Ko4XD/lFgQl3Y8/ReTB8P+CYPjZdbgweDqm5DuQY63ATGcTopfWKkuz4l1iiybJnixSYVNSEkZ4eePDa6LfD5juHd9jScNQ/9EXNqOlGUsSu7sFeN2eDrD1eDrlMT6TvT7pUuD7rwPA2vqPcGfhWoxOW4zro5cgOP0JhM18DqNyX8O4eRsRmL0aN8xehfAFNQjMq8ZIU5WAaFheAyLMDYhgAYusOmVXRk2uaSMiGJU1+RatGIDa/oQ2b/U2X/ANM9cK3EpZ49x6hBqbpIXlNokrRMiccow1VWJifjkmGFdjTNbziJz5JEYmF+G6KXciNd+OFa82Y8u/PHi/AwK/1DG7WQikl+WJ3YBblW7W4Orpc0uiYA88ByUccix926Hn1g++h46Jf45/BL4/IzDkwKuhQYObTqKhKT9dGGg3xogVNY7Unaa6tksCVUpRLZIf3gCzpRYXDM+A4aSL8OdhY3HeRZfBcPLPcf6wZMwtpu3WO5hQ8h9MLH5HPHsT7Y2SDMQoIzWn8uj3CFFSfUxfrPeF3a9f0nC0YyP0cB25YWBZZSZW8ebBqrSm0x9rxSx7OSblFOKMC8NhOOnPuDosF+Z7XsA9JTW4yV6L7AdXIXBiHgynXArDOTciaNr9mPbI60gr3CRuDoxMJtneQJztTcTY3lRyCdcWxNIdw9mIOEez3ASIXtumfH2Pdtyfe/kQAy8dGaZYCfM7MbXkHSQWtSHN2oo0ewNCb34WhiuSYTjzGvz8T2MQPm4WDIafw3Dyb3DBiFSYSxqQxMS/4h1IL96G5EerkW2tQeItT+P034+D4cSLcUngNKQsXIm8Jetxu6sB+Q+vxrApC2D48e9wws+vxl/GZCN32XrMtNYgbkkNkuhNbWchlu1yntX/J3VjIDc7/cCrbOr0/ze5Rr53lfMMC/cAACAASURBVNaG/g89AU1HKRm37EMPevu6pDqZp7dTAJjrdHuAzj5VtGJPD/DGv914cf12mO55BmHp9+O6mLsQPqsEkdnPIzT7dUkyCy1oRoCpCSNzWxGQ04qQnBZE5DQhIrseo+ZWYVRWOSJyNyLctB6RxlKBXia2qaQ3ujmoFmqiXldpdhkllpan5A2RefWIyK1DcFYtQrLrEG5sRoSpFWG5zQjOqsfY/FaE51QjZE6Z2KuNNTKRbi3CZv8dQRlP4IpJd+H6uNsx846/4sXq9/DuPuBTD0THzOj3/n170NfTKeBLAGYEvBdEXTe6ejvRAzd6WOnOpwy0r+sGx+7glx96Dx4P/5R/BL4/IzCkwEvYjXWqxkgdgUg9fqXPLm2v2sQfNsa1C+OXtYsGlcUnkgrrkOmoxlxbOcZnu2D4n+E47Zc3ImvhgzjnV7+D4dSf47chaTA+3o5o27uY4PwIk4rfF0eCeAc9UJlUtAtxjrcl4vm54epzgfHxCbzaPYDgyybFDIoaMMNVj4xHXsW1E7JhOO2POP03YUi96THkF23ErMXrkLmkAmZLNeYsWolf/CYEhlP/iN8GTcOMJaswzVomEXOJEHpvIAR4mbhGe61iFltoFvsylVyntKQ6SVFFwr9I9Pww6w4x8LJIBG/GEh27kOJ4A/HLWzDdtVWS+66cbcNJN2TiosgcTJ9vxaI7XTAYfoEfnvIbXDIiFVkcv+J2TLK0iQwis7AWxqIyBKfcDcPJv8OPfjkcCXkWLLKXIfuR9TAtLcfNtkrkP/gyzvtDOAyn/AZn/TYMc+57CVmWaiQurZHEOe4vvngboqT0sLoeVaRfWZ+pCK8feIf6zzxhTCdh8b2yK1PAy8St3t4D6PV0golcnZ0HJHpJmOvo7kNXL/BJB7Dzv91Y8VodorLvx9UT5yNoqgWjsl9G8Jy1CJzLKG0bwkzbEJa7FcHZrQid24DInDqMza3BGCOrsa0X4I0waeCtlApttC8jyFL2oJo3SY3FKgi9ArzlCM9VxS3o8Ss+vzn1CM3xVoYzNWLkrEopmEF4Fss0czUiTfQK3oQI41qE57yIkTNKcF3iw5KYZ7r/GWxs/Tc+7Qb2dgBuD8ByzR0H9klZ4t4+Qm8Hutz7WbgYPR42VcKZvdvTJ/CrVbtqXH3OtBAwoVev4bPM/9Y/Av4R+E6PwJADbwwjgU4VgRLgpWWYtU1smQi8CSt2YZJjJ8YXbkOc6y0pOJFuq0PWyiok3/cszrwsHoZTr0Z4zHzcseRpnPPrP8Bw6tm4KDgesx1ViLa+jYmOjzDF9R/EON4AI7zJjjpJ8GHE62BJxWGA6nNBru92xxvwtiuJhZXV6VqVl6vYi7XKjcMsawUSFz2GX105DoaTL8SfR83A/OJyZJfUYKq9VmyvZlprcfPSVfjzDbEwnPQbnHj+cEx/+CWkF65Dsr1WzlW8bRvibLskwstxJvBKoQ9xI2ByFO2x1E0N3+tH6l8ZeocYeHnTxmgqSy9TIpNo2dKv4Q279XkEFTyG2Utex+3LX8XC2+wCvD8+8/e46IZ4FKyoQ3JxOyYUtiDdtRmzrbWY/cAr+EPgVBh++BtcFjQVc+59AQXWSuRYG5BVVIcZ96/CLZaNGDZ6hgCv4fQ/Yvqix5Fnr0bKkio511GsmleyTaoPanmNlrYMSBp4Prz+zH5Jw5D8kSd7sTobo5Oevl5ptOSihpegC8Ju1x7Aw6pkfLzvjXCKtrUXXR439nrc+HdHN2p3fYAHVryOUVMfxJUT7kVA2pMYlb0BlBSMMrZgjHkzQrIbEZpVi9HGBozNq8coUwXCjaUINZV65Qy0LPMFXl/oHQBeSho08IbllEOqutEKzViJkFzVKIOgxVlEfp0kx9H+jC0wuxwB2azaVoHRC2oRbKKTxGsImv00bkhZhqsm34bRGffi0ZUbRd+7u0ONDyG/s0tVaiPw7j/wMTy9B6R8s4dSEHe3cnVgcYq+XlVOxFu5WEOvsK6caT/wDskF7/9Q/wgM8QgcF8BL6CV4Jtu2iT8ugTfJxqIGbQJOE+10FXgHaU/8S8z2Z5U0Yo5zHf6SMB+GnwfizIsnYf6DryB7kR1nX3Q5DCefhYvD42EsrkR80Q7E2D9ErPMDxNvfkKIKafYalVRkpxetL6wei/fHH/ByHAm7bBpy6PNKWUiuowaxZit+8usbYDjpfAyPn4d5K6sRv3QjEl2tqoDCkmrc4yxH5IS5OPmsy0SLOuvRvyPTvgnJ9mqpHBZrYTR9u0TNeWNC4KXlFm9a+PnfReDVMgDKGmIsLDyxC2nOnaIzZ2GJzCe2IKmwDPkrazB/8YuYfwcjvOfihNN/h19fF495j9FZoQ2TrZsxzdmKOZYKpC56HP97+UQYTv09Rk42Y5GrCmn3rUZGYTNmOtqRePcq3L6yDnGz7sWPfnIpDD/4NdLNFtxcXI+UR8vlhoY3ibGu7Ygt3ipJoIReJR1Sjh0qwusH3iH+2ysf7wtj4ujLogq9bqVfpVWXuwO9dCyQam2dEvXt6+uAx7MfBzo+wQHPAezxdGGPB3hvH7C24X3Mf+Q1RKQ8iisnPIiRqU9IJbRx1OdmlSF0bjlGmWsxOq8OwTkVUi2NBSXo2MCm9bphXicGLWuQnglvbD5aXkZrKYMIpxbYqBortQWby0A3iEBjqXqfV4lAc4XXw5egW4Xhc0vVvPwKsU6LzHsN4VlP4br4+xGYeA8ybnKh+MV6vPVfiD/xR5+6sedAl0TCRfbh7hA7M3jc/bZmlD309TEOfrAbgx7ngXOupQ0Dc/zv/CPgH4Hv9ggMOfDGlVDruVWAl5XPUm3bkWKjR24bkou3YoqtXQojJK54D1GFW5CwrA6znNWIv3sFDL8NgOHcYIzJLMQty8uRd9+z+OlF18Jw2i/w64DxmPdYmZQaTnb9Gwm298U+K62wFtOY5FPcgFRbrZTXPbbQe7wArzqOJNcWSfZjGWGBXJd6lB1noY9rMxaUNGHKnCUwnH4JfvTTPyAgbh4KXFUCw1Kq2fkWZjm24uaHVyMmfiFO/cllMPz4EiTeuhJzV1Qjdvkmcb+gmwaBl5IGifCymAJh1+k9ju9ghFeuG3H/8Hod23TxiVYpuxxjrUPM8nLMcVbiFucGLHjgaRgMvwSjsuddnwLTymbw/EwsbEKqpQ5Z1nLEmCz42SXUUv8OEcl3YJ6tGrNtzYhZ2iI3hJm2FmQvL8fMm0vwo7P+DMOJF2JC6u0osJQhs6hanBoSV76BSbbN4lAijifieqKLUuikNVWQQt8AaXhnMiOt7FRE+OB1RLrivUFkEiRlMmku5UjBAhk3zFqCwmfLBE50NK0fNPqLNxA0BpKLvtt/Xo/+7VQBBV1IwWdg9MD1MXlLATAhb6B1oA/d6HZ3obtXSRwO9Kkyxe/uBp5Z3YKZi5wIiLsNI+IfRGCaAxFzX8YYJqgZKxGaS3CtR5C5DsF59eKwMCKnAgE5FYic34DR8xslGhuUU4Fws5IjEILp6sBpvmc0V1VkY+Ibo7aqBZs3gi0wj61Umvj6ep0fCNgsYBGYy89qRMS8Bon4BuWsQ0Te6xiT9zKCppfgysl3Y1jMnZhx0wqsq/8vPuoEPu0ADnT1QhergMeDvi46OrilXHF3x57+Ms4qyU2dA+3eIEUrvPIR3+VHP1Pf7BoDBTd04Q39P+qbPQ7/p/lH4Ls0AkMOvJLN71TJVHzkTRslKWdrbZXIoBSgcO0ES9pOfoRShs2YbdmAi0dnwHDaxbhklBlzHtoo/rGmh1/Hyb8eBsO5l+GSiDhk2f6hKlLZ3kG85f+QantXCixMs1RhqrUCCcvLvnfAm+xUcpHYogZMczTBaKnC+IwHYTjx1zjhzEsQnHCzetRuawQ9XRNK/g9TrVsx7/5ViE28BWf9/GoYTvsDxhkLMcNairjCMokEU8tK4I0W/e5OVXyCkhSHqrT23Yzw0kaP/s6MoBIkt0t5YZYSjnO1SyGVRFczkpdtRNbi11DwwLMwnHwJTjr3RvwqYCZm2+vUNW7bjOmuJmTbKjB21oM4+bwbYfjRJRiddi+yl1cia+UuxLvewsTCrZj1xFuYXVSNmKwinPjTv+AHP74YYVFmGJeswdSl5Yi3tCC2ZCcmOrYgpmQrFPC2eiO8GnqV/7E6J36XhqH+g66syRT0+jpo9bK4ggfekrtu0J2AGlZPX7dKbOtjNLNP6VY9bnT1dKO7l+lcwO5uN3a8vxu2v61DSsFS0fcOS3gUo3NfxKjctQicvQ4BWWUCuGIlZqpG+PxGRN7UhJHGCgzL3ohRC+oRXlAjpYVZaY1yh374NdaoCLGpQiK4KqrLaC6bhl3dq+IVhF5KIQi89AEm8AblVCMi3wu95koEGzdKxbZw8yqEZ72AMdlPY2TSIxg+ZRHmPfAs2t7qQEcvoRdw9wCde/mGAV2PSm5jRTxPpypewbLOmhO9NxD99xH90DvUZ//wn6+B9/BL/XP9I+AfgS8zAkMOvPxBZnlVRpRYZSrZwZK/7fL4XSQNllakP/aGPCbOdG2GcUUTptxUAsNPfg/DBcORcs8qzClsRsZD1ZjxUClO+cMEGP7nMpwfkoC8x8qRZG1ClOVNxDs+QGzRe4hbvhmpjlaklzQhfSVhjO1YSBn0Po6XCK86nkTeTFDS4NisbN34eNvehqjCeky1NyKnsALjMx+C4cQLcMJZlyIo9XbkrqxDYnEbJhRvw7jityX6nr90AyYm34rT/t+1AmNhMx7C1GXrEF9U7gXerYcAL8+t1uoSrihZUTpeHptexl6P3Zfoh1DDy+9ASQK/o4qIqugpr+cpzs2YTP1ycRuSiiqkwtrCpf+A4YTfwHDq5TjnxkzMdjaCNx4sMUzgzbFXICz9HhjOvFw8kMOT74HJ3ogUlm9e8S7GWnZi6mNvY4atAUnzHoPhtD/B8MNfY8To2TAtWyfVBpmQOMm5FRPs7Yh2bfNG2AeucXWcAwU//BHeL/Nn8xhuQwLzgq3ALQHXA3lK3+MGerr70N3jkdbl6ZUErU5Pl4rsMrrb3Q13t9K2svwxpQ497n0qoQt92N3Th4r293Cn5SWMmf4ArppyF4al2jAq9xWMLShFZEGFAC0BlKWCR+RuQoCJ3rqqjcjdiIDcjapQBaUPuZUCuhHGakRSn2usQqC5SiQKlCyoVgb6/jK6qwC4THn7aikEo8P0/2W02FQPegIHZlcJfLPSW9j8WoTPq0KIeT2GZ76AkRmPY0TSMgTF34vxaXdh6Yq1+M+nLFEMUNbbS0UDoZcQ2+vG/n2fqChvLxceLOTVwKtED/z3+Hz5gff4PC/+o/p2j8BxBLzKr1bbaCXaVNnf6GUNiFlSiwxbs0TEzPZy/GJYIgyn/haXj8/C3OXVyFzWBCPLEN+5CidcPA6Gc6/HeQEJMK8sF63u5KJtiHP+G/Gu/yDJ8Q6SXTsRbW1EtLXBC15fArSOCGnHL/DG2lW0lRHJ6KImsRQj8I7LfESS0QxnXYobkxdhmrUUk52NiHS1I7z4TcQUb4fZWobIxJtx4tnXCIzR/3XqkrVItFYiwdUqXrRR1m3eSm7b1E2Ms1XJHRyskHc44P2KsMtzcBwAL6FXAy8lAdH2LZjoaMcEeyuSHt+BNGcTZj26FqaH/g7DyX+UQhLnBcxFVkkbEiyNSLA1Ic1ajVxXlRSoMJz1F1kvLEkBb/ySBiQ99W+x1othUuejlci8/TkYfnIFDCf8CoET5grwznI1Ita2GVHFuzCF13g/8OrkNG1P5gfe4+bPtpaS6miuF3jpTsDGKmTdPX3o4nsPE9VUslq3u0ciuiy/6+7pUBrfPia57UZP5yfwePbCgy4p5MCiDu/vA16uehtz7nwS10ffiisn347g6S6MNv5DbMzEmqxgE8ILyhCaX4bg/AqMNG9CxMJ6hBQofS+T0MLzakXGwGS18Fyl/VXliFmSWJclZtlibzTXrPS82s5Ma4R1Hzi3HGHZCnxZ7W1EThVuyNqE4dw3P9dUikk3VSJ87vMIzyxBeNoSXDeuACm5y/G317dgD/2Je4A9nUA3rcncekDdKuJL4CXXatLtL0/MSLgfeI+b/wf+A/GPwDcwAkMKvIzs0ROWP9Js4g/rnaZfrER7La2YXbIFqYs3omBFDa6KXgjDmX/CT/8QhoJHX8HM+9dhztJaGG2NyHzwdRh+FQrDedfjvBtisGBlBRKXlEoEMc71LiYUvolJy3ci2rYTUc5tiC9hqdVjCbvc1/EBvCpJiY+w1Rizj7ar5KU411bEWNukXDOT1sbNehSG0y+VyPiI9DuR6apAVHEjRpW0Yexf38Hkki3IcVYiOGkRfvTLkQJjsx78O2YWbkSSreawwMuEtXhnCxKcTVJCl2WcByK8Gri+/cBLbbSO8CY4d4AtvngHokq2I6pkKyZaGjHj8S3ILixFzr3Pw/DDi2E45c84+9oMGFe2IdVrk5diqYCppAaTzUVqjE+/AqNS78OCkhZMtbVion0bRtvakfb4TonkZix6UmzkDD++EBPTF2Hu4lXIdDRiwvJmxD3xLmJWvI1oukc4VHJafxlt7w0CAZ3///wR3m/gr+xnfYQGMYJZL/WaStognNarXBwGBSkVu3F10TxQ1kCk7VRNktt0xFcFOHfv8+DjA6pwxTu7gSdXNSMt72FcOTobQanLED7zSUwseA3jCl5H4NxXEJi7BqEEXmMZwhY0CHzeMKcUQcYqjJpfh9HzasTdITBrgySwaZgd3IubA6PCbMYqSXjTyXE6QS4ouxxhOZXK89dcg2BTNURLbGaktx5jbmpEcO4GhOWsRmTOqxiX8xxGz1ohuuThUbch586nUb31AFiJ7oMDwPu7O5ThmNutvjxtMLwOZIwEc8jo4kD/XgKvjqR+nv6zTuOxXnak4znWn+Pfn38Evk8jMOTAy4iu+MLaWyXJiY+DdUUwShzil9RhlqMJmUvWInf56zj32hgYzrwMgVPMuG35KixYvAF3uBqwsGgDsu59Gj/7XTAMJ1+AywMTcPeKDTA6q5DhbETU0nokFe/CzGfo1rATEwpbvcCr4etYge/xBbz6JoKQS+BlT49Wvk9zbZaytOOyluHEn18Dw08uQ0Dq3ch7vBFpK5ow0dmC6Kfeksfy81bWIyjxFvzg3GFSdnj2w69hlrVMbMkSXG1yziRpzbkT0Y6tiHY0I97VhARng/ge9wPvd6jwBG9uCLySzGVXpXvF35kJfC6Ws96KyIcrpAjFtPteRdadT8Nw6mU44ewbcUFAFgpWtiPd0YhUVxNSrZXIclUibqELP/3DGCnpHBJ/BwrsdUgvqsPYZfWYUrwFGY9vh2llI5JMFhhO/i0MJ/wvMuYvg9GyAXNWtGHi8lZMdr2FKeKUsVOgVsaeNxxiQ7ZNouIqIq2ueZ2Mpm6O/Elr3+gPgAZe9j4v39n6ff/i/hm96PWwLhkBl80NULcqGgmgu6sXXd0KohnLZJS4mz6+fUDztnfheHotrgifg8CkBzEs8RGMTHdg8oJ1iMxbj5FZ6zH5rh24IascN2ZVYXhODQK1By9tzMylCM/b5E1UKwM1vEduCnqp3VXAy+IVys93VEG9RIoDs0oRlFuGsHwlaWCpYkaMQ/NqRGoRkVeJ0Jz1CGM1uYLXETr3b4iY/Rium3I3rhxlxh1Fq/CBG9hP/XIX8OHuvTJcvW5VkUK5uvXC0+NW9mXUQbPIR6/bq5HuPWw/GDz7z8HX/EZ/Lm9qdNPzvuaP9u/ePwLf2REYcuCVQhL2LaI1ZKJPdPEWAaYY+w7Re6YUNmGWpQZp9z2HWff9FYbzrpEIrzzOPf1yGAy/gcFwAQw/OAc/OOX/4bQzzsUJhjNw8km/kKpWp/w2CKn3PoucxxoRv6QUcYsrkVjUhKkraCU1oG38SjrSg6LE3yzwqsSjQ2Gd8MKmbiDaBXRZiIAWcPRonWJvA62zphVtwqR8G372+wip7HVlpBHziioxc0klkhbXSAJhpq0NN1krcEXoLBjOvAo/OD8YGQ/8HZmWUvE09gXeWNcuOX9R9iapsnZE4BUngEOP+wufhyGVNGxBsqNdnkRIxT77Tqm4RlkDq51NtrcjecU25Px1O2521eBOyzoYTvwdDCf+AedfPx1GWx3SrPUieWCEd4ZlI6be9Qx+OzwFhpMuxWWBMzH9nleQvqwCKSu3IPWvOxBfVInZhRsRFJWPU876Ewwnn4/8B56EyVaKmStaEevagbGW7ZjsVBXveH2I64nND7zH5V9xDa/svS9VgEIVoqC5FqfVi7oHEaqqnmJfvT0xt7dXijSobbyr8Ik+vX6pBe52i95VtqFEogvY+QGQfcfjuGb8PNwY9wCGp7gQkbMKYxfU4ZqMdQjKa0HYwq2IvGU7whY0i5Y3IGctQkxrEJ6/VuzEQk3rMdA2SqliujYMODcoWQOtygi9ou81e5PdcstBmB1VUI1waoBzNkpjVJjwSzCmlILgS2cHnRQ3MvsfCM55GRHZzyBihh2Xj83DiJg8uF4sx8c9Kqh7oKML7m4P+rp7AbaePvS6PQK5dLhg+zYArwZd9v6XfwT8I/DlR+AYAO9XeyzNH2TqdZlIxSSnaPqS2loRVbQZcUWbMc3ehhm2Otz0ZCNS7ngCP7ggAIZzroXhp9fAcNIfccJZ1+GHZ1wBw0m/guHH58JgOB2GH5wJg+FsnPzTS3HuVVFIu/cFZFrKMNVajVkr2yWRLdWpHud+YcA6CG4PB2zfLPAe6fg18DLCS+hlZJdlmhk9p0crvV/5KJ4V6zg+vw/MgOGHv8Pvr5uKBQ+uRt5D65Dz8EbMfLgMxqXVyLnrRfyM+ujT/oJLwrMwe8kapC0j8DKKu1WsrHiTEufcBRY3iKGPsvdxfZKjRaKLqoqet7TwtwV4j3K+lSSA2lhGU99Eov1NJDrfEGkDn14wip7w8DpMveNZLHj4JdE/G864ApeGzIWxqBzTLLXIKG6RCO+05esx+9HXcNWEPBhOuBjn/GE8Zt31Iua56jCtqBIJj27AbFs55js24dw/jYXhtN/j9AtHYqF1HdIfXgNag6U+/g7oW52w8p+Ism8X/2Md4e2/OfKxHeP1oyO8fC92Y3barA08+VDfccDNoX89vy3Zl//Lq7f0AVbOUrArjrxQpYa1JtWnF48tDb9M2hoAIb079qw6xp6gy2plnOjt7sK+jz9G5ye7hQr3dgN7+oBV1f/E1AIHbphyF26IL0LQ9BcxJq9KqqQF5tRieHY1hmUxGa0CEfMrEDaPEofVArrU/4YbNej69gp6FfiWIdi4SRr9eTX8CtiaWPCiXFp4XhnC81m2eJOCX7EvKxPnCLpIBJirMTy3HJE3N0tSXIR5DSJyX0DoLBeuilqEoKSFmHHzEpQ1vYH/7nGjo5uwD3j49Un+zAyUwh2d6EOnePYqoBywhtOykgHQpPRBNWVlps+FPomDe71c94OXH2F64DT6fJ7vunp/Ax7Dvkv97/0j4B+Bzx6BYwS8nwN6dSRuUC86XVbesraKiwDBl96kqSu2Y9rKnYhbVo80WxMyrDWY6aiBcUUd5lg3YebytchfUYfsolosWrkFtxQ3YtY9L+Cnl47CCeddiz9FZCD7kVcxa1k5MiwtSOMPuH0bmLhFGCMIJroGftSPBI7HYr4k4nm/d6JjB25cuAYxRfQe3o5YK4tfeHWgdlVs41AdsC9EHwrZGm7ZDz7eOJa9de2QFm3bKmVw6bMqcFrUikxbE+YsL8N1sbfBcNrV+MFZ12FC0q24e+lreNi+HrcufgULH3oZkYl34sT/DYXh1KsQmWXDrMIyzC5ukZsV2Zdth1Rao30cq65Rg60TDzUUauBir47T93t9mffHMmlNnYPPHvtDr3O6YLBMM6O6CXS0cLwpVdeSrNuQZmnBTEs9FpQ04BZ7GUz305bs96K9Pf+6JNz6WAOmLa1CmqVR9LcZjPIuX49hdGr4+TAYTrkMQdELUfDAi7hpyetYVLgW8xa/hticZTD87CoYzr4BI2cUInlxmdwYxi5tEFu0pOKdcnNDS7/B18PRpnXp6c+zHv/P+n14P/sP7Ney1Jdqj/IBhDa3WwGvxzMQEWaks0vKFXfj/Q//JVIH+vja/lqBkJjbcc2YuzAizoGxc1chMnsjmKQWmLNJXByC51Ui5KYqBNCFwbhJJAkRedVgE3DNo3dvhWhvWcmNLSK3TIpTsECFKlJRIZ6+oaY6qFbjLWjBZLdNCMkrlRZk2qgKWJgrEGSuQZC5AYHGRozIbRCZRfhN9RhhXIMQ82sYP/9lDE9bit+EzEZw/ALcuuQZbH1vv5Rg7qDcoeOAlCWmW7HH8ynFHV4A9tq/UQ3CIXIryzPqfSUC7LWBY0RYy0f60KMKXPieCwFWQilFw1pmMiAx6Y/Ge7dRYK0S5/ohuz+/rkci0FqeokBb7bfPm26nc/GOcgn4F/tHwD8C3hEYcuDVrgyEIMJuIsveMqvf0Y4UF6O8zUi2tyLR0ogUSwMyi5uRbq9GatFGKR6RuLgGGUWbMWN5M9LuWwfDJeNg+Mlf8MvQGVjwRJOCXct2JNjeQIxdPW5nFJnljA8HiEf7of8yy79O4PWF3cN9HwFPllB27kCsAC+rbe1ErOMNsDpahrUVOa5mxC56Dudcw0fpf8T/XBiMcTH5mJH7MGblLcX45Ftw6gURMJx5Pc4PzsGMJZswfVklZhXTSYPwvMsHdhXwJlq1BZnuGUn0TZL6MoA7eJuhB94UXkd2Au9WGd9Y63akOHZimm0LMpbUIPG25zHzrmcx547HEDv7PhjOvBSGsy/DJcNiYHzwOaTd+zKSHlyNmZZKzCgqRZatFIm3P4Xzrk+SaPAPf3Y1wibmIqugEKZFLkyeehdOOj8Ahv+5Gqf8OQ5T7lmPdNcOTCvZhQTrZlAixOstmnId6uO90hbdH+369QPvd+u3gZCrH4WzHQF+JwAAIABJREFU5zSjvYRgljB2u/dR3Yv9+/fi031dUjRk+7tu3PHoKxib8hBunHg/ApJdGGtcpdwSCsowPGcDbjRuQoC5CuF5dQg31SIkpwrBucpqTBLSaEFmrBC4Jez6NgW9nw28oV7gVRKGUjAhjsAbYmpAkKkJweYWkVv8OWMNghfUY/QtLGG8FuGmVzDa+DeMTFmMP0bkIDA6D87nyvF/nwJ73cCBbpYepta5W8o393R19wdtpUAbYVRAlxFf6p894n1M/+MB4PVmwXm10geBrMhPuJzaaq2vHojG+64rkCvg3Of1W/YmKYqMhaDM7b3b+uyXsE3Y9QPvd+v/qv/bfP0jMOTAy6irBjXdsxwtqzjF2Vh8oh2ECoJv7PI60TumO2qRbGUyUA2SljUI8E57tB6Zi8txxtXJMJwfhAsi52DeE43ItDQjtWgr4ou2gTAin+FSFcC4/6MBwLFYfijwvn6YCK/yxyU8HT7KqKOiB/efB2T4+XxEHWvbItKDOKeSHtDiKs2xGRm2RkxfVoGILDvOuiwahjOvVF6wp10mMgfDGVfDcPZwnD9yjvgeL3p6F1Ie3oT0wiYBXkZ1dYv3RngPBl4Nuzz2w0DrYSLTn3vc9ROD/n2o/fM7s4XdXY6xD9VJNJ3wz8aouqpA1yweuPTBPfS4jn6cjFwnWpulKiCvUYloW9uR4WiXyG3CbS/C8JNrYfjhhTCceD4MJ58Hg+EsGE46W/UnnKP8jy8MRFjmvcha/DJuXVGOex+vQVzuUvzy8gn48S+uhOEH/wvDKRfD8INfKSuys67BRcFzMGH+U+LgkFa8Q4GuTemJJQnUtlmeYOjrQ/d6XHW0XU/r3g+8X/8f3W/yE3yBl++7urrQ0dEh/r2yDMDefXy8rwDqoz0HJI5Jz4fylvcwa2ERRk+9D9fH3oPAadT3vqqS1czVCDbWIiCLDgt1CDPXIyyvAWGsmpZfJ24LtBkTdwb67eayKQD2jfKymMVAU7KGULNPn8eyw5Q4VIrXb4iRul5Gg9Xnjbt1u1iZXTdjnbhBjLupBpGmNQid8wLG5jyDkUmP4o8hOYib8xBe2rAD//oY6KS8gdpmcqlEZSE3AIzmqsS/brh7DggQSxIgoZcln7mR98V3/VP9b7hQR3jVvlSZ44GNqEYR6TVXkxfXU1DL3YtZB90jCMISJdbr6f1yvrufm/VSf+8fAf8IHH0Ehhx4fTWD+kdZOwvQqoxRqpSSHcrJobAJyfYWyWpPstciyVqHqbY2ZJW8gVmWNqTcuxbnBs7CmVfH4c8xCyS5J9PaiLSidiQUUTZB4GqVQgnUlSbYmr2gczBE6h//Y9UPNfDye6gI5BbR18azch21tratIh2hF+wMZxNyXE1IuvMFDEu6G+ffkIYf/Socp/1uAi4cORuRs63IeGAtch1NyHK2IWVxNRKW1AnwssKYAC9vXrxyBlVggtFdX9j97gEvxy6F16TTmxRmbcc0RztSl1Yj+tYXccJvxuKkC0Jw5oUB+PEv/oLT/vdK/OTX1+LMC67FGRcHwHBBAM4Lno6Y255GtqUcGQ+vxdylm2AuLEPWAy8jMOEmnPOnMfjJJWE45aIgifwGTH0AyXe+jPlP7UQaKxJa2lSzt8sNnEDroMiu/r+lr2l9XvS07v3Ae/Q/mt+2NXQym47saolDZ1cPupm85gZYu0IkrlD64Z6+XgHfjzuBJ16pQpLxUSnzOyxxOYJnvYBI0waRLdBSLCKvFgTRkdnlCDTVIiifMoNqDM9m5TS1TEoZ05rMyMhuWX8LM2+CNK+GV2l5NQRXI4xljCWyW4FgYzlCcsull32YqjFybgUi81swqqAVQbk1Io+IpPODuQJhOWsRkf0KRs/+K66bfC9GTL4VNz/wIioaP8LHe4ButypcQb9jlejHqGoH3O7d6Olh8YoO9PV2i/aX0V8CMmGVr4OA95ALgnCqxrEfkr0bSFERT5/Yo5FuxWVDdMUDBE0dN0efFfUOfhGOeYzsB9Y/eB3/lH8E/CNwpBE4DoCXRQq2qmQq5zYpYEDtoW5cRk0iS9cy6isa3+I2JDubkWRvRGpRM6Yub0bm8maBMbO9EvOLy2Gyr8dc60bMKKrDNMtmpFrakWxtk22SHXVIsTdIUQrfhB39o3+s+88G3oGILr/bl4/wDo5IDkQ6+X30jQWlDYkCvDsk2ssENnrmxhXWI9VSgzmOetCbN99Rg5tWNCLfUYc5yyqQX9yGHOdmpC2pR/qyJsxwbJEKYARbidL363ap3d0qIKyhiv3AmLIc70AbmO+7zhd4/5UjvJ815r5jeugx8dpJdbYhydYsUhzqpXkTwaS1ZNcuJBS1IeuJN5D7xFaYH29H7ooWmJ5sh/HJdswoaUHGinbMfv5dxNG317ELUfZ3MNn6DuIc7yLV+TamObZibnEzCp5shqm4EvMfq0OWtVxcS+bY25BeSLkPn4TwOJVEhzeJhFvq0+m1PBh09Xjrc6Onde8H3iP9qfx2z2c0l+CrXwQxVnDT+CSPyL0SB0/3p+jt2SfleVnIgdj1xgc9KHy6ChNmLsHVUfdiRJodIbOfx/j8UoybX43w/EqxDxuZW4mQeY0IKWhGgKkegaZ6BBtVCxH4rVK+vGJjpuzNaHE2AL2E3Wrly2us6e9DBZbLRA8cYtyAMOM6hOVuxChTHSKNjQjJbkBgdgMCchsxPKdeGo+BDg+j8sowKvtVjEi04doxd2By2hIsdVShtnkPPu0EOlmxra8P+7v3ocvzCTz4BJ7ej9Hbu0eVKfb0gkXb3N5yxpIz6EVaPZ661yDMnmM62EOZp4A3HNRQM4mu+8BeeLo6xUFCgsOyI8Iyo7jsfeGa50+fMT/w6jH39/4R+LwjMOTASwCLc20/CHDFScALvVGECK8GVUUpFSyxuhcfRSdatiCxcAtSCzcjdWk9ZliqkVm4EbNsm8AkoKlFzUizbEMy/V8ZgeTja5ZmtTUjmbBiV7Cgf/C/jn4ogVd/Hw28jMaKntexHdGObWKdlfD4TqSspPa0FRnOZky31SJjeSUyi6qRU9yG6ZYGpC1rQOqyRqRbNmOqdSum2rch1U6w9YKgRHcPD7v96xDKfGCX7/Xxfel+CIGXMgjqzOMtLUqCQ/9d+y5EOd5EnOstJLneRHRRC5JsbWKFRy16gqVJrkEmTiaufENKAE8qeRuTV76PSSX/xeSS3Rhv/w/GF72DWPtbiCuifr0WqZYqpBVVSAni6c52pFlV5TqpNEf5hmubJGJSu0vITSreLtN+4P28fwq/f+sRpw64u9HV54GbsOuhiy2LWOwH+vbDvf8jdO3fg48++kTAcF8fULuzA7dZVmN05sO4LuZuXJtgxcjpf8OY/PUYd3OFVGkLomfu/AaBXgIvk8w09LKwRLCJpYiVLRm1umHmDUcA3jopO8zoMEsZRxjLEWEqRYR5nWrG9QibswGRuTUIy21AMME3vx1B87ZheN5m3GhqxghTk0gsRsxcheDMlzB+zvMYMWUxho29C+k5Tqx8sQEN2z4AI9l7Pb3o6D0AN/bBjT3o6PpYIrCEUw/dHjoV9BJ4+eqP3vpcOhpQ5QaCsdhBhd540+F2d4sfMPeLbu7YG173ukiII8Qg2FX79QFe/UE+n+1/6x8B/wh89ggcB8BLAKO2Vml5tW+sjvrSUYDuAoReRn1ZMCGGGt9ilXgW63oP0fZ/Idr2LuLtb4F6xkR7M+Kt9Yi1NkkyVYz9PUQ73sMU5zuY7NwhBQFYaS3G+7lfGraoGz0EuA6NBH414D10f4c/Xt9opC4l6ztP7YfjTPjl+LJAREzxDkxxbMFkSwuiChul1G2aswXTnW2iD01cyshvm/jxptlZJncnkqxbEV/IymlarsDPUxpW7cygSgnr5QPfQQOY7g//XQbWP+ryQ8ZffWeOOdvRNbxHi/Ae5Vic25XzhXMnEla8hRjXm4iysZofHThYSa0V0xxNSCuqxCxXA6bbajDDWYN0SyXovZtcWCG2ZCm2VrHhi7e/IddxnG2XAPPUFbsQs6wO01a2I86iSmGnrnwL45dsQTyLS9B5xKn8luX/hjeqSymQb9La4HH0R3g/+w/jd2XpIdrTPj5OV3pU2p4xxtuN/egT0D0gpYndnZ8AHpYr7oGnowOeHkIa0Mmkrz7g//YDL23ajNy7V2D09KUYlrAEIZkrMC7/ZYRkv4zhc15BUG4pAnMpQ6j1wu4A9FL2wCIWgeYqsR+jBZlqSquryg5TnuCN8Jro0csEuTJEstyxeR0iTaqNMW3CKHr25lQiKKcaI3LrcV12La7JqhPgvdHciMCCJkTMq5Vtx5lWY7LxVYRPXYnrxt2DGycsRO6dj+O5De3Y/N6n+PeBLuzu7sIBd6cUpmDpZkZiaWvm7vDA00kRrg67SgxXaXC95dw0h2rg1T3nS6GLvm5JFmQBDHoEg6WQPSyvRx2vKhyi9Lsqsqu3Z6/8mCln4Hrej2Xvf/lHwD8Cn2sEhhx4CV8afiT659giP+DKP5aRqx0CvLR9YiRYAXEb4ks2i5/sJMe7mFLyISY53ke0g96jhLk2RNlakFCyEzH2fwoQT7b/HyY538Ek1y5Mcu3AFNdORDtZWviLWzcdBA+HANehgHQ8Aa+Kkv9/9t7DO4or6/qef+Vb61vv9z7PDCZnlDMSygEJRMYEg7JERhlsYxyxUSY5e5zG2UQhJEDkZJLH9nhsjxNBIKlz72/tc+t2t1oSCMYBocLr+lZVV3VXnSqoX53adx+WGb6AdCmvfFU8eWdsv4y5u/4pA7qYsaRPcdZWZTs2kyVz6y7Ja/rULWdFpzuDDyAiEfHCNbPlGqSGCvBSbiMPEARPyZpflp7XrcSj5qSqprb1CBbUHcGsLQcFduc3HJX5eS8ewuK6Y5hfcxyzt57GrLovRGs+nQ8VtV9IcRR+D8/JrG0XkfrSWWPg4T+R3nBF/k5Iqej68/L3hpArf3d85Az8+9XjmhXvXfUw4r/clDQM6N/NQbdSX9lIuhXYXZ1wohMOFyuTMbtL0LXAaemgaS/s3bfhsNkFerstDnRZlcSB4PvtdSd2fnQOs1c0YnJ6OYLnPIPQRQ2Iyn0bcSsoOdinNLt5hyFyhjwlbWDWV8kdjor7gh6UxoFphF1xeSg4LNORRUeVjreoTbLA9PSl5y+9f6Py9yIybw8ic/ep8sNcZ8VxBBYdx8S8dkwpPonJBccxpeCIwDUrwXHbuKI9SCrcjYScdxHzaBPGxJdiUtJqFD/5Bvad/gHf3wKuWwF6FLNeBROvHM9ms9jhJPULZJJ6ddPaWsoNesoQuKpiU6focl1uq6r0ZnfBbnHDcssGe5cDbpoFUzfhI1ngdibwDrq/auYOP8AR+Av37ZYTiFy0AWElu2RwGG+kfPVNeOGgGGafKAeQggL1X0pmlDdKllX1jm7vfVPlOjrT5smEGq++9Yh57w2XmTn1fepVt9Iiij7U8NDVWWD1WvyM3ODTGr6S7C3Blq+ABZ4bTiOLrZ6a3y+RWcvXw98oKy6RSpyTbfsCAe/+9H08vT7XwKt7P7ggQGjg5THTh3fiqk+QzoF0jGmN0vDSz1XHktOyjd939fjtfn5PQac/hDK26nj0w4XWSLP3hX4NrOxV7C8ZNmZXBLAk086Sz0ZGnvvs+5u6yMEfCbyEb2+8vBnemY0XEV51UFwaGHd9zfm6NOiY9+7vcv7r1IA8yhQ4aI0946Dj3LNX+8Trm9cuC6yw4h3PBeM1p0ZXQfM9R+q8yDUjnsaGr3EtNcOUSZzGDPo2ywOisa6cE+N86uvjd+h1HGmDxsF6s+pOStGLiUuew5Y3m8VMSZhAJ8L4jwyzV3IzJyQoCNDryALzf/cVAYJsXzCrl/v3/j9CZwLlBqDPD6GLdlgWuNzdIKCpqmSqDK/T6YbdTg9fJzoJbAC++N6O517Zh5SlTyAgswyh855DzLKXEVfwAeIKdiNq+T5MLTiK6KJjCF6uvHTDV5zF5OXU87YjvEhVUiPoUu7AzC9tyMJZ+ELKEbcivPgQIujYkH8QYXnN0ou/r8ghKIk4IGWOWdAiqKAFgQWt0midxsbvCcnngLdmgWOWKY7K+QwxOR8hdtm7mJy5FaPjK5G85EU8Ub8fB07/jP/cAjqdgMUJ2B0QSzcXM76kX8nGOuCwM1aMnWHxJgPN/GzIuAYz5rDC7qAHsFMGvzFrfOrYZeFmyhtsVkpKaCHBc6D+jvQEXv4Om/H9OtPsf1LNeTMCZgT6jMAdgZevp38L4JWbv8+NV98w2SswIBBo43/f3gAIva1AGzW8BAcDHuqvIL3eC7QCsQK8J9V3sgJW7VfiwyvlX8UW6+6g3hNY7gA/et907wepDzLwCrTSqotQXqvazBrV89xINpjxbWA2/EvJiFMGwsyw18d4YMArAO0H3XKu/OI14Ljr7Yy43w14ee71dfdbAi9hl4VRekKvum55fIwxHyoYM8aObxX4diG5ScWTrhaUiHCgH8+DWPJRssNpvv2QwYAXITZvNV/IwMvs2lOYXXtMBmAS1PVDi3oIMYG3z3/pHvCFdwNT/933X99/nrIFLV3w/cz/e/hana/XZRCVAb9OWOFEN2zogpVaXncXHG6LvIpnJtJtpdTBDnuXDV3dDnS7gF9tQNsXP6C69h+IW7ABUzIqELWwFjGLX8fUZR8gvuAAplGWkNcmVmYhee0ILzllSB6Y6T0iLbT4CEKL2xBafEgayxCHFLUI8BKAWZlNDWAzHB8ohyg64G0eeQSLV7R4KrqJZpi6YdEPE4CVZ/C04nbEFbQgLm8fope8h7EpL2BY5HpkF2zDKx99gTPfdOHnTvUI0G13wcpMrJHZ7e66LVRqs3bD7VZAyzXp7MC0rsfXV9YnGPPhQVmK0fHh3JkfsL3pAzjsqhqeAl07XLZuVRCOmWW3QlyHyE+4rQm8/tewOW9GYKAR+N2B1wMwGgiNXsOHBl5mi1U7DVqGqaayZjrrqNfVcCzZMgHerxT0spgCCwEIDJ9WA6TqrogmUjK9zEzKoCkFy78dcF3qV8vbG3gvYeKqjz0ZXlpKMTOoK87pY/Wf99p86UIOqteZc72dt+8Jovo8+EOR9o4l6HJwlThZCPT6A6+SgPQAXmPQmfc3OZCqd9Of633w7fs7rntd7o2XOm7OswhDZOVBxG9qx8zaSwKVApaUB1CyUUunDx0nr95Y72/Pvmfc1f6pDC2zuzrD68ny8k2GZFyZIedDApt+eNCxvCIgm71Vgay4PdSdwIy6E1KWWVw7BHgvY0YNG+GY5+k0sg3g5ToKjJUEQv+94nF63TK8+66vl/+m179hZngH+s/sndZTr8V16Vrda6i6U6/WVRleX6jVsCs+u0YGWH/eY084oMrigsvqFo0qs7cic4ANVlikWdzdYOOgNhaqMOgM6LaLoa3T2gW7nYCsoPDfNxz4qO0KSp54DaHTSxGQugFxj27D1EVvIXrx+0gqaZOSxcHLDyK65Lh499JTV2V5j0IDb3Bxi1RyCyo6CE4Tgpn1ZRZYvHgNuzM9+I1yBeqA6fYQXUitr2rMAisbtEPiCUxNsQykKzwpBSwCHzuCmIKTiCs8ibiCw4hlVbn5b2F86rMYE7cWi9c24rXPTuHbmy50OICbFhdsPgPRCKT8I1IFV5dkxiVLq7OvBFYHIZmwa4fFYRdk/eUGsPnZd7Fk2SZcvw10M40snyjgJSNT4eALvA7x5jWBt8c1bM6YEbiHCPQA3tDinZJlSuPrX74m/g0yvB648QNeeV3eI8N77g7ASyjRmkPv62F5RWxARSbB1gO0p5HZqDO8qsrazNorAqUiwxC5g87y3iF7q7OId+rrCLv/HfCyupwGNgFQ41h9p/sDQA0uGsB0nFSvYc7IlBsZRw293JbZxewaWrYp2zYBXroK1J5XRRoE1hSoKWjTjhrKcYHn1/c3+wJeLvNdx3NNyLZeGOvvGAeyXMdPAyzn2X5f4FWWbHIO9DXiAV0Cr4JdVrbTsOvb85plCWJf4KXVHoFXBl6yCIlcW3xL8SWyuL64jRC0+YBI2Y7PwEnj79id4qWvl/vtNezSgs0E3nv4l7bfVRXw3gls7/SZBln/XkOv7/Jeu0BYM2SjYgXLwgeSQ+RQNg5m8/7HAVcEbPUu3njHT5BzWQBXNxxOC7psVnS7KYYALv9gwWufnEFO2cvifzs5+XEkLnsLcUvfQ8zyT5BQ0oKwnL2qHLFRoEJnYZnVZQssbEZwcas0Oj+EFCl9L50elMXZUSlwQQkEtyX00sc3SgayHURs/kFE5rIscrMMamMlOGaWg/KOI6jgJILyTyM07zSi8s8hIu8UwpYdQWROG2LzmiUrHbVoJ6ZM34iI7HLkb9iBz9q/xE/dwE0XcNMGyWyT+4mqHFBmc96m74XYuTksfDgQEobV2i0Zcsb0p45OGfi3t/UnJE2vQlRCCS5+ZcdtOpPRCoInRPzPALdVjWVT54SgzGpv/FJPklnpHnqdWHOBGQEzAn1FQICXT67U8PoCLweJ/d7Ay5uugi8NTyyHqvS3noyvz0AoQpPWWjKLS+BlIQpqjD3a0fqToM9udsMRZNcfx6zac5hVc1FsnLjeHPrvig+vKmLhychpYLnHXr9y1iDg6evOy8ODJ8NL2KR0oL53htcXbPub7g9iNGz3/LwnYKq4GWCvM+y0JxMdKi3bLmCWp/noSeX8eKHNV7/rgVa/Bxm9H9m1/N6eTX+mer2Pvw3weoHam+HVwDvtyaO9MryzG78QDawCZL0vd+p77ydjLzDLNwtSrpklm3W81GeeMs6yjlpPP5xxewKsihN/Wz/0efXACpqNUtC+32GcP30OfWPrH3fO68/vFXR5Pftuo69vE3j7+uf03pdJltaoqqVG56sKWwOadquywYTa+/rDzfybzyK+QFcwp7nKgHNDv8ryvBzY5qFmOAR6mQHlUoLvhW+6UfNKKzIfewFBaRsQMeslTHvsDcTnf4DYgk+kgEV4wX7R1Qbn7kVw/gEEFTQjuPAQNORq0PUdzEZZA6u4EX4V8Gq5Q+9yxqrCW5sqf5zbjhACr7QTiC46h+DH2hG45Agi808gpvg4ogvaZEAc9y988auYMuM5TEgrR+Lip1Bd9wmaz/2C/3SLeRs6XcD1LsDioueFHd22m+jqvAGnlYPRVNEKOj1YnF3ocjtwwwmc+9aGVRs+QGBsGSaEl+CdT6+iywV0dHPgoF38z1zdbohKgnJd2sbBBgd11fKE4nOS7uvEmxuZERiaEfAAb8Sj1QK8NLFnhvePBF6lQdQerQpkBXh7wa6CNiVLYNZSDRiifpJgK6+W609iZkM7ZvoBrwI6rs+CE8eRzfUoo+h3oNHAMr+/JfASdu8PeJXHsIaankCpIM4fUCUb7ge9CnwV8OqsLKFM1hVpCLPkXlAWkL4H4PUFLy+gemGs5/7f63INqzqrfcGT4b0z8Ort7tb33h8FvIRYA2RlMJ+Kl3/cBHKpJ5emQJew6x9PBeDqjQbPma8swhee+X3ejK933/qCXb2M8fWF14FMa8D1703g/S1uGC643DZplAv4Nr2cve9y/2kta7jvvfEHXmNeZ4Z7fq/ORquBbmqwm0scBqRimGQf+ZkLNhcLOShHh187gfYLHXiq4QDi5z+LsQkVCJzxAqKXvoaphR8hqvBzRBftFT/esIIDCCloRkh+q1iXhRS2S/U2yeqypDDdGwpZnKJFpAoqK6wyvDJtVGNTWd1mROW3StPFLOjpq7PDYXSNWH5UPHxjik+KDVrAYwcQlKMKYdDzN37lAcTkfYCIRS8jcNZzmJRWhpTHNmPL39tw4Wfgmh247TLci11WdFquw2K9BbvVplwd7CoeVlhx02kHkbb+reMIS30KcbOaMC6qFOuf+kCyvrdtfESww23rhus2S+AplYNcC7B4gJenyPOg0vMEmXNmBMwI3CECYkvGDO+fAby8mSt9o66spqFXyxaMV/H6Rm1kXzXwstcZOgUY/B7alZ02Gk34laRBvRK+pJwbGpTDA0HbA4L3mNnV290/8J4T+KCGV0Nu38DrhZm+gFABkxeg9DpeyNEgp75HQ44GXtWrOBBCPZlyLUPwQJkXsPjdzJqz12Dnvx/69/vvldZX7+9/36vjpDxEXxN9ZXj5O2K75snw6vj0198t/goi+b18SNDxkwcwiaGOu87mqoyuN37eQWbqO3RWXH2XftOhdOu83i+qrHDNFWSzeTLEajt+r3/T54Dbes+/93f1sr56f9DV8wMFXmoQxaiJgkRmBI2spCznwB4/jenQmncawEvY5aAwb6Mm1Lf5fuadtkr2774zvL43Bg/4MqXI5nUiUA4bSjtKmGXW19O0VpXLnHaRNhDQ1Pt8lix2o8sG/NwB/PMn4PMT11Gy+WOEzHwCE6ZvQvCinQhZ/ndEFXyMmOI9iCqmFrdFdLpSlriABSWOGZB6GBEFreLHSxhlZpiATGcG30ZpA8sQs6lSxodkO27LpiGYfUz+EYQsOYDQ3IOYuuooolfQEeKgDJLjdwbnNiOmqAUJJc2IyX0PQbNrMHH6BkTMr0ZG/lN458AlqUJ3i5leWxe6HZ2wu7ph7bYI8NpsNomVBU7cBHDk6q+YWVCLifGbkZX/CQKSnkXSvKfx7Q2VJZaBa5Q0kH2NMHIQnFjHgUUx+AbABF7fS9ecNiMw0Ah4gXdRZY8ML0GOA2SUS4OCM/36llpOSgt4MxYdrQxeMiqg0Qqsz3llw6SyVkam1rBUkhHsjcp/l8CqHRjU9/TcTsOugK2u2sXvMQpTpDeeQ1rTGaQ3sp0zXjHTzF9pfDmoTWyhZNQ8b/o+39/nfvscjwZuT6+KOMigIR8Jg4KCix5XADoIcL85Ap8+qRNWf4i0rUZM6ZdaR0syJXnQ03q+Lwjpe5mPjtiQKgjgaJDvA3b4wMHCBSxCQVcAkVyIflfb0yGbAAAgAElEQVRBOAFc/ZbORBpgLXCngVfLUvTvq17AzGc/es8rwOz7WLxgdrfPGWtZR58T4zgYP8pHwioPIHbTUQXmxrq0gstuoisCLeHuksn3iVuf++LRMBuZcWMAm5bZ6Ew2t9UxUACqjp/Xjoq/GnSm3Rj0IEJaj4met/6Uqiwo5+cL0f5S/6sHp+memmzf5gu7cj14ZET6vN051hpw/XsNvBwYyDcr6ZtbMHnxc3j+rWa5V8tNWUkYlW0WrZbYXBxkRdDVjfPUhg7NXll+WSAGX/JAwIcCajX7kjYwG9wThHWG1xd6fR8a7nYj0OdJ1hPo7QN2xVJOA6+CXc4Revngws1YMlftAwnYAafD4nm46eq2w0p7LwA/WoCLPwKv7r6CBet3YETSWoyfsRlhi3Zgat67iCv6HDEFzYguOILIgmMIzz+OsLwTYDaWg9UiilQBCtqQKeA96Kna5gu9ku0lCNOGLL8ZYfn7oTx86eN7QHS+0fnNiMmnc8QhgWjCc2DO5wK8kauOiL3ZlGXNCFp+EJF5BxFb3Iy4wo8RsWQXpsx5HhOzKjExqRArNr2MQxe+xy824JrViRsWgj+tyDgYzYZOuxVdcEl296mdn2Nc/FpEzHkNM0paEZq1HaOiV6P96jXccrtlcKCMVmNwKYh2usT1gc4ZStbgVMB7txNrfm5GwIxArwj8hf9gXbcDMYurEVrYJNkxlvPNpg9vD+ClJvFLpMvr20ueAWbU3GawzC+zpvfRc5veTX1nX9/Xe11je8nsqun0Jm+GV4Oxp/f9PcMTlZ8RSNXvqW0z5bjo9OBzXITWuvNS/MIDKo1fYHrjeXD9TIESZhiZhftSBhplbL0oxTP43ekNp6RgxtjV70vFOH5X6kunPYUCMmrPgS2d3rwNX8i0sqeiRdXdmoLWu6+n9r9nRTt9TP39hnJs4DGr1nM99V3693Xfc52B7Nf9rCNvCWovIL2Gv8ciJuohhD2LQoRWHUDs5qNyDrhOhrEuH3R0Od77+d1+t6GlWL2q3tbvOj7n0nsedLz84md8X0+7MnWsfZ2Le/1N/r7vNnzoUQ8+LBt9Uf07UHdRHC+YMedANXkTUP8FMraex8zGr8U9IvPpQwhe+jw2v7IXHKsuGUD6jbocRlM2WQQjlzH2SQZK0QqLOlDnUOyZoe2E230LLtdtwNkF0AlBsuGCk/IPNjPkzKzSKYEgTC9XDcjio3u/Gt5etwMjc2jYbvUeLKc2EMCVPfI6RAj5qnS+stQiqLFCGa8BOvm63LjtcIvmla/1v7/lwNlvO7Bp2yeYWfgCgjPLETH3OcQvex1xOR9hau5+RLOkcMFJBC9vF+ilm4Oq3taMCLo35B+QssGs3BaU3yaD2sTHN0+BrnJtUGBMOPZvdHRgxlcKXtD3t5CV3nRTy+NWHUdMyVGRUGjf35jifZha/Dniit5H8IItmDC9FCHZFaio3Y0LPwA3nSqj3dFNVS/Q4STwAm2X/4MZhS9iXMomxOfsRtyyZoTNeQ8BGc9jy9st+NntAjPBNkc3XDZjxJpT6YA9b0TkFKmHCmNUXF9n0VxmRsCMQB8R6AW8fCWbroG39qwqK1vL6lqXkVZ3VRqzgsw8cUT59PpTAnKEucHXTktVNlZmk9Zw2jiGE0hv0E0fl1qX5VtZpYzljqVvOIu0xlOY3nBMmmTkaAlVc1X8fzNqrohPakrdabXetnMYv/YD2ZaV41LphtDELOt5sGKWLhObtY1uCDrrbfb9xYIxFJ9bApj4A7P8NON1EZlNlxGyoRnRm4/INJfr9fm5mh7asdXXvoZenfHW2Wj2zB4Tdgm6WoKTzcpuW89jxrZvJHue9sRuRCx7Ac+90YwOmjARbA2YJdBy2uk2vGENGJISVjQk5ej0odgIt5L3pNOBVcWANXxZapa8SzmIU1X6YuzsfCgQfFTWCjquvtndPv6N/10W6SyyQbcq1dsDeFmdzKmqinHQlq0bN7s6cMvajW5x+XXjhs2Bn7uBg2e+w7qn30RYxlpMSCxD9MJtiFv+D4Qu+ghBi/YgpqAdU1cqjS21vczeshBFZMkRVaLYKFUcQo1vEf18W6VIRWRxixSk8AddPU/gJeyK1Vmh1gYr2YOWP6gBbC0Iy2tBaC6LWyg3CJU5/gzhy17D5DlbEL6wDqGznkb07E148bVj+L4D+LETuOmAZHb/YwWWlG7FmIS1SMx9F1NzDyBqWSsS81oxOvF5PFregB/cEDB2MJdr75RrgueYDw5UmUgWXS4L5tcNew15OPldTrH5pWYEHroI/IVP6zccwNQlGxBWtA2z6s8L8Kos5VlPFSgOkGF21zfDy4FfzPAqCYIuBjHYepXdVVIJve88Jt30MmM934FEDRcFYtOajmJ642FkNh3BzIbjqgIW/VNrrmJGAwc0XUJK3VlVXavpMias3Y3UFy9LFbi0reclm0vg0PIKZnmZfWSvl/XXa1AZrL1vhrOv6f5AVy9nJpyxYa+ndSwZw7Dq/Zj6VJt8lrb1jGQzGVcdr/7iqpfr9R7aXmeQjayzb4ZXyySyaEFmSD8oB5EMcMN5cIArHxrmbLuA5I0fIHDRRjz39/1gvSjekuV1vZum/J3eql3MUkoWk5lMA/KGIuwax8zKXQRD2N2qadMDQ0IrdQ5EaqAAmHcglrglDLs5IIow9FtmeAd4i9PAq6qOKUmKyFKcquCCqFR8gNfutMDi7IbFaYXN8BpQpRiA6xbguw7g8/bvsGTddkxIXIvJmc9g6rK3EJv3MSKXf4qYooOIKTmsCk8QaIuPIii3xVORjZBLEGY1NQ56C847iKC8/UoPTI/efNWYpdVNBr75lTKWwW0F1AqrpopcHEJYXqvSAxuD5qRqW95exK38DEkrPkLYol0InlOL0OznMSFhDRauqMf+Uz+Jm8MNAG83X0HM/McRmP0CYh77BwIf3Y3YghPIWHUaY1JeQkDWWlztcAsc0xDO7rgNl4sWZ17gFWl1D+BVMpMBnjJzNTMCQz4CkuEl8MYu3YiIkh2Y00iAuIQ5TVcxu+EC5taeFwieUX8VGQ3/lDa94TKyG85iVv0ZzGg4i6zGs5DSuIOuV7CpYSmr8TxUU8fE41LNWC5QdQlZDYRVaoIvqsFxTe3IbDqMrG1HkdXYLv6oSvN4GTMb/ynAy8xwZhMzjF9g8rrPkfI8SzN/hdQtZwQgCA8aNlS54QtI36o+08v76gU6CB6DsBE879buCppG3DSQ6RhRN53ddAkRGw4gbvNhsMww48k4aWjjNnr9/vrBGNd722fGRMWF21GyoBp9di+IHlh6sQS8IPFj3GY2cvoM0mpOYe62s8h46kMEPFqFp9/4XG7anSxB66Q2lXpUpVHltBq4xp4aT2Y2DW2v6Bs4TWIaKr0TLoebag6wWi1DIU8KogdRXqv8jHBrtyntMxN69i5mgVWST8L1pwGvU8rsCpQxmy+Zew28Lk+GVw9iU1XGWLhBFWDodrjRZXXB4lAlfCkFuPoz0PT+KUzP24LRiWsxMesZTJnTgKn5H2Laiv2IyNsn8BpVTHuxgwjK2YOQvH0Izd8vEgeCKCuyEYhVtpeuDMrCjAPYqMWVRh0v1yXAFhjuDz6gq4GXPaFX5guPINKnDDKzvbErDiI672MklOxG2srPEfvY6wietQVBGdWYkrwGK5/+AB8cvY7F5W9icuYmBM/biaSVrUhYeRpBiw8hvqAdwXNfx8j41dh77kdcpz2Z45bkwJn9p/sF48pMvxd4TUnDkCc3MwD3FQHJ8Ha4FPCGFDQi86VTSHjuJFKeO4mkpw/fOcMrsgblqMDXnTKQbVD13syVAiZfdwjfaQOMWP5VGgekKes20RQ3HUdmY7u0rIbjouUVsNp6QezdqBvNqDuFGdvoMXwSIWUfIWPLCczfdhWZL56R18UcIMbXxmx6muChnRse5r4/2BzQ8n5cLhhHDqiKqNyHhM1HMHfbFU/xC8aV1ys/f5jjej/H5jvgTYqSbP0CM7bSUcNwm6hRVQFV7M4i7aXjmFl7BDOf/RTBSzbimbcOiKThBv3zyW9ODsRxGs0ur7iZ1VTm+lbPK29qPTnKfyj1jIvTpSQLomvmwC8mS+X1tUuqnhES6b16q/OGVDQTqLS5ZAS/o5OQqQeL3de///e9kcrwGhptw32DO6OgV+l3OU0dsLZW43HQ44HV3OTYSXAUdVhssFidIoOhGxcHfx26+CM2NH2KkFllCJnzFCZmP4fABTsRW/gpwnM+R/DSPUhaf0pcHaKKDxiDzjiAjRXZjiC06Jg02pqJ20O+AlcZoEbYzdsPDloTkM0/goh+mqrqpqu7KTiW7y84LB7A4gSRcwCRufsQnfs5YvI+Qnz++4hevAsT05/GmIQNCJ+1FSMTNiF0/quI4TqFhxGwtBUxJWdUoYtln2H4tEpsfvmQ+rtj64Ab3fKgyL8r+pro+VyjYnvfJ9Dc0IzAEIzAX2gb84tFDVpjhnf+9suYtf2fWLjrGyzcefWOwEv/W2p+CQ+Dt/mMUveM2Dfs0Dzz3mpWyobsssgVOK0GE50SLXNm/QlMrzuO6bWnROtIjaNyEDgnmd/Z29swo2EPIqtfRuaLn2H+tkPIrjmAeY2tmFPfirkNhzC3oQ2z6w5iXuNhzKptluVz6lse2n5WQytmNbTgfnvGSsWnReLmPx9Z/gaSnvoQC7Ydlvgy1lyHsfVdl/F/mOM8sONrxdw6tsM92uy6NsxtOCLnaGZdC2Y1HcLcbZTvHMTc7S2Y17gb6U/uxLjZ+Xjy9X+I/RIH6XR7qnVZYQcbl1DB2Slj1p24BTs6YRuyTUWIkZHX2HyVjW7YcBtW901YcAOdrp9hxXXYoCGI2XEX3FYnHEZhgz9P0qBs1cSPV8jbV9pA3Sl1GXbJUtJKjQ4TXJfAqx5saGXAbL4LTocNnZ23pPQupQ4dbuCaC2j+4ifMKH4Ww2MLEL7gRfHujVj6DqJzPsGU+e8hfuVBxJUcFMlDZKEqHxycexiBeRzI1i7lg1lKWEFvm2SHI/P3S2GJaMnyUrN7eEDASy9g3ThQTjyCc9oQs+IEYleeQHjOAQQ99jGi8z5F8qp9yFx7AEFzXsOolDoEzX0LkY99Jq4Q9AKm1Rqb6JNz92N43NOYVbwNv7qA227+HaFVXReU5MUNVs7TjhxDkFPMQzYj8JtEoIeGly4NM2rOIOmF00h74TRSnj16V+CdVfcFZtVdGpSNThTZdZe9jfPSmGnV00ZRB5Z0lcbyrmyq1Kv40BqZX9+MGrNj1EDKg4AUw9iP2dv/gdlNryDp6Wcwu7YeC5q2Y15DExbu2In5TTuxYPsO6ec1bcPC7S+D/YJtu2S52fcTh+07VNy2bcf8bdtl+tGduySmjF/KM88i88WXsGiXiqfEett2ie3cxiYVW36HGWeJwULGofFlT1vY9ArmN72MBdtewfztuzC3aQfm7tiBhTtfwext2zC7oRELd2xD1jMbEbdiObZ8/Cp+QQdu4Qau4Xvcxo+4jR/QKe07dOJbdOIbdOFrdOJr3MY3Q7Z14Ft04Ad0SIwYJ7bvcUuW/xM38SVu4KoRr3/jp+4r+PHGP9WjRPdtdN/q+JM1vLTIMuzTxF/ZC7wq9ewU2BWJimeQlUPAzWl3wG6zoKvjOtw2Ph4RfG3ouPUruiy3ROf7S5dFCjtccwOv7bmIiNkV+D+h+ZiU+Qzic95Axuo9iF7+IcKWfoSwZbsRXXAIUcXHEFbQjtDCU4hYeQ7BhacQVHDCKF7RpqzJCvYiKv8zRBfsEVmDGqDm1e2KjEGkDszoqsasrm4cHKdaO6KLz4qDRNDyVkQUHhEv35DcPQhY/LFkkacVHkLEY/sQndMirhO0Wgtcdgix684jME+BdlxBKyak7UBA6uM4912nWJixqpoAr0PJXpgll1jrNK/Wdf8mGGB+iRmBoRGBv/CtE23JtIZ3bhP9OS9hduMV0fDO3noWM/txaciuPSXWZf6vQQfTPH1PezQ6LPTVjPWytl6Calekz6676gFgXQhgVt1lzK6/gjkNVzF9y2lM39qC6TXvY/a2HViw61lk163Bkp0VWP5yFRbvKMOyV6qwZGc5lu6qkF4vY8/ld2pc52Fudzp29VkpluwsxeId66U99nI5cl6rwvJXKz3LF21fh9zXq/HotrVYuqtMGtfj9N2+/2GOre/1pY9zyfZyqFaJJdsrsWRHFRZvr8TiXaot3F6GR3eVY/ErlViwoxQ5bz2BoveewpLtq7B463KsaCrAiprHUPz8PJTWLsHarQt82jys3ToHa2tmYW1NtrQ1NbOwpmbOEG3zsLrmUaypWYy1NYuxrnYJ1tcsxvraR7GuZi7W1c4G47NqSzZWPTcbK5+ch21/fxrXu/4tgwCpnaUM5M/M8Crg5Ug74xW7BjEj48t9VOIWuwxWpD+vLON6YkFhg63zJuxd4u1Bfw9pIoMAcK3Liv902HHTBXx1A3j+1cOInv04RsevwsSMTUgp+Aem5n6EqXmfYdqKFsSVHEFI7iFMyTmsQLfwpPRBhUelBDGLUoQX7EVkwWeIKtwjfrwcvKZdGXSvrcpE/1t4WJwcqAkm6LKUsdig5R1B0LKjCM09joii47JOwHKlJaa9GYtZROS0IWxZG6LyjiO68BRCc9sRuLxVLNTCV7Qj4LFmJKw6hfCFH2NYVBX+0faVyBpsbmbEu8EHAw5qVNlxm/dc6zgPDU4xj9KMwG8Sgb9wgPA1m5I0UMObtfW0ZHgzXjyL9BeOI/vF07KMfrIpNZel0ac3s+YkZmw9gayXziLrpQuDtmW+dAH31i4i86WeLevFi8h68SpmbGG7jBlbLqr24gXM3HoO2TWHMKfpbSx5rQ7xm+YipmoqItcHIaYsAAkbQhFbEYiYsimYWh6AuMogmec0l7N/mFscj/ke2rSKQPg2HSPGj9OR6yYi89k4adGlk2XZtKpgsOk4MsZsOuZ6+dDseY15W1xZMHzb1PJgxFaEgH00r0XGrjoE0ZVBCC+djGlPRmB2fRpiyichqXIKkkrHIa10LDLWjUHGmpGYvnaMNJlfNwpp69lGIG39cKMfhbR1Y4ZoG4f09ROQvm6Sp01fNwHT141HRuk4ZJSNQVbZWGSsHYe55aGYuzYKGxuKJRNswy10WTv+5NfchFwqtTnazl9TquYV3KrsrQxYVMpuES47rTYladAuHeLaYYHbboHD3g1WKbPaHDI+73oX8NMtiAPI6W8sWPf064iaVYZxyRXijjAt9x3E5PwDgYvfQ9xqAmorAvNaEVRwDMGFxxFc1C6wSguyiKJDYldGaUNE3n5xbaCLg7g2eHx4lR+vFLCgFVkhIVWBanDhIYFesUjLO4JQNmaCi9oQVtKKiJWHEb2yHdErjyOy6Dhiik4jZFm7lDEOyT2MgOXNsl5w4UGE5LchuugYpuW2YdS0Z7H2ufdk4JqFVn6wwWazSCbc4ewW6PXc9f3D7fnAnDAjYEagvwiIpIEZ3siFFZi49EVMe7IFQeX7EFK6B4FrPkTIqg+ln7xmN8av3SttwtrPELD2Hwha8z4CV3+KwNWfDdoWsPoz3FvbjYDVun2GgFWfSAtcsQe6Ba3cjeBVexC8+nOErPwUYWtfQ9yG55H1YikiyiIRUzEWseXDkFg1HCkbRyGh8hFMK39E+sSqkdInVI4YEn1SxQgkVT6C/vrE8uFIrBgG9n2tl7xhpMQxvmIYkqpHgP28l0KkMa5sXK4/4+eMO9u08r8ZMeZ6QyPePY9zBHi9+bakipHwbcmVo5BYpVpCJdcdhaSNo5FQPRKxFY8gffNELNoWjWmVw5C04REklv0vUsr+itSyvyKt/G9ILR0mLaVsGNiSytn+arRhSCobjqSyEUO2JZePhG9LLRuJ1PIRSKkcjpRKFbPkdcOQWT4JWWuDUVGfj+siFbmBDmuHDAL7MzK86oaiBqQxG9u7wIgqOKLkDNTy2uByKtcB6lIdFiu6bneq7CWt2ZipttukOa0W+dzeTScP4Mb12+wElX+6SZUzcMMJ/H3vKaQ+thmBM6oROvdZRC9pQkzeG4jOfx8R+Z8iZmWb2JMFU3tbpDS3WoPLzC0zsGJXJmWKWaDiAFisgo2ZYNqb6eptIUUtBvC2QgGvskCLpHMD/X/p51vUAkIsW1BBi8AsYTiy8CTCctqlchzlEsG5+xFWdAAB+XsRvvI4gnLaMDX3MCamNyDx0U34yaKqCrNEsQLeLjictPZj9tv4IxlyPWP2ZgTMCAwkAiJpoC1Z3GOPiy0ZJQ203Zq77Utw+k62ZLPrz4h2t4cO1lcTOxinfbS7vjrePo+R69adAcvIzqq5iNm1l6XNqbuMufVXpc2vu4J59Ufw6Pa3kPP6M0h9ahqSNo5EYuX/QVrV/0VG1UikVYxESukIudGllY+SXt/4hnqv49JfHNIrVbySSx+ROBIWlrwUgcUvhqs4lo8A12FLKRsujfGWmJcN96zT3/c/1MvLvLAlx0nYKhuNlNJRSF0/SvVlo2UZlxNMBc4qRwkUx5cOw6xnJ2P5tmgkVf4NKdWPIKGUMDtMXcsV3u9PqhgNtgSjxVeOBpueH5J9pX64VQ9m6mGED14jkFg9DInVf0V85V+RUP43ZFROQMa6YFQ0FeMGfgSVnp32bilD8WcCLzO4bL2Bl0VGWChDaXwJaxy4pjK+1KU6YbdyOw7GUmWmmSQWRwKHG26bCy7WI2by2A04HC7Y7aqsLqu2Xe/sFPD9rgt45vV9SFxajdGJeQid/xSm5jRhauFbCM8j+H4uAEt9rWh786nxPYnI4jOILj6NyMI2SAGKAhaV8AJvaOEBBOfvg/RFBxBS3AwFvQpqNQhTAkG7MwKyXjeooFn5Aee3IiS3VUA3MvcoogrbEVXEAWv7EVq0F1Py9iBizRmRX0hVuYVvY1RMMc5800UDP6msZ3d0S2U9leHlUuOPKWnQkTB7MwIDjoBn0Bo1vCw8QdeF1JfoxclX/afuXGmt9pSydfLXwQ62eamMpipK9anf9dX08tg865+XanOsOJddc148S1mOObtW2TjNrr2EGS+ex6ythzCv4TUse3kTkp6IRvLG4QK706v/FxkVI5BePgpppaORVjYS6WVj+pzXy4dar+PSb09IKx2O5HWPSJ+yfjiWbAnH4hfCZZ4xzagYhYwKxneEinH5SIlxaqmKPeM/1OLK45WHK4Fcgi7jOEJAl7CrpAcKfDnvaQLAIwV+E9YPw5xnJyF3WwySK/6G1KoRSCxltn4kUipGgdlhL+iORUKFavGVYzGtcjymVY4V6NXgO/R6BbzxVepNQ3zVCMRXjgT7hOrhSKgeplrlI0ivmojp60OwYecq3MI18XKwulXZ3j8TeNXgNOqInR55BfdHN1/oJfx6pA8ENuOPTGqA8+0JwFoeTCiWIhvKCs1m70KHpUsqk7FU8cWfrXhyxweYumAdxqYUI2zBM4jJ2Ynk1Z8gZNn7MoiMcBpVfEJ0t1OWHkF4Pqu3cVDaAbBymgd4iw14zd+H4KIDPZuRwSXwEnLD8poRlr9ftuX2YUXNCC0+pLyAC4+IA4RkgfPaQEs0ZpQJvGGF+yUTHFhyAoFFJxGR24qEvE8wblopdr5/El2EfDhhd3XC4bqlHhYkfj5B84mhjqXZmxEwI9B/BETD+6tVaXgJvCw2Mb3uogxaY9W1O2V4WXhCfGPrL2HGIG19uTL0yOz2yPhq9wbdn0d2wylpdKpQ5VjPYabEhb665zG7/jLmNbRj4ba38Ngrm5G4YSr4ajhp/f/Ia1+B3XLCxzgjkzYWaT7zXK7nH8Y+vWycgs1+eupBBXZ9eoFTY31mIwluKjajkLx+JBa9EI5FL0QKxOnl7FUWc7RnfWYt1ecq/mr64Y537+tpDFIqVPMeP8+Jt/EceM5Thbo+GTvGc87mSchtiFJZXcofykcgsXIMkivHeGDXC70qo6uyuwp2+XeBGU1KWoZkL7IbHv9II9vNhwIlGRHoNeQ+6eUTkbEuFI/vWo3buIYusGIZ8VHBZf//xP+en2ghqbfily+vqmnuITO9qmngVUCsPIT1dK89Fcg1KswZwOvZ3mWD3WmDzeVEh80h2l5W+Dtw9husenoXoueWYnJmKSbNfAqxuW8gZfVexBTuFf/ekOWHEJJzVLxwZVAas7P5LQpgi1qkbLGvPIESBZ3R1b3AbsFB8f+NLDqIiKKDqqpbkVH9rbBVYJqwG5XfKi0it1kBMkG54ACCi1sxPu8oAopOCghPy/0YwRmbsXRlI7pptUz/avdtON0datCaDATU8TDS4b2CZi4wI2BGoL8ISIaXFW50hleXFmYWkxneOTSarzuHPksLS+EJr0eteM7W+fjaPujTrBjVy0NYGewrePWd7mvdc1JaOav+DDz+vHVnxZN3ev0pZNadxvSXzmBGTSvmNb6FJTufQeKGOCRXTxDoyqwihI1GSvk4acllY8HmO6+nH9ZeARghs++WWjYWuvVeZ4x67V45FqlGSywdiYVbIqUllY3ywJyGul69EfuHNb53PC4DdAmnuun1GWsdd/by4CEPYmOQXG7IHgi8myYjpz4alJSkVI1GYvkoJFWNFegluHm+V36LWV/1W4RiBcY8R8wID82WXG7IRMrGIql8vDRmwpVuWmWAGZv0sgnIWBOIJ3etQRd+gcXdLQj55wIvbysaetUtpm/g9UKvILpkgA0Zg9+dSWeGdc+PrVYrHA7qgBXs2SxW5V7AAiYsbOFy4FbnbdH4Ut/73S3gjd2nsLSiAZPS1yBozmaELW5E+LI3EZH7CaYWtyC6WFVXC81rlXLFouk1BqcRdtUANcoXOECtN/DShYGNcgiRRMh8qzg1cGCcgHR+mwG6LWKZxgpvlD9EFCltcVDhYUwqPIFJlDqsaEH08vcRkvkiItPKcMMCWJ0s03ELTvdNAV7KPyTcEgflcdAqA2EAACAASURBVKwW+AXRnDUjYEagzwiIhpcZ3uhFVaAPr87wzmL54LpzdwVeVlfLZInWQdikRKoA770Uz+C6qvHYWZaYNm6Ztd8gs+4riUNG42lkNJ6UssOZdWcxq/EoFm5/B0tefhYJGxKQWDERKesV4AngVo1FStVYT1aMUMB5ZsY0IDysfS8ANSBMLxe48lvm+axyDBLKeg6yii99BAtfisCCF8PBab5Wv3Pzwt7DGuP+jovAKdlEybISrhhLpbVl3Nn40MCHCGbSGXcuSygbjsRSNdhszlMByKmLlcx6csU4JJaNQVLVeCRWjpPv5m9T3iBZeGqnJRvvfRDxZtiZZR96TWXKRyO1dDxSSydISy4br2JdORLJVcNFfy6ODasn4+ldK2HBL7C6O+FmfteQD/T5r/sfsrBv4NXVkSm64Kt59kqA4eFWtXeENw806+8iIKtmtbMstbENX/Mbml4utFtdsN3uMkrTuUXn221zi7HZLTdw+Wcntr57FNOLt2JE0loEzt+K+JIPEJb7HiYvfQ+RlB8ws5vfhvCiowKrrNTGAWe0HiP0sif0etwaCMUFhwwbsxbJ2FKmIN9RoHTC4YXHEFF4VHx5md0NzzmImMJWQ9LQhsiidkhxjKKTmLLinABv9MoDmJr3PmLmbkNA7Fq0nvgONpE13ITDfQ1Ol1Uq8PUAXo9E5A850eaPmBEY9BHwaHinLtkgGt5ZDReQXndRCi/M2Erg5aAsZngvIaP+S6TXf4mM+kvqtX3daSknPBhhl/usywkrgO0rg9vXMl/gJehfREb9FWTUfYMMAd6LYLnh6U2npGXUnEZ2wxHM2/62F3irpoA3tfSK8Z6MGAGXACLAYQCwzpCpTJjOiP2+PX/rfhrB5l73sz8QG/hyNXhKsmHlfCU+Ul6pL6yNEuiNK1PAy8+120Dv/t73+16P80FeP6l6NHyb3lcFqkrqoOGXWXSVxR0hcSbIzns6CLn1cQqKy73Am1I5Xh7W1MOJF3iVhEdLULSURMknmFWmdGIw9P5vG+5vv9UbHpVBH4+09QTe8ZJZ13FjdpcDLNNLRyNz1QRsfqUYFvyIbufNHprZP+9OpCFV7YEHTgGxE+M8gVcDLOedrKzGN/IyWo3bGa/n3UoLzC286xN8uYZbtuEMKxLbWXBOU7XNDlgdcNsdcNpduHGzU2Cx06jW1nblFioaP0f4gicxIrkMk+e/hJiCNxFZ8D7C8vdIpjayWNmK0WosMP+Q8tml565UV2uTksVK9qAGqOkML6u7qcbBb8waexsdGWIIvrktUk6YrhChuYc8wBtafBKTS05jUtERhBXvQWzBB0jOeRtT4svwbMPHAu4WVwdsruvgoDVWp4OLAeATAGPG9uedefOXzQgMtgj8hf/m3HICkYurEFq8EzMbLyKl9iKyG6+CwDt762kDeBXYEXjT6i8K6FKrKllODzxqiHx4ey2B4MA0ThOaCb2ZdVekUdaR2XAGmY0nkdVwUga4Zdcdxfxtb2LJy09j2oYEJFRORFLpGKRXjDYyOQZYSDZMZRwlm/YnzA8cNo3MaMU4MLPHV+E9emYD77L/3pu6N+OnX6nrXss8PFIPT7aX24wS1wBalqVW8nX6CCSsfwSLaqIw//lQD5QRcv+seD7ov9vrfBvnkvEn1KXIdTpeMrsEX8aZcWeWl7rTWZsDkNM4TTK+GozlbQVlD55zpa5vlenV0z17Zne5/r336tqTfdX7fE/9/f2u/7Hd+36r39UxUxleBbsic6owrm/jIZjfT4/eTa/moxvfi6RBQ6OA4wN459Espvu77yLhmX90b8z6dbxnOZ1ugVvx8rU5ALsdbqtVNbsNDpsdFqsTv3ZYxM3hmh3YfeJ7lDzzNqIXbkRAVgUmz34GUXnvIDj3A4QV7kZ4cbNkciOK26WQRODyw4hecQoRhcekmIXW+Qbn7hUHh8gVlDQcQFQBW7P4+OrCFTJALv8woqSxfLFyc6CkQbk6HJLiE4TdoNXtCCneh9Ccd5GY9xYC0irx6IoXwDevFnE5toIWZcqWzKX82Qj7DNPAg+sXRXPWjMDQi4AXeBdt6AG8M5uuIqtGA+8ZGZDFzK4XeC9IlldlSR8+wNVZa//j6w28StLRQ7/MB4EGBbwzay7CA7yvPCXAG181UV4Vi1VWLyjoCQL+N9bfe74XAPnoO/v6TINpr37Ax6Vu7J7j8tPU3h146eGqgJe2WQTexVsV8HKekKXanxtXz/ENOC5/zP7qc+rZPyP+OoN5b8CrbMgIxbQv+0Pi7ne99LoO7/b5fZ6PXnG7z+9Rnsda0tATeOU3KsYhqVzp+lmk4snXctFpAK+dyT5pQ4t6CPjMdlLXS2szViODwykevrBbAKcVcCpPX+peb3c70OWEODp8fRN45bMzou8l+D6SXIGQpTsRU/wPhOd/iJDcz5Wmt/AIQvOPImBZG4JzlAwhqrhdBraFFx9C5IpDCCnYb1ieKR2vqtjmW7WtZ7liSiG0hZnIIApbEbzyBCYVtGJK7j5EF+1FYtE/EDRjMxLmb8D5f3cLrLMIhR1WuIm/brsCXcIuoXdonfqhR2jmEf+mETCBt5/s9FAFXg/4DPQG3h9QDHR7fyD1+z4TeH9f8O0Fbkb8Bw3wDvg6+33jeM9/b4z9NoH33u9nBF6CLIGXmV7x8qVlmcshoAsXK7upQV1Wa7cwIX0kOl0KemljdubbbjR9eBxz1zQheM4TmJy9GWFLdyBs2d/Fuzd6xWGRMcStPSP6XhasoNwhKLcFkSVHELXiKCYt32cMbjss2dqQIm8vA9fo/StNlSamDpjQq3t+Flx4CkH5pxGWd0wGuMXnfY6YR3ciMK0cr396ViD9tpOiEKPkMmGekMsHHR6U+ceMgBmBAUfABF4TePt49XwPcOAHqJ4M24BBxC8D6/d9JvDew7kYcMy932kCrzcW9wut/812JvAO+F7VY0VCLrO8BvspaasM4OMyVeyCvZMV3GTAHNBpc+G2U8kEugD8agPar97Apu27MXVeFSZlVSJyaRNCl76GgKXvIGZVq9iRsWRwZEm7tPAilhFukwFu4SXHEFx0BIHFR6TntG4hhUd6QDABWA986wG8OScRVXAJkXnnEZHTjmn5rYhd8i4mJFRi9eZ3BHg7nW443BQ3WKVanaR43UrO2yMo5owZATMCd4yACbwm8A4IeKkh7Kt5ANcPVAcOASbwDjxWvz2cmcD728f0Xs6nCbx3vD/1+6HIGjj4zXizr5yA9VA3+kEo6KXgVUofy8A5pQLotNvRaXOKjRm9jH/qcOPAqW+x+pk3MTlzHcbNeBwROa9i8qJXEZL3MaauPiTShcDle42BaYcRtLwF1Ppq4CX0KvA9jGBPppc2ZapJ6WHx31UFKyhpiMg/gojcM4jKuYDQZWcRk38GSavOIGj+2xib/CSyC+vwYxfQ7QasdiI6TdcIvjwmU9HQ78VhfmBGoJ8ImMBrAq8JvPeRGb0XqHmQ1zWB1wTefu4ND/RiDbwEXWqZ2ThNAFYeD8z0KthVS+xgmV66Hah6xQ41b6dUQG37z1+seGX3Wcxd34TJM6sxcfazCFy0DVNLPkJEwccIXPqReO6yPHDgsoMCvb4yBuXd2yoyB3r5soUXNnt0voRcNvrxssmAtpwTiMk/J+AbkXcKMcXHEbrkYwTOrUfUnCfQcv5HJWbwAC/31yHHa0p4H+hL1Ny5BzACJvCawNsDePvK4t5pmZnh/XOB6b+F6aEOvPr4/fv/Nq4D3d7M8N7fXVHreOlU4aBjg+HQpYGX0Kt8f11wum2S5VWgyyIWt+Gw3oDb3gFH53U4uqnqVdD7qx248KMNte8fQ1LOs5g0owqB87YgetmrmFb0EWLp5pC7D+H5bYgpOebx49UFKFR/QNwb6OCgG50cIvNVi8o7ALbofBabaEV0/lFEF58Wb97Jj+1H7KpDCF/2GsZnlOHF1/YK8Noc3YCLteQIvPQ2Vvtrjlm7v+vH3GpoRsAEXhN4TeA1M7zea2CIDVrzB109P1Bg/W/XM4F34DdeQq7+w2k9zwJk4lZhmBborC+hV2l86eJLiYMNbnc33I5bgG7OLoAet9YuWCwWWJxKOPBdJ3Dym06UbnkXUXOrMDJ+NYLmvITIJW8hcvmniC0+jKlFRxS05jcjOl/bk/XXNyvg1dndvIOy7bSSIwheuk8Gt4WWtGPM4t1IKG9H4GOvYkJmFfKrG5WIwWWHy37byE4rKYeuwaFjYvZmBMwI3DkCJvA+5MArxQLuA+j6y+qml42Bblznbhle+ozeGQpMDe+d4/P7ZpB7Ad4gBV7tZ6v7gcZUV5bzr2p4t+3134+7rXe3z03gvfMNyv9TDbq6FzErBa0uBcAEX2NWes+gNsKv2wmX0wqnoxsuexfcNlZpswMOK9x2C5wOCxxOC7odFty22dBhc+LXbmD/yX9h1dNvI2zGRoxOfgJBC15H8NKPBWDjCloQk7cf0Tn7EZ3fjJiiFkQXHhTpQ2TRQSlKQbsyKSlcoPx4IwvbwKIUkiUuPIzg5QcRWnQMgUXtmFLYhsCCvYgueh/jplcjbWk1/n3NBrvDBThpS0acd8mxmZIG/6vDnDcjcOcImMBrAm+fQKpv6P69hl326jNl/N8LfA3INoH39wXWuwHV3T4fysDLYzeB9843iAftUw26kt0VsmX1MaMCmcwb2V6XT1EyI/PLPC+hl/ZlBF8XHRxsLjgtDrhsVgFfSMb3tmSBnS4Lbt7qkKFiX18Htn94Edmr3sCwpGcwYc7rCFr6KSJz9yMm9wCmFrZK1pclhFlRLTi3WYpVBCxvRnDeIZXBLTiM4LxWabQkiyo+hsi8NoQs56C4Y4hYdQqTcpoRubIZyWs/RdCczYiesQq7D52TTLWqrMaKcjY46br2oJ0cc3/MCDzgETCB9yEHXn/g8c+AaeDRvf/6rPrk29JKR0M3LtcVrkzg7Rts7wT8+lzcqfc/H7/1fK/zPsgyvKyix6YypSM9vV5+t57AyxLe/hneXnExHuD8HwD/2/NhZnjv7Q7ZN/CyGIOGXi/w6sq7Osvr6Y1iHZINdgIOO6Rqm1AkB7U5OgDHNcB5U2QEnVabyAp+tAG7vwD+v6lVmDD/NYTk7kFEbrOUDma2NrzgMMLyD4sWNyifHrvHMXXdFwhfcRqBBUcwObcVAbltnrLFhN7YopNIKDmDwKWs2nYUUSVtmLZiP6bmvIn4JS8hIr0Iz9W8KfvosFgB0W+oSnScNP+YETAjMPAImMA7hIDXF6z0jdo3wyU3/XJWyfI2X9gVwPUD3l6gq+3JzAyvZM4ZSx1r/943zv1N+2/zW8/3ArshBryEXRN4B37DeBDW1NCrNAsU8DLdaYxa01TLHSXY+i3WH7OXhDAzwWRlsXhg4QoDeO3XBHotnb/A7nTgp9vANTewrqYF/xNfjcAlbyOq+KCUEw5l1jbnEELy2xBS2I7QohMILjyJoIITCMxvx5S8NgTkH0ZYyVHErD2FqauPi4NDwNLdCFzwCWJzDmJaAQew7cbYGTsRPG8Hpi2pR8SMCkyZuhDVTzbCTTDvJpnzgNRB0YfY/GNGwIzAwCNgAu8QBV4NYjrD5Xvj94VgDWL+4KvnTeDtO7OrwVTHT8/rXi+/W6/X/716E3gV8PL6T+yjjLaOu39mV8/rz++3NzO8A79Z9b0m05x3oFqSrc8fX30v2ZHSAJXhBdxc4GJZ4tuAs0NcETpuXQMHh3W4gf3nb+P/Cc7B2JkvIrr4M4QXHZBSw/TaZUEK+vKG0Vas6BiC8tsRkKvKE7NQRWg+JQ57EJrzCcJzP0R04SeIL/4UaSW7Ef/Yu5iSWY+gmTWIWVCL0bGrMHlaIWYs2YCXmj7AwdbzAu82i13KKEtFObhEnuFzaOakGQEzAneJgAm8QwR4+wIrDbYadnWmSy9nr7fTgOvf+1ZC6zHtkyW+Mww83IPWdPz8Y6CX36333+63nn/Ygddf6uA/73vtm8B7l7vFA/ixb8bWs3u+C32mVWZYJUkFdp1uSZYyu+tyuAV43S7qY7thd3SBVmBSmOK2E9cBLC7biUlZmxCx/E1EF+1GWOF+hObvR0TRIUQWt0rZYJYepk43vIAa3XaE5CgHh4QV+5G44nPE5r+HyCW7EDx3KwKyn8X4pCpMTqlEyPQqxMzegPyql/Hm5+fxUydgMfyFmce12wCrlSWTeZQK8qlHNv+YETAjMPAImMA7xIGXwMMbvX/rBUL9aBh7QG7ZWHjmTeD1SBo01GpY1fPs9bI/q+91nh8ySYM/4PrPm8A78JvFg7Ym2Y/IJ2oEY9q7jzrz65UACCgabg4sTcwmAMllTs47pFmcVnTau3HLYZcBa784gDf2Xcb/Ri5H6KN1mFr4AQKWfWgUlNiHyKL9CC/YqzK4y/cgPOcAInObxbkhsaQZscs/RNi8XZg8/XlMSN2AgLRKhGVVInpWJeat2IK6d9pw8uvb+NUJKSVMp13ao93s7EK3xSYqBouVtmrqj8OhnBoU+HqP2JwyI2BG4M4RGALAew5ZflCbWX8BbL7LZ9b1nOdn/utwGddjy65VfVb9ObVe3UVk6VZ/BlkNJ6XNrLmI7LqjmL/tTSx55SlM25CA+KqJSCobhfRKZjfv/Er8t/rcF7J6TFeOQmKVaglGL/N9vN4lHPnvD79LZ4Tle32ht2wsUsvGeq3L+jzWQZ7h9dMsq/ioY5IBU+VqIBVjo2PnG3+PJERio2Ph3V620b+h+z7j2Pvc6N+7Uz/YgTelwoiVxHkEkipUS64c6TeYzXud6uvV05eP9Ru0xoFwY6Tp2GkJg2+vP/tvegXgo5FaOl41+TvDa8XYh4pxSCpXf4fS10/Ak6/lohPfw+Lulmpb4j/L/w3BP30BrzcSBvAa2VBJ5XqmneLhqyQMLNGmRq5RKkC/Xpvbji63CyxH0QHg/M9ASt7zGJlUhrjCdxCT/wliSw4hqqAZU4v2IbbwM1kWV/gpEot3I6HgY4QteA0TM7ZidOKTmJC0EYGp1ZiavRHzimrwTNN+tJy5jh874fmNG3a3lJS4beuCxdkNl+SW7XC6rCJdsNisAr60J2N2mn8E2IfgeTcP2YzA/UbgzwdeDYm69wNRDaf99QRO1S4IcGogVT0/OyOfZ9ZfhGoXkNHApkBVrXcR2bUXMbOOTYOv/l7dq+VqHd91Bxfw9rw5j0JixTAkbxyO+Kq/IWHjI0io5vQIJG8Yg/jyUZi6boRkf1M3TkBC5UhMKx+OuLJHkFJFSBiJhMpHjDYCieUjBOSTSscI6PImnmbcyNPKJngdHQhsVWORUqVu7ApaDGDTUGf0noyxBmcP7GnQ4W8OR2rlaCSVjUDC+keweGsU5j8fKvMaiHoe9/3BYa/vKB+H9PKJHlDh58nlI5FYPlzimlQ9AinyEDESCWXDJT7xpY9g+hMTBGji1w2X7RPXKBhOpdtA5SOIXfd/kP74aMSVDVPgVTEByeUTpaWUT5AHiLTyUWDrtU+e+AzsGP3BTgM43TfEgaN0DNIrxnulLZXqnPF4eP5nbQ5ATuM0JJYOl2Pn8fNcsO9xXu9xvwZ6XGllI5FePhJpFSORUjkcyVXDwbinPj4aCXKdjkbC+hGeh67kinGYtm4kEivHIX79KKRWTETi+rFIr5qIhDKeuxFIqByBjMfHY9r6YUirGqdcStbTf3oc0ivGih0fj43HOdD97G89E3jv99altiPg+rZ+v80gYT3gTWV4PX5lSufA4g5OC7ostwQ+KWP43gmUNu3G35LWIWzpLiSU7EZ07h5MzTmI2JwDiF7yMcIXvomwha8g+tGdCJv1gkgUAlPXITa7HLNzNqP6hbex5/A/8cN1VSeNGVwb5Qoulcm1up1wwCmV4Vyww+GyStljq60TTqddGveX++77x3/e9zNz2oyAGYHeERj0wJvZ4A+k3izszDp+poCX2de+gVfBrgJe77ZekPb/fg27XjiWTLAGdvYPcIa35413FFKqRyC5ahhiy/9HQW/1cMRVPCKgxcxSWvVkpFZNQkLpaEwrG4npm6ZIH186QjJoCVV/Q3z1/3qgl6DDDKaG3bT1EyCNwFumYE324aEB3klILZ2gAL+SED9KoCvRAC/CLyE3tXocWAQktWI8Ylf8DSmlY5C9MQjppZORUREg2zN2adUjEV/2VzkviVUKzBIrTeDted0qmJdsa+loZFTQOm+kPPgw/nwIiy8dJkAu1yKzpqXjEb9qFFLWj0da5SRpGdVTkLxunJy/jIpJAq+p1WMQX0GY58PKaAFc0a2vp++0Al7uC7P36oFtYA8Wfe0/l5nA2/um9Hsu8QdeDloTYhZRr00NWoMDN612/OIEjn1nx6iUIoyf+QRi8t5AXP5HmLrsQ4TOfhsRc99C8MxGBM94HsFZmxCZvREpi55EQeU2vPbxMXzx7U10uYFulwJcmwuwOlSjwYJyiXDA5bZKYyU4l5sSBpY/tkm7M/D+npEyv9uMwMMXgQceeH1lB1pm0CPby0ytka0V8JQM8TmP9EBB7zkfuYGSKujv8GZs7wS7Gnq936sBWe+TR84wyIA3qXyYZBUTyv+GlI1jkLZhLBLLxiCjYjIyq4IQv2Is0koDML0yEAmrCWyTEL92NNKrJiOxVGV4CbwEPAKazurpV7QPO/AyQ5hcNl60ywK01aMV9FZSyjAC8euGSYY0qzoASavHYGZlMGZVBmNGWQCSiscisWgcZlSEIGX1eKSuH4fpVeMlhsxUJ5aPAjOSyczwGs3M8PoAJrPQxoMUpTOEW2Zppz8xCVEl/xcZlRMwc0Mg4ktGYGZ5ILIrgiT2SatGI2HFCHn7kLZ2IqaXByBp7RgkrKOf72jJ8sat+5tALaGawMsHFH/gFej9LzPXJvD+sTfVnsDLUhSAze6E08HhabQ3swto0pnhmg1YVv4SxiXlInL+ZoTMeQbRjzZgYspTCM/agvh5W5H46GYsXF2LzTs+x/4z3+OHbq8OtxtO2GCDxXUbnfabsDpuwoluULxA6YTT6DlN0O3RDD0xdcWEXjPD+8deJ+avPZwRGPTAS2mCyBMM8NWZ2Z7SBi1T6KMXGYM3W6u376/X3+sF3sEtaUguH4E0Q1pAYKMcgZmwtPVTMH19CLLWhyB9VRAy14YisWQiMtYHIXnNRMx5IgLhBf8jr48TqocJ8BLwemR4yyYgvXSiNA0m+pW5epWuZAkakiULNpgkDYQdvb8CProAAuUd6hX/9KqJiF8xEgklo5FcMgHZa0MwrzQKGUVTMHttODJXBmHhxljMKA3B9NJApK6biMyqADDjyGwwv19Br+rl9yp0lTsf+LtP8BrUkoZylZ1Nr5oisoSEdWMwY1MQpq4chtlPhiJx5SjMqgjFgg0xSM4fi4ySychcMRmPPRmPhNwRmFsVjow1kyXuKWvHScyprWeWl9IG9ipDzAwypQzjPBIGXdCiv8ztQJebwPvH3lg18LJ3ul0y6M3qVFCpgffatWuy/J2PWxCZthSh6fmIX7gRsXMfR9KCp7BwxTY8WbMfHzb/G/+6Dlw3BptZAHS6gds2CyzOLrjRDQc64EIH3LgFN24brRNOdMLu6qT/gsCvL/RyWjkweAfS9QW8f2zkzF8zIzD4I/DgAa+WBvhpeb2ZXm+2lVBKPW56o4Le3uDrzdpqUPX2xiAzn9+RrK/OGPv1GoD19oN10Jr/jZi6V+oURXdLze36SQK1s8qjkL0+ClkrI7G4KgXr65aiansuNr6Sj5SiyUhZNQWJa5gRHiMaYNFOcsBQmdLxiqRBsm8TkVbG5iNpIMR5BgVp6DXgTQOk0T/QGl55te3VIxOCCEpshHjRe5aOl+wtoWtOaSQe25CC0q2LUPbSYmzeVYSVz89Daf1iFL+QjSVPpiB1RYDEP6syBElrjQF/Bkz3+WBwn6CrrwPveegZf8KdAN6DrOGlxKBqMgi6IlOonoxpa0Yh+/FgZFYEygPG9BWBmFESgtXPz8GTO/NQUbMIFXWLUbp1PtbVzMWyTcnIXBOI+OJRyKoMkL8HhFw5h1XUhY8S6NWD1bxxUxXe9Pz99ibw/vE3UQ29zO92O62Sa3VDDVjTzge3u11YXfYUkrOWo2D9Fqx5Yhea/t6Gs191o4vSBGaGDS2uzeUGodnuNLLE8mk3XK4OuNw3pTldN2B3shGAOz1ZXg23UvKYZY/7aNo9gr0GYW1N9sdHz/xFMwKDNwKDFni1VIGQKoPQGs8gs4FNSRwUoBJ4teaWcgSvJIHLRYZguDFoiYPe3r9/WIGXA3oIlSnrxyGjbBIyy0KQuS5YYHfm6mgUPT0XH7Rvx4+uL9CBr/E9zqLg6WxMy5uAlLWTJAuWtOERpVstH+4FXmbEhgDwElio5ZRsoFHiluCbUTlOXqmnrh2HlBXjMbcyCgXPZOLlPU/h0rWD6MLXuIkr0v+IMzj8rw/R8OnjWFCRiISCKchYFyyv2hlDBbqUi3glI0ra4KOJvk/wHdTAWzEGGRsmYNqaEUirnojpGyYjsuivmPtUBFLXTEL6yilY9ngaNjTm4sTXH+EGLqMb30h/G1/j0o1mvHe8Fmvq5iKpeDySVqprlgPYGBeeW8p2CMAm8A7em5z/nvsCrwMuOOQ/O7qtXbDb7bDbnbh06Rs07vg72o5fQZcT6HQB11mEDYCFlYw54MzSBZfTCnr3crCb09YJh7UDsHeq4hUO+jx0SwELMOPLohbyDQ443QqQewOsKijB5Rpw9bSe98KuYdfgf4DmvBkBMwJ9RuDPB16fDKtkcTWIGr0XWHW2VoOr6rWGlrCb1XDa69gg7g10brikmgxeMwaw8TeN71fA7M0Qa4mE7jX4eoFX/W62wLPKNvM79PcpiH5wbcn8M1G8sfPVOQdeZZRNkcwts4zLn85E1Y58nPv1IH5yXYIVP6ID/8L3tvMo2DQXKUVK2kC47P/AHAAAIABJREFU48A1ugv0kDQYlmQ6s6t7/UreC1qDPcOrBi8pQKJ+d6wMUGN2NGndKMx5PBQpK8ciuzQUR75/Gz/hHL6zncQ/bxzG8a8+xZVf2/Cj/Txu4V/42noGr7fUYcmG6UhfFYa0tQHqoYFuDJXDVRMbLkopCLsTlaTiPmGX14L3PAzCDG/FKCTS9WLjOOmpe567ORQhS/9fLNk8DTPXhKJ6Wy6u4yo68S98de0k9px4GwdOf4CWLz7FD85LuIYr2HflNayqmysZ4cyKKZjxRCDSN06Q88jvpEyHch+Jl+E+weteHnb+i9jz+8wMb5/3pd99IaGXGV5R0rrtsLps4BL9x84qwwaefv3vXxXoOoFuqwUOp0VBrLsL1q7rArpgdldKHEsVC1WrWEamsZybC06LA3arQ6q6sTIwB6xpezH9m+w9MK59go2KajrL2xuQfbc2p80ImBG4UwQGPfAqIL6AmfWnVRNnBmp1CaME0Ste4KU3ro9vLl0btEWZBlz//mEHXvEipc/n+vHIKAtE+togLN2cgr+f2oqfcQnfu8/j5Nd78e2NC+jGT/jZ9iUKNs3H7PJ4pKwLELut+AqOiKdrAzOQCpyoeRTdoyFlEODtoYHUPqeDG3jFgkukDJQzEH7HIq1yAuLXjkRM8f8is3IyZvBVefFYfIcjaP/hAzR9+gSe3FGMqtoCPLNtDT49/Cr+Y72E2/gP2r/dh8JnHkVyQTCyysKQzvj1AF4jxgK7JvBy0GVKJdtw8bVOXT9K4h1fMAZFz8/EV9Zj+AmXcOqbA9jy8kYUVC/BtDnhqNy6Bu+07MQvuIIfcAavHnoW6asDkLhKve3QA9g46LAv4GVW3wTeO91aHuzPNPAyY8vmFPx1yQCxjg5qbgGrzYGbnRbQXcHicMLGGsTU10rm9jYcll8Ad5dydpCSxDaARSGkuQCHC25uzB8wbIFddGcwHBp6mowp2FV2aaooBqe5oZnhfbCvJXPvBk8EHnjg9WROPZlgreE9IxKF7NpLmCXFHU4ju/4kVOaV2WBmYi8iq/ZLgd7MhtPIbDwusgdCroLdvoBX+/SqXksdtIZYfe8543cGX4aXr2fZdHZPikyUj0J6+SSkrpuM2MIx2PhuPs47D+DYzc+x68DzKHp8Pi7+6yis+Bmd+A+WlmUhbXUkUssDJUNIhwZl9O8FXq/2lg4GysVAZ3c1FKt+cAOv51j8tMdyrOV0XZiIqSV/xZwnwvDi5yuwbtt8pBUGIDFnCmYWx2JOfgKKqxbh7LeHcR3f4xd8jS3vbkBinnr4EOAVj1kD7ETWwMFU2pfXO5DKsy/3kHUc7Ble+u+mVQwXH15akyWvHYXZG0OQVDgOr7RsFgnOdXyFytpiJC+KRlZBMmYWpSKrKAn5mx7FpetHJPu7/+qbWFAdL/ppatNF015NrbnS6irPXXWtysBMo7DF/cTcdxszw/vH3ix1BlUDL7O4KpPLQWxq8BozuFJW2NENVl1js9q7pNnsnXA4OhT0UqPrug2nqwtOl0VZi7mY6fUhWgN0CbyqSIjqxZLM3XOZ7+d6P3VmV8sZCL8qy6sp+o+Nn/lrZgQGcwQGFfBm1p1HZt1ZI3tL4L2AWVuvYM7WS5hVdxqz6o4LiKoBZRp4vxHozWw8iczGdmQ0njayuoRdE3jjK0YgoWK0DCzjQB/qcpduScYTHxf+/+ydB3QUV9K2RwFjbK+zN3z/hm+D12sMSigRRBIiKYeZUZZAiGSSJIJQGo0SOIEJygFw3G/zencd1zaYHCVQJBhsHHDGgFCY9P6n7u070xIiGhBCrXOK293T06GmOf109VtVyHgpGXG6AATNGoP6D7fBiO/wvfkTJGeqoc6YAE3huG7AS0DAI7z9BXiFtpMleDHoJamBTVvrnzGE+4SSv3K9Ebbcm0XR43InQ506AbPSIzE/czo2172OM/gC3+JjvPR+CcLTxiFaP55HeHsCXqkZRc8PEZI84QrAt28Drzu0JMnJdUW4zg3RRSMRvNyVV7tYOgL7vnodX6ARn1maMCNbg8T0CMzIiYImdSoi0qYgWR+Jv26txlmcRNOZbSh8JQXqzNGsBB8lw4la0wS9XEdtA17SU8vfaIjr/mpHBXhv7u1TgCQfbU0rGICSqIHKgFk6YbS0MzOAkto6rPMEtkyLayEJRCcrM0YNiGkdahpBxsUS0ra7QK3FJlmQ1uoKuXIAFutemMjGw8UK8N7cK0fZ2+3ggVsAeEXEVioZZtXwcu0tbxZBTSMaEVnRgMjKQ1JiGulxCXiPIbH0SI/Ay6LDZR9LwLsPUTU7wcCXEttknddEFLfrKBpV8NEW4eVa4r5apaF7hJe6p4UVerKKAFSPVKOnEk+eCC0Yjinpbghd7oPguT5o/nQPTDiNM+bPMEcXBXWWH4JzvKW2wtTOlbfQFe1aRYTTBr7yigNyIBMwIS3rFim9+PcFfPRipzVZeTBW1YA11iCZAdXN5ZUoqIoFwdHU5Y8jONMJkfmjEJrhjfDloxGTORWzMqKxMHsGA96z+AKfmBpR9p8VCF40nCUQXlzS0BWsrxa0xPp9GnilOry82503a+ARnO6J+Dw/qJeOQcPZ93AGx/ENjiJkzhhELZ6CyPQpiNMFIlbvD//5I7HqpVycxxf4sO0AKt4sQtBiqigyktWappq+dF3LgZfkJRTh5QmEtjcawp9XOyrAe3NvoxcAL0VerdFXi4ShvGID1ckVRq1+uVE8mGt9SZJAUgie9Ea90qjMGS91JiLHtI7NSDXc1S539rbjFfuVhYxlmuPLbUf5XPGA4gHglgbeqMoWK5gS7GoqOfBqGPRy4I0rO86gl8kZKvdZKzEwDS/BMwPe44iu3oPomu2IrtrHI8QiaY1FjSly3MCB2jo2IbKiybp/nowmEucacbsAb2ghVRjwZB2nWNKafjimZrjAn9oGF3hCW+CL0AWjUf/RNnTgawa8CwoSEambjIAcLwV49aImrqcskUwAL+l6ebtbqnVMkcjYFWMQnuWN8EwfaJb6IWGxGrmr09H4xT6m4W38fjtya+ZBvXwUonJ9WDtb3kJYQBaBfvf6v/IHiKub7uvAS4l79JCmyR2NsKwRCM8chVidH8IX+2D/l2/iFBrwkWE/pmUGYU6BlkXO1enjoc4m2cNYZK9biHZ8hc8szUy+E5g2AhE5o1h5M+3K4ez6lgMv/YZkrJWxJC+5WsiVr68A782/Ddsg0qatFUfBgZTglTeG4M0hKHpLwEsRXIJiqTmbVQ7BZRECcnsaxXdsIwdfsd/Lj91BV8xf/pvKGooHFA9wD/Q68ApNrIigCs0uh10C3hYmPSDYVVcR9B6CuuoQtNUH2fLoci5ZiKaENBnMsmQziuKWnkRU2YeIqdyJmOrNiK3YjdjyOsSVNSC2rAkx5Q2SNSGqXG7NiCqn0mUUaZZKmFlLnFE7Yg6/8iQ463q3cGtheYSXbrxUb5RqjVKDiJiC0QzaqIVw8FPuCHmast+Hw3+eF+pPbmUR3lbL53hSFwtN9kQE60eA2g/borsUEaNknmHW7H9rhFbSDYubvZAC2F4V99UIrxtLKuOSBl45gXdF4xHeoOwhCMkdgugV7gjJGorwbA9oc0YiLn8iopZPRvgsf5T/eR0+6TiMUziMf9VtRHJRIKJzRkOr85aAl3zTLRJ+BXIF4etLjX0deKk9cHC2B9R5PgjPHonoPF8WpY1YMhbbP/4HTuMYvkYLVr+ShcTlwZiYNAzT9MGIy5+KsMXjUVSZhfP4Gt/jU/xldzUCUkayZEzSSNP/A349i4cNV0RQo5ZcF2ioDbQCvH3/PioIVDoTPisitVS0zNYYgutnSWogj9r+sOm+70DlDBQP9B0P3PLAq61sgbqyGerKJga8BLvc6qAhDW7Fx9BWHIe2+gC0Nfus+lwOvC2IKv0UMaUnEFu5A3FV7yOhfCcSS2uRWNKAhJImxJbZgDemnABYQK8ceKXSZrch8BIMUUa6JscbVJKJEsyCC9zg/7QzJuU/gfCikazmbtNnO2DBabRZTmFOdjTCM3wRqPNmbYj7L/AShAoY4lFX0QKYjfnu0BS6IzDjMWgLKLrrjSj9CGgyRiBy+VjEZQRgfsEs7DiyBd/iE1B0N/+l+awmbFgGaX558wd6OBASkUvB67V81qeBN88dEQXeCNYNg7qQZAjuiFvhh4gMH2jTfVH6r3ym4T1lbsK+j95B8Z8LMT07Akm5YVheOQev7qjAgY+34xy+xpeWE/jHvhcRuHAUApd5QVPog8mZvL0zXd/0O7MEOWrUIgEvg176bX7Aw4cS4e3lm6UAXhpl8gMSNVAM1lpegWWdSSvIVxSBVrEdvpku/4qPehq7rKjMKB5QPHBDPdCngDeiugER1Yckq4O6mkD4Y6grP4J6Qx3UG/ZBU90ITRVFhUmny4E3tuQE4ip2ILHifUwv24mkkv2Ysf4QkoobpEiviPY2IaasmVlU+WGQWcuaSfV8u9cF7usRXuqyRl3RtFnDoc7iZa5CitwwacXjzEIKPTB1/jDUnXgPRnyDVtNnWKCLgzZrIov+huRKUeJuGl4BUiKiLCpDCDi4PSK8vD4udZlj2fyk26VksjxJX5tHsPs4wnOHIqbAAzF6d0RmeiE2cwxilk3AnIIovFn7L3yJz/GJuQV/3F6MGUVTWd3eiKxhiC6gkmQklRByCVsy3PWK+IrfSfwuAqyZJpn2ewt3WiOfU0m8kCIXhBa6wT9rKKIKRiGuwBcxy30xIycYb+5/BcfPHGSyheNn6rHv483Y8+n7aPh+F06aDjPY7cBpfNp5BH/cVomwxeMQnj0a6oLRCC3wQpCe19ulFtwiukvAq811hTaPovsK8N7QO9QN3bgk4BUwK5MqCDhluxczPY0EvHJ5LRME00JKKpMbJ+Pum7ihp6dsXPGA4oEuHuh14JUnipGsQbTupZHmKUmNZAyRlXWIqqhDVGUtoir3Q1O9F5qqWiuQUoSXgDe8phHqqiOgyHBseQsS13/IwHZG6QeYUfYuZpRuw8zivZizrhaz1tUhqfQgEssOIr68HvHlpM0luUIz+661aYVM1iAHXi7DILDuO40nBIAKwInIHcY6gpGkISLbC9rCEQgudIVf3m8R8owze6UbkuqJllM7mKThnOkU5uXGISpnMsJzR7LyTbydsAtL5qHtd5E0SGXQugOv2P8F4GYFPA56VkkEdYOjz6zRtFshaY1HeHlJNipf5W5NVuPH6YrgrMeh0TtDk+0EbaYropZ5ISFzHOYVhGHD68/iND7Dp5bDeKvpT5i1MpQ1P4jJHwUt1UbW0fb5NnkiHAde2s8FfrP65eoArK8DL5XECy10QaDeiXX5C8sZBq1uOLTLfRC91Bfpq5Pwp/fLUPvJZnzS1sSkC19YjuDD8wex+/j7eHfP6zhj+YoBb8lrTyEoZSSiCybAP9OVwS5VMaEoLKvFS9pdZlzSoABvl3tJH5yRgylNd5UnWMW63SlVPk9fY8BL3SREBpyRN6HoAry8qoItaY3v61qdJnTI1/p95XuKB/qjB3oVeAkUNRuaEEFWWQ9txUEkVtYjsbwOCWW1SKw6gJjK3Zi2aT+i1m/GjLKdWFCzD9PWb0F02ftIrNmJ5OIdmFm8GzNq9iGyai+CKg9Cs/EEoitOInZNM+au2YPFJdswf93bmL3+LTxZsQ2zindgQfE+zHl+N+aW70dSyV4kFu9FclUDtKt3IaakDtNrjiC6jEqbCa0uB3Axz2r8strAfQN4baAoByLeqUpT6MFALVTPI1oUNYtY8QTC9Y8jNtcb/jOd0fjpdlAk7LTlFGbnxiM8fTwr3xSS4YLoQk+WyBOW44xwnYsVxhgIWyO/PNudRUKl2qY2aONgx46xTwGv1KUuz0sCcYJeV4QVSDVzC56AOv8JaPVDEZM7DDEZ7ohPH4m5en8Uv7oM3+AgvkcLdn76NyxeFw3N8uHQZHpz7W6eB6uXTEAaQiDdw8jBV/57Xv10XwZeul5If04PU9QtMP7pMQjJcOPl35a5IzHfF+GpXphdGIjFq2Ox8KloLHk2EQuLorHk6WlYpE9A7nPL0YEz+LSzBSteWorwZSMQXTia/Z6BOUNYQxUOvLb2wpREyOv/ktTh6n0u/44iaejt2y6PvF72KOSQK19ZvlxMs+oJYrvyUf5FZVrxgOKBm+2BXgfeiKomJk0g4I0sP4hp5YeY5GDa+t1ILt+NxPIdmFm9AzPXvYs5q95G6pr3kbL+PcwpeRfJa99CRvV2LFy/GfFr3kNs5QHEvvoRAkubEFvyEWava8bcwteQseZfyCp5C4tL3sHc9f/FjNVvI2XdVqSV7sa0597Dk9UHMLvqAObUNGC6dAzUwCK6jDe3oMQ6VvWh0gbANuClJha3foRXfpO1TbuBdaqiigx5HohYMQrhRVR5wQWagiHQ5jyByCVuiE7xQdNnu3CeXv12nMDS5+cjKmsigpcOQ2zBSIQsfwIROhdESjVRCQgIdqlYP7+h28o7yYGXpm3QK4FDXwJeqSwWdZHjPnUDkzcUPIGw/MHMogrcoM5xwYzCsYhNH4lp6RPwf/9dja8N9TiPY9hx/C9IWRWB4Pmu0Cz1Qqx+NOIKx0CT54UQHT0kuIM1Bylw46MEvgKCbb/ltYFXXwfe6KLhTFYQmu3CxrAsF8QUDkd0vheClzkhdKkLYnU+SMgZj/BF3kjKnIS4Jb4Imz0CT2Zr8fauv6Mdp7H7xDuYs0LNaviGZLkiNNcZIVIHQbqGyc9c5kGjlKgoLf8hv4ECvDf7lqfsT/GA4oH+6oFeBV6SBFBlhBiSH1ATifJ6JJOVHMCs9XuxsGwfUir3Iz7vNQTMLMXjPgsx8JGJUN0zAnf/Jgi/80lG0MyVyCx/F0+WbsP0yjoErNmHmA3HEbWqDuNn1eDOn/tg4CPOUN3nAtVDXlD9ehIcHwuGwy+nQPUTX/xq6jIE619D4vodLMobV3yAHUdU6SHE1bRYQfd2BV4tlcvSuyAkhyKKwxGs80BA5lBo81wQn++ByCUeUD85Cgc+3MwiYSdbjyFZF4ugBaMQkzcOsQU+UOuGgVq60qjJ87DqGoW84YLKDVLEkkcoBfT2QeAluUEXqQWP8IbmO4E0nwT9Wr0XwjO8MPVJd0zLCsbG19fi87aj+KbjU2w+8B/o1j+J0HnDkZg5ETPz/RGZ4YOgVDeEZ3mCgbSeV3sQYCp/YPghoCW+K7Yr5vuShpceltSk4c35PYukRxY5IUI/BBG5TyBuhQei890RXzAc6uWuiCV9esowxC8bjScLApGwZAJy187GsW9q8S1O4i/bK1nt3pi8MSB4pt8vWD9E6iAovYGgBxxWhs6NvdGg/zdWv11jpFcB3v5661XOW/GA4oGb7YFeB14qDRZfQZ3SmhhoJlXUY0bpAcwp2Y+UigOYPG8jBvxWDZWDB1QDvKG6cwRUg0ZAdacHVPd64M6fecAvPgvJz7yN+ZuOIHTNQcTXHEf0s7vhHf00VPY/h8ruEdjf5wrVA8Oh+uk4qH4+CaqHRkJ1rxd+OiEFoYX/QXLFAcQX74V27R7WrS2yvJ7V5hU63Z6AV+iM+2qElyJVpEOk6Gx4jhe0BWNAdU3DczwRne+B6Ex3JKSPRtwiX+w/ugWnTV/ga/NnWPzsXCRk+2NaoR+mpAxGbNEIaHLdEZrpDG2+JwMGbZE3i1AK2O2i672NgLe7rlgAKQMhvTcSVk5EfMEUxGYFouy153Do8/04j1bUHTuE9IKlCE0ej4SlU5GUEYC45RMRmzEBYYu9WaSRdNUCeml7XR8Quj0oXCNw9XXgJcDVFg5FJOl4Mx5lmumYlV78ms5yQWLhGBY1n5E/CdN1kxC7bALU830wLX0qtrb8Hd/jYzR+vR0rXkyDJsMH6hxvUCInq58stQ9mfpfePBDwslq8UhIbf0NxbdF1+k0V4L3Ztzxlf4oHFA/0Vw/0OvBGl0gVEkrrEUMSgvI6xJYdQFLZASyoPoifjEmD6kE/DPytGiPD8xE5vwraOaVwGjkb9ve5QeXwUzj+jxvUy17FzJIGJL1wCur1zUguO4hR056B6s5f4tdDxiJ+3hrEpm5CSMpLmJLyAgJSqhG4bBPC8v6BWZV7kVxdh+nU0KKkFrHVzYiuaoGm7BCXK0haXZI2UP1dbs0sKs2i1H1U0kA3b43OFeocN4RleiB4uRcmpzkhaJkrYvUjoV3mDvUCL0xLm4J9R7bgHDW+PXcUM3NiETDXG1FZozDjWT8OunoPBr2UBBeY5cSg4VIRXltkrBu49SVJgwwy6VyF0bmp872hKRiFwCWeCErzwfxVSfjIeATf4Vsc++4j/HPzWyh9uQyvvF6Jv2+rRvW/n0bZa4WoefspPPe3ZZj1TCBvQyxBL6vWwF6r2/Sj9MBi8+O1QVffBl53RBYMZ5UkInI9QB3XqJNdTNE4+C92w9KaRLx14hWUv7MSC1YlIGyBH6ZlavDvfX/BJ4YP8TU+pl8DL767CtHUjCJrJEIy+e8YnOsE9VMUXefJiPRgI5c0UHSXR3h/2G+gAG9/vfUq5614QPHAzfZArwMvNX5gkdIyrpmNqjjEqjFQ57RZGw7hx37L8YR6BaIL/o3Uyp2Yv/59pK59B4lLXoD3xDmwv/fnUA34CUbHPYuE5/ZiWtVJJFR9iOSy/RgStgwqx0fwO9cJyHnudaSv3c6S1WaX7MHCyj1YWLMHsyp2Y1rJLsSs24EZG5uYjlhdyptaxGw8yjqtEdTyZLWegLfvJq0R8Gpz3RBT4M2aTqhzRrEuU7n/l4ztX72Mz7ETn2EvPjceQiu+xLemz/AtPsOXOIovcAifYif+82EJgpcPZXKGyAIvlkREETJKJmImNaLoHuG1gVrfBt6IfA8GnXLYpWUceEciMnccnlwTiVV/z8XX+ATf4BS+xTf40vg1g196nf6FqRnf4UMGX1+hCc0dW6F7cSaCl3pAoxvJkrDksCUSpq5H44M+Dbx6KqnnzdpiR+hGIHbFeGj1Y6DWjYF/mheeeW05PmbXaQs+QjO+wqf4GqfwiekEPu78GCeNH2LjO6swqygQ/gtdWZWSUB0vsxdW5A7qQijeUBBQczmDu5Sw5sxGJcJ7s29Zyv4UDygeUDxwbR7ofeCtakJcJTV/OMQssroBmuqDUFfWIrbmEMJWfQDNug8QV70bsdXboS19Fwte2oOsmr2IT12HAff+BCr7e/HbkbMwv/gQYotbkLzpBOJWb4aTeilUd/wEg4cHYdWmvViybjdmrd+P2NW7MKNsD5JKd2JG6Q7MKNuFuNLdmLGpmZVBCymtRVhZPSJrDvNubhLw2qK7fa+1sA0wbZFAuoGHZTshIpvf7IOWDcPUNFekVUXj3ZMb8BG24oRpBz4z1bEs9s+Nx3CivR4fGevQcO5t1J1/jQGvWucBDgS8lBjBXni+FwKzXXknNurGpueVIIRG1CYF6NvAaz0fa7RXnA+P/EXmjWAa3hV/XoCPsA9HO/bgUzThO3yCz8xH8YWlBUfP7MEpC2+B+5FpLw6d+y+K/jQfEZkjLgBeW5KfrOGFdd+237an37unZX0deMOyPRC3ciwD36lLnDB1iQuiC8YjImc0IvV+eO7fmXj35N9wHHU4YWnAh8ZGHDl/CP+u/RvmFMYhKsMXwYtdWSkzeqigiC6BrvopL/jrhnLg1fPrm95eMOjNc0VEgQszBXiv7cajfEvxgOIBxQM32wO9DrykjWXthUtJznAI0dUNUFccRGjZAURUHMDcv36M2BfqEbZpH9Sv7kdI5QeIrtiMBaVbkbC0DKoBP4Lj3Q/h8TGzMX/NXsSs2Y+ZNc1ILt6KYdplUA34MX7jOgErK7di4XPvI7W6DnOqazF/Qx3i12xGcvF2zN9Uh/iS3Ygq2QdNeR2iNrZAXd2CsLIGDrysw5qQMthGruHt2xHeqAIPhGe7Qqsfjrii8QjP8kZYphei9aMRnDYMU2a5InzhGMQuCULcshBELZ2K8JQxCFvshbg8H8QUUktXXsuXmhQQ7E5ZNgSRK0fxLmwS7F4ceAnSOBwyIOtjkgaRtEZRXW70SpwS1pwRpnOCNm8YYotGQZ3ticjsEQhN84T/PPLrMATO8Ubs0imITPVDxKKxCJjthcilYxGWNgIhy7yh0fmwls+k4xWSBg6tXaG6J5C90mV9GngJ9HVuLFGS6u/GrBjFGk+EUse1leOg0Y9CRJYPQpePhH+KN6uxG6cLQMiiMQheOBbaZX6I049DTP4IRBeNZJH6YKrOUOAKqr9LTSeosQpFfemBTgHem317UvaneEDxgOKB6+eBXgbeesRVHWK63cTSOkwrpeYPvPQXVUhIfOEYQtbtQdQLjQjccABBL+6D5uV9iCrbikUlOxGe/AwGDHoYA+55GCODFyO96gCml+xi3dQSn3sdw9QpTNLwG+exmKfbgCTdK0go+iu0+X/CzOdex4LizZhXth1J67YgufoASEZBwB1ecQhhFY2I3HiUNbCIloCX2hDz5hQS9FY2sSoOfTVpjUERJawxc2fJapS8Rg0oyEjTGJU9FpHLJyAyfQqilk9FZMYkaDPGsq5smlxvhGW5gWBDRHipHipFPQlwCRbCWPcx6kDGzRoRJbC1Rib7LvBaz4edC52HK8Lz6XU3t9CswQjPcUJMgSfCMp2hyfRAYuFYxOWORmzOOEQvH8sS1WKyfBGdNRaRWaOZ35mUQT8C6lzqfmdrOCEAVYw2H159dJe+e8F2rMlZXLN6K3daowclrqXlVTHoQcNm1CxCqhpCbyByPBGe6cGi7dRVMCrbBzG5PgjPdEN4jjOohjRV1aBktaA8Mg68dN3Sb2yTlHBJA/3GvK207Nq1Xs9X/lsoGt7rdzNTtqR4QPGA4oFLeaDXgTeq6iBiKmqRIAFvQmkDYimBrbwJcdVHWPIYtRMO3FCL8JcOIrwO9r8LAAAgAElEQVRqJxLKtuPJp97FiCmLmWRB9aNfIHppNRaW7kH02i2Y9cIBxK9+A+6R6VDd8QuoBvwMqh8NgWrAo1AN+h1UPx2GH7trMDa+AHEFf8f8ql2YXrIdUcU7EVNTzyK8mpoWRFQ1S8DbwjuwdQPemKq+DrxUtJ93kWIaVIpi6SjDnZs6ZwQidWOhyRoHTZYvtNkToMkZD9L6qnVerFYsARGL7Oo92TwBLxmBgojqCtgV8HAhJMqgoY9FeLsC54XAG6EbCnWuE2vOQWBFLYOpdrFWRw0mhkObSQ8VPqwqgzZnJDRUJYAs1xsReTLYFQ8OsgoXBKtd93/187cV8OZdCL6U2EdVFeihjqqRUPk8jY66sXlCm+vOlwlQzndl0V1W91iqdyyuVRvw0m8s/c6sDq/s2lWA91L3GuUzxQOKBxQP9KoHehV4o6rquV63uhYxFXUsuhtX1oDYkkZElzaBIqtxG44iYN1ehFfVMmlD1LotWP5yE2Zm/gOqQd5Q3fsE7ncOQObLjYhYuQUJmxpZx7Vp5bswdn4JVD8fiXse9cXAX4+H6lejofqlO1Q/GQLVoN/jjv/xwW995yLp2TdZy+Gk6v2I39AITXUjCHjVlc2IkloUx5c1I66USqdJ0d2KesRUNfTxCK/85i27cYsoX643ey2s1lGt3THQ5I6FJnc0izoSAFDNXetrXqr4QPPSq/1wkkoUeFwYQZSgQCQAcXi4cN8CNG7t1sICMIUfZRUU8lxZUpMmj3SfbqyaAIuC53gjUj8SEdnDQQ8U7MEh15tJSsin/NU5b6MsouXyB4aQfE8IYw8Q1wBZckju28BLTSC4seuFfNHlzYF718oZ0rpUQo+M2gRr9PQ7SdHaAq7fZclqBR4IpWv4otcrVW+QXbfX+DsoEd5evf8pO1c8oHigH3mgV4FXW1WP8A0HmWmq6xFdJTWhKG9BdEULoqsOQ1PWwHS1cVUHkVixB3HPvo3ZK9+A99QMqOyGYOBjgQjNegWzq5oQX3MM/iW1iHn1KELW7kDC+h3QZP8Ryyo+QHr1TqRWvIvpz2xCwPxc/NotACqH30F1jzvGJq/BzOIteHJTHaZtbERoyX6oK5ugqWrpArwJJTbgJd0xAS9Be1+WNNjqxgpokyBOgl6KMkZQ4pWej3za06pXFcAhxv4HvMJvfCS4tZn0EEAZ/qTDzaW6uiMRnjuSVcNgGlMp+797ww4CLfKlANLu0BuW582i6ALIrnUU27d+XzzsSK/xb21Jg+h+RrIDLvsQD0oCfJnGWoJR6vzH/Uy/Ufd6urbW19TFrnsnO3F9iwc0a7myawRd4W8FePvR3VY5VcUDigd61QO9DLyNCNtQj5CN9Qjf2ISIDS3QUGWEasmqmhFaUouEmnoGr1RKbMHq/2JYwBKoBrnA8Ze+8J7+LKaX7sbMTZ8h4PlmxPz5K0woqWPbSX7xOBLX7UHqpmbM39CA2ZW78GT1m1hS+RpmpZfgx7/1g2qgE37hMxMJK/+N5PJdmL6xHuqyWmiqmqGpaGbgHVfegsTSZtyewNu1oYGANXFDZlHEfC5RsEIXtRCWynHZQEBEO6XxIhFesb4YBUCI/XUBFsqaz/WwmoAYvi4HTJEgxtoY57ogOMcJs0snIWnteKbJvGD7PxBQrMdp3U5PwGuLPLKorc4LoTpP9uAQoR+F0NwRCM/14Q8SbDtdt8GPuZs/JRAlH7DfRMxbj6Pb+le4vO8Db7cIr3Te/Lx4DV3xUBeWT5UtyNd8pLJuop4uk/YIuYgkHyFJDv3edK1qpAcZ8ftf4Lcr9Lf4vhgV4O3V+5+yc8UDigf6kQd6FXgpMqquaWSwG7KhGcE1zQirOYLw6iNMPxte3gBtRS3iy/ZgbslOBruT41fA8acjobrvCTiFzEPy2neQsH4P4sqOI6zkBMI3fYKAqsOIqDmKqOojiCluQHxJPSLXH0Lk+j1ILHsHqZVvIK/4PxjiGQXVIGcM/K0/YvV/ZZUdplXXsn1qLwK8iWVc0nDbRHilpBx+Y+8anaRldGMnaQJFvIKlmrq07HLAK4BAjOIGL0BXjBcAqQA5abz1gZdAkyCKA6c4L9voCW2+twS8wxGePxwhuZSwNwKhei/uX/b97tDbDWAlfxBAE/SKUez3Wsfuv4944KDts33oKErtZZUG0IMFnS9VMyBYi3/OBwuqAxGic7bqwWkdAki5X671+C73PeFn23rcjwJywwuGSc0jeNtnflzi2Fyt0Xjr92UPd9zPXYGX9sd8lselJdbvKcDbj26byqkqHlA80Bc90KvAy9r2VnCdLCWIhVS1IKzqMNRVR5icgDqdkZSBKi/MWvE6fEIy4Xi/JxwedMaY6PmY+UwNllRvRvK6bYhfU4/pVZ/Bf00tYl86ziQJESWHEF9xGAkVxxBZeowlwSVUbMPs4n8jr/h1OA+PhcrhMQz838lIyPsL5pRtY7KJqMpaaMtJYtGC2PIWxJfZIrwCeFmr4eo+Lmlg8ESvgqnsFdc1ikgWzdPNnMCBoIF0jcH5rgxyCHbFK3gOHN1hjesbBXSwbUhQJyLIYrw9gNemHeX+4BFB8iUlTEWSllnvCgaC+W6sAkBY0TA2km+Z3rkbMIntdB+Z9lT6reizHwpcfR94+UOauI5s1xwHUyFPYCNpdNlDG13HHNyFf+maZprpfP5QYgN/22/Jr1kFePvijU45ZsUDigcUD/Qq8LLuZWVNrCKDuvIwQisJeI9CW3MMUTWHWfWGaaU7MPP5tzFWmwuH+4fhrvtdMMY/GSnPbsCi9X/C/OK3sLByH2aVtyCutBkRJGeoqkPihkZo1+7D9LImzKg8hog1DUioPoKkmj2YV/Yuste9iceG8Qjvw0M1SMz/K2aVbENc6U7W8EJdUY/Iaq4ljitvvkDScPsBL0X0bK9ubTDFgZdBbx6v6NAT8BLMCSP4sIEHnxZAIkBXjHy5DNykCKYAjotHeOk7vMpEeK4zKDmMMvHlkgaaJ+jkEC/bR54E95S49EOhsdvxCoBiiVHU+lfngqhC6jznwo+XKgHonRGxkoDXmcGuiJYLeKV52k6XhEAp0YptVyRdiYStbrAstnM1ozhuEdkV44UaXnrNf+tEeMlH9FDBO93xkXX109u6pAmoF6P4nL4jP29+zfEHQJrmpfYk4JWuFVqfPhPyHvadH+B/JqWgSilMuuOFsFwy/vvT70fHLKQVVMHj6VdT0IbP0I5WmMyAGRZmdCuzSPczZeSOuJwfutz+aWW5yT6ULxbblH3cdya7n0ifPpm+43blSG8dD/Q68CZUNSKqooFVRFBvOI7wmhMIr6QWv7WYXrkL80rfhnvYAqjuexR3PPAofIOmY2lhFXJK/oaUtf/CrNWbkVxyAMkbWxBdfQjh5XsQVbkXCaV7MG3tHswrb0DSmn2YWd2EhLL9mF66FanVOxE6dx1+9L9ToRroipGRBZi+8j9Y9OIhhK/egsjKOsS9dIx1W4uqJB1vMytLZq3QUN4IFp2ubOzzSWv8Jm+rictu6D/gBn41kEXrcuAQdU4vHEXVAg4f3T+XEo90LogkeMlxQWg21/Amr5nASlBF57iDTEMyApn+NTbbE2RUnkrA3fUfxfnZxgv8Y63xeiOP4+LblgM0n+66Lmu6QJIGAjI9PdRwXWuIjsNld0kDyRz4wxFJCaRoqKx8mg0whU+67u/afgPaFm2Hj/Q707z12u5h+ur2I7ZvO1ax7avbju374ngj9E4gY5FnPbXm9kaY1GiEn4/0UEf/V3JG4KlXl6ANp2Awn4XFbGSwa4SFsZpJYjZl5Ox6OT9YeU8OgmYZ+Mr8aQRAJrZpheNev5fTAV+hWcyAMLOY7vUTUA5A8cBN80AvAy81muAd1iIrmqGpPAZ11TGWLBZbdgCzSj6Ad1QGBv7CA6p7fgUnr8mYnbYC8zLWY3ZmOWbkvIjkFf9BuO4fCCz6FzTFmxFTtQOJlTuh1r8G98inEZr2ImY9+yZSN+zA7LL/Ytrqf2Hyk2vwyBANVD/yxsBfBSJs8SbMfH4z5lTvx7QNhxjwUnvhqA1HYAPerh3WeJe12wB4byLcXgB7DHi7Q0DX+WsB3llllLQ2gXXhEsBLgEFRuaACrr2M0nkiOsfTCsICYPrVaH3gkMp0ScAoII580QV4qU6zDHgpspr4rA8WVQWyxg0iWtkT8Ar4FdeAAN9+5e/u4M0aZ9iANziPgHcEM95djze5oMRM8ltEziiseHUpWvElDJbvYTF3wmLhEV45synTXYO1l/MHu9v2sBItIsAVsCuA94L1b9rt+mI7IuClP/l4EQhWgFfylTL0Rw/0KvDGlTdieskhJBU3IKGkGbHFLYgtPczkA0nrD2Du81vwiJMWqruGYOD9Q/GbP4yHx4hI/OL3k/DLwSH4pUsEHnGeiruGTMLDYxIwdlEJktb+F/OKt8I3YRVUjs649ze++KV3MP53Qhh+4RuCn48Jw12/84HqHjc8/OtATJy2CguLP2A64Li12zDzZYoUNyC45ACiNx7tArwiwsukGBU8ytvXy5IJAOmtUYDPxUb+yppeW8tfP/PoIB0zRRRJIqAljXCuM4J0Q5FcPhEJ68cjRG+TNBBwBRW6Y8oKbqLsVBeNp1SOqt8tKyDZiS0ay6alZEYBvOJ3ENpt6kxGdWynP+OD1MoA1q2M5gmCSdpCvwvJWlgThwI3lvTY7/x6meuJyX6oWUaeE0sIDcrzQrDem0V52YMAXdP5zgjNd+Jl0nSjUPB/S3EGX6IdZ2E2GwGzhVPZRfjmSoN/fXa9HkDVGn293GcSzHay0QyKl5us1hV2rZFdogTaLvlbbL83yUEcgxilYxGzYrQeolggRusHyoTigdvfA70PvMUNmFHSiOnFzUhY34TE4mbMKG3CrPW1mLNqKx50ioPq4ZFQDXKCyuH3UA34A1T2j0F1hysf73oUqrt/C9UvRmNoXCGmr3kfi6tqMSP3NQweNx8PPj4Fqh8/AdW9P4Pq/p9B9dDvcMej4+A+cQHUs0qxtGQ7FlXtxYySnYhev4OVQNNUHkLMxqOShrcZsRXNrOGEArxddbDXA5IvBrpiuQCtnoHXDUEFLgjMd0YEJdflOiM4ZyhmlU1E4vrxLClMaIUJtgTw+he5I7iAAx4tp+l+Oxa4IbgHIBUa1csD7ygsrgxARLYzNDpX3swhj1dpEDpueeeynqC3v/qf/MMlDS5M0sAjvHJJAzUycWZAzB5CCHj/mI4z+JppeM30WpqAl1k3EBNApoxdAVXujwsiuHLotckXiA2tfwIUxWj9oJcmxHGIUToMMXupsZeOWNmt4oFe80CvAi9PWmtAAquE0Mw6rFE3s6TyRsworcOMtTsQvvxPCE3bCE1aNaKWVGPK9GfgP3MNwhdUInzxJkxdsgEjF5VhyJwSTH36bZasFrVmD5Ke343kZ7ciUv93jFtQDI95z8AzZTU8U9bBP/ePmLt+B+at34UFFXuRuO4DJFXuQ2JVHaIrD7Lav3EvfCjV4VWA93qA7cW2IcD2YuOlgJeAYWqRC6YUDkV4gRsidM4IzxqKJ0smInntOITmOsvKTvHkOQIuOhZqK0tG07SMgKJ/jjwKK+BURHqF1EAAL08K44mJlLRGZcgomjv9mVEswhuR7QSNzsUKvPQZT0jsnsDYLZLcb/1O1x61OXbhftPRWwwP1nmPuu9F6dwRlesKrZ4i6S5co5w9GiteyUArvkGHuQ0WlrVmASwmDnUUdiTCUcYr84OgQQHBwnfCfzRPn5FRiFesJ92u2eq9duuWHYQ4DzHKgs902BezW+L4e9t/yv77lQd6FXgp8SuyoonJBkgrS8lrMeUNiK88hGkVBzGtfB9mVe7H7LJdmLF2KxbXHMC80m1I31SLhWU7kLzmA8zdUI+5r34IbXUdIjfUI/7lo9CW1SFu/X7MLD3IEtfm1BxC8osNmP5KPaa9fAgJL9RiRlUtYp7fxppWxJfsRFz5fkSX1yJh01GoKxoZ7JKumKK7VKWBorvCFEnD9Yv0Xgx0xfLLAa9/oQumFvAIr5pes2cNxbziC4FXRHppu5ScRdpeggqavxiM94/l3UvKSf6Q9KZy4GWlvFi7aFuVBtLwplSRpMGJgRmBLlXMoJF8LqBX7n/x2/Z337NrUefKI+M6atPNgVeT48GuzWidK6KoAgl7kPCGOnsMnno5C634DgZTOwNei4W/hCcaIzWvMl65H9jDgaBBAbOMAqUHB5rmLrVlrNE8A0ouf+h1aKQD6G6yReL0aBQaZLGs14+9X6GWcrK3ggd6FXi1VY0Iq25CaE0TwmuaWRMKKikWWVmL6OqDiN/YgLD1u1iZsPjqQ0isOYioddsxvWIvA9XppXtZU4mkDUdZolnQ+u3QVOzEtE21mF6xH7GrdyD++b2YVX0UUaWHELJ2H0JL9yKibCcSavYgaeN+JG2sZduNLj+A0PV7EbeBN6zQlDUiqlyC3bJmUP1dDrz1oKYTStLa9QFFOfz0NH0p4CUgpdrApNXV5vEqDWHZQzG31CZpEJFLsW0BsSIx68IqBbaSZf3hMxHpFiNVsxBGPhLAS6WxqPFISKEHk4D457kiKN8VMat9ML8m0NqIwlpvWGo8IfwufCn2Qw8bNC2W99dRXN9UBYM1Isn1YuCrzXWDVh7hpcoN2WOw4pUsnMN36DC3gyQNhF1GGGCBkU31v1GA59WP7AYsgJYC5bIENYJCBoT0D63ThRJpXwbJ3xIB99bdnI6PHWjXAxCL5aM4J/myrt9S5hQP3N4e6HXgDa1uRlA1gW8DNBsaWA3diIr9LGIb92IzwivqEFFZz1r9UhJZ3MZmxFTUIbp0PxIr65FQeQSadYdYebDojfWIqt6H2Kp9rARZUulBJKw9iGllhxFZ1oKo6mPQElxXHEB0zX6oy7YjqnI/oqvq2HYjKxoQXtoAiuxGlR9GXMVhUFvheBnwxjHYVYBXgOMPHQUQXWwUQEBjj+voOOgS8FLN2yCdE2ZUTER88XgE5LuwpCmCXvouQRWNXL5A1Rr4fH+FLTpvAaBiFLBL44XA68GAl7TQBLyBBa6IXu2DuRsCEah3tjYmETWbeXSX+1j4v/s++7PvySf0EBGU74HAfC9mLHEtj9c6ptbHTMNLdXrzvBGWMw4Ff8zC9/gO59EGA4wS5ppYdJdivRTl7V+jTWsrgO5KR8aJMuCl71ECmzCxHQaUsvUoVmpCB4NeHlW/NSHhVj62W9NjylHd7h7oVeBlrYWrW6CubgZFe6niQWzlQcRW1rGRmjuwFr9VLdCw7mtUJoyaQTSDwJPBZ3kLoiqOQFvZwtal7ZBUgskQyppZl7S4Mvr8COvgFl7TwqLJmupGaKvqoa0+iKiqg2z/tC/aPm0vuuII67JGwGuTNEj7vIoILwPmyj2YXvN/mP3SMwhaEYKQopEIzeVJVkIbSeMPhce++H266V/K5MBL0+E6auZAHcyGQaMbhjidB6Kz3BCUMZRFHEdnPQZN2ThElo1DwEo3BOe7IIKqNehcEZnjCrWONyTwLxgGMgIOUU6rP470kMCN+1X4VpSDC8524/VsCzwQpHdjFlrkibAVFOl1Q/y6cZizwR+hRe7M/wS+QXn8QYMePqhaAzdZIwg9+VwYb2bSH31P196UAjf4FbnDt8gTfkWemEiVRArcEJjHkzGDC53Zg5t/gRfCV/gh+9Vl+AZf41ucxveGsxA1eOlGRbhLf/xfwGQx28qWifJlt93Ic/YsUu5eTyNJneXLad5gMKG9zQhLO4/ekgy602jAuc7zOG/pRCfMONvRhnPtBrSeN6D1XKfkWPKyAQZq/IEOSUZy8zCBytB1Men35o864jMTSOrCRAxM323iNZtNBhg7DTAbTaCuJaQBJ6M3BbRNdu1YLDAajTCZaBsW62fi85t3psqeFA9cfw/0OvASZJIRpJJUgOrycpOiqBUtDFapRi8Zh1GCUF4XlzS20RUtDFQJegWsclCl9VqYDpdAVlNFcM2NAzRBsgS+bCQd8RHrNqitMNfwNloB2wraV1iWLLqkEbHlu6zAG1gUzICX4FRLbWVldXBtECBgYBgDY4Lj29UEwF5slAOvfB0BvLGZHkyPS/rSiNUjEb7OBzP/GILYsvEYn/4bVtZJo3dmCUCUBERd1yjiG1DEjbdLpiz5/mlU45UbB0/S3jL9LUXOdcMQle+FyDxPROhcEJbtxHS51DmOoo/+GY9DU+iOWaW+rDoGrxfrCnXBMLZcbJv7loMv+VtuHIb7p+/JD1MLh2HSimGYuMIDk4o82DxFzulBjcqRBeU+wTryBVJypX4Mlm+aj69wEt/jKxhwHkazoSsAycBFgEwXQOoOTH1+nsMsnfblTNw+qbiF0WhGZ7sBnefaAQPRsglmSwfaTGfRajnHIujfd7biTHs7zrYbcfa8ga3G/A0DzDgPC9okvYPY8o0fL/gtGdZSLJfH9gl0GexaDFaBhsnYDpiohJ0JxvYOmDsNMLS1M4A3GYwMcFnFD+nwxXVDs2J/N/7MlD0oHrjxHuhV4CXIpWgt72RGGlkCXptRZJWgk6KtURUEu8dkkVeeSMYAtJxgmYw0txxySYYgNLdxFQcZTIuauSxBjuCYRYUJeMk4eAtgZrArAS/fNo/uCuAl4OY63npQpFqcBxsrDyK66gCz+IrDSKzeh5kv/BVzXn4W/gWBCKB6mzlS5LHQC5pCL6gLPK1F/eUQfLtP25KZSHZwoVEClDD557SMZAz0Kj66aDiCizzgmzMY47MfR9TqkUhaNQrReidosgcjImcwwnWDEcZsCEJyhyA41wnB+iHMaL7fWt5QhOq5hecOZclnmmwnaLOcEJXpBE36E4jOcEJspjOzuCwnJOpcMS3XDfE6Jyx8fgx0G4PZdLzeGZGZT0CbNQTanKGIyCTfD0F4zhCE6SS/64cgkJkTG+k36Le+p+tO58QsmErq5TojTOfEfKbNHoyorD8gMd8ZcfkuIE1vTO5IrHx5PtpwHAacRKfxc3R0noHR2AmTycCMpsW8mL69Rw5sFJXsyShS2d1ovc7OTnS0n4ex7SzQeQ4Ww2kYTd/BYPkWbfgGZ0zf4LTxNL7taMX3JhNOdxpx3gy0GXjVXjDY7X3gFZFdLmThsEsRaMiA12zqYGDe1nqeQT69AjAZzGhrbYfBYGAmIrrdkeNSkd1LfdZ9O8q84oFbwQO9DrwcJqUuZiRTkCK3fLQBL0EvtxYGwdbIa5f1bd3Q5LArgJciyCSTkIM2waoAXt5VjQDbtg8BuzxRTYJetk+q4CA1n7gE8IoI77TqP2LmC09han4AA15qzUpRMtG5iqK7tKy73a6RXXFetlfq4tV615GijZdaJyD9caYfnVroiin5Toh6ygML1oxB+kofLFg+FPMynsCcrCeQpHsC0yVLyhmKZMmm5QxBgm4wEnT9dNQPRYJ+KKbrhkL4ZWa2M+ZkOmNuhjPmpg/F/EwXLMx0xbz0IZiV9hhmLPodklMexcy03yElxxn61WPZNPl6xpJHMXv545ib7YQ5Oc5IzhqCGdlDMD2b/MstPncI4iSj6f7qf7r2ZmYPBfk7OcuJ2cxMJ8zJGIL5GU9gwfLBmJn2W8xI/S2SljyOWeluKN44E+fbdwGmFqDjOND+OWD8HjCfBUxn+DTN03R/MCOd91ngYqOBfEImrUfzhu+BjtNA+7eA4Vug/RTQehLoOAngc8D8Kc60nsBZ41f4rvMMzlpMDHpbLcD5TilyaukAyFhG2827lYuIq3wk6OXzcuAlSYOZPQTJj/G70+dwvs0AUjW0tdNDgrmLhKH7mYj9dF+uzCse6Ise6HXgZfBJwChMivjaIqZcs2ubJ50tGY+qdo3ECg3wQVZPl2rqMsCVZA8MZFmpsa7RWdoWh10uj+BRZWm/7LgEKPMKDQLGaaTjFsdiPUZZhDequAFxFbtZhDft7yWYVpGEeS9osfhFNTJe1SD91SgseyUSS1/WYslLGqvRfH+w9JfUuJQtf1kDsp7WWfqyGsv+HIVlf4vGor9rsPDlQOg2BKCkOgSvrg3Gq4V+ePXpiXjxuQmoWDMBZWsnoHztBFQ9PxEbVk1E9eqJKFs7EcXrJ6B4Xf8c1xdPAlnp+knMF+VrJqLi+cnMqp6fjJrnp6Bq1SRUPjcRNc9Pwgvr/fFSSSBeLA3ECyVTUb3GD69WBqPyufF4uTwQNWsnsmXlz/uh7Hk/0PbIxyXkX8nWrZ+ItcXcaLq/+p/8Qtdi9erJVtuwajI2rpqIF5/l1+0ra6fglRJ/vFgeipriEPylejo+2V8JnHgd+Pg94NPdwJcHga/rga8OAV/U8XmaJqPPFOviAwv5iOzzA8DRHUDz+0Dze8DR94BPtqH1w/fw5dEtMBtO4Vznt2hFJ06bzTjHgFeqeUwSARNFUm9ulQYBoLaRyw5sDUh4XWb6nP6MZhMrMHHmfAcrS9ZmBk63mXDWyJPzOjp5ZJwivELKILZN8/JlfRFwlGNWPCD3QK8DrzzqStDJks8kuYFIUCOoFBFWAliSJlBUlkFq+THElh9hcga2rao6RFUfQFRVHUtIs64nEtEE8LLkOAmIBTzTZ5KMgUsTeCIdk0JUcYimqC6BLkkmSHJxOeBlEouK3UisehXJm1YySUNQwXAmaQjNGWpN3hERz/42Xip6S59dKsIbmuuCKdmD4Z8/BJOKnkBI3uOYnTkY5UXjsGNlMFryA/GhLgBHc/3RmO+P+gJ/NOTz6SO5AWjRB7D5g4X+6LdWFIBDZIWBqC8gf3BrzAsCWXNBCBryg9hy+vyAfgr25k7CPv0k1BZMRf3KIOzJmYBdWePZdg7mTeF+LgxA04ogtk36HtlByeoKAyCs3/q9kF+PzXkBILP6O49fl0dy/UHWpJ+MhsIA7MmbjMbno/BOmi92pgfjSG48mpfHoW5pPGqXJ+Lg8iQ21i6bxnQfJ9wAACAASURBVMa69On9YKRzvLzVZSRBmHz9+vQkHE1PxpG06TiyNAH1S2JxICMRWzKnY39FIXCqCTj7JToN53GGJA0WoK1T1C+jxC9jrwMvCzBTgV06FNGIxCxFfK0qXoBi0V+2duBbA7C75QS+NADfdAIEwh0dHUz20R1uCXzl0CsHB2Va8UBf9ECvAi8BKskNyDg4EvDyigoCdhlgVhxEfEUd4ipE9QaqrCC0s1JFBYJVKbJKpck0NQegqamDpppXeuDbk5LQ2H5pW3USSAv9L4dYnggnwaxUyUFUc6DjFMBL0HslwBtfuQdJG/7EqjRQ0lpw4QgGulQ9gCQN3e121+3Kz0+uy+1pmrS6PS2nZeEkCSlwQUiREyYW/QGaFUOwIOMPeCHTG2+F/Br/+oUKe36qQv1DKtQ/LBlNy6zpIRUU4z5ofFCFpgdsRvO196pQ+6AKB3+iQu3P7LD7Jyps/7EK+35lh6bH70bbpD/gwM9V2P//VNj3MxV2P6zCgYdVqH1IhUMPq9DwsOLfS11fdC2Snw6Szx7hI02zZY+osOcRFfb+XIV3f6zC+VAP7Pz1IGz72UDU/b/7se/+gah/cBAaHxqE5gfvQv0DA9F4/51oePBONk9j0wOD2PztOdK5Xd4aH7oLwsT6NN/y0I9w+KEH0XT/A6h/6H7seOAuvPWTe/DHXz6EXVEhwL7twGefAN+eRvvZDnSYgXbiXErmYqUfRGe7m3frF9FXFsGlltIEu6SyEDlqUvk0CvBKdRpAUowzFuDkOQOavzqDyIXLkJRZiC+MwDdnWtHezms6C8CV70M+ffPOUtmT4oEb4wEGvN+bgYmzijB+yQuIrW5GOCWL1RxFdFk9EkrrWNUEinhS9JUqJagrpaoKTDIgkyMIWcIVjnLgZXpYqn8rVVqwAS+H4osDr6S5ZXpaShbjEV7qvKapPigBr02yQPsR+2Ul0KQSYwS5XBfMRwGyLJrMSpfxyLIceLtGePlx8MhwvTVpjTS8JGmYsfHPLGmNgJcivBTJ1eS7SSWxbFUZSMvbn6K8l4vwyj8Py3FGWI5N50wRXqoEEErAm/97hOsfw5OL/hf/yBiB+sDHUPegCl/frULHABWMjtw6B6jQ6Wgzs6MKcOjfZrFXQW5mOxUsKhVoNA9wQLu9CucdVDh/hx3ODVLhu7tU+PY+e5z98Z3AWBd87qjCmUEqnBmgwjlHFQyD7NF5hwqGASpc3L/2/d7v5Bu6Lun6bBPmyKfb7yB/q3CWxgcccYyuU+1EfHKfAz50UOGbgY445+AIwwBHmB3tYbSzR6dKBYPKDkZ7FZs32PH5S460Tl82lR07ZzrvSxn5hz4nH5HRvNHBEUbHe9DucDc6Bt2Lb+66G8fuuwsfPPwjHAkPBBpqgZMfA199i9bT5znwSlFT4t0b99fDxqVac10AVA68sigvA1cJeM8DIDsD4OCp85g8PQ3e4bPhFjQD8/JKceqMGec6LDBKZd3ovCgB0mzphMVMOmUi6Z7AXiLrG+cEZcuKB667B1T0/0gOvPFVzQgvbUJc9VHElNYjrowDL8kHWFmvqmOs7a6IvDJQvULAJVjsbgSfZNblcg2vtL5Yh68n9LTStsT6bF3xGYdTEQVmGttu+7Ztq+sxyc+Ha3u5rEEcHwdmW3IcLeca3p6BN5akDxW7WYSXqjTYgNeFAa8ozi+Pevbn6YtHc20tb8PyPMEs353VeA0pcIV//mBodY9jYeqjeCNzDI5NegzHH1Dh7J0qmBwk2GVwYYeOAXbodHSA0YHMDqb+bI72MJE52DFfdDio0GlnB5OKGwMFe3t0qFTMDPb2OOOgwuf32OOrH98F+Ljh1J0qnBtgjw4He9Dn5Nd2eqi4g6a7+rfTkXxP+1J8L3xusFeB/E4PFuR7mif/0XVKy87eaYdP7lYB/iPx0T0q5u+2Owagk6DNzgFmlT3Mqmsc6aGmLxs7dzr/SxsDXOYv/nDAr2sHdNgPQLvjnWi7YyC+G3QHPrv/Tux8ZBCaYgKA4/VA6xmYTBa0dprxbauBlSnroFq1UvM1iUOv+cZM35cbTzCjrUtASVBLK9CsWJEtM0kFiAGjxKS0ktHQBoOxTerAB7QC+KwdqP8S8J+Rh2HBS+ETuxI+casw2C8VS4pewqHjraByxK0GqkJhZm2rgbOA+TuYjd8B5ja2LzOvbMbO1cJqEN/8pL1rdrTyRcUDAFT02uO0LMKbUNkMdUlX4CXJwY0CXgGSt9ooYLf7ccmBV8Ax0xJXCOBt4YlyUlkyBXivrgVxd+AVdWFFjWJqcUstWIOptFueB+uaFpbvykpcxeQMRkrq7/Fmxhh85PcYPrlXhfYBdix6ySNpdminiOUAB3RSdIeZAN/+ORL4C/gnYG0ns7cBL4EEwUEni47ZwaJyQKu9HT6/xwGnfnI3MHoYPh9kh1Z6gLBzgEllD5O9I4PfDumhwmTvADKCXFpGJt8vh9/+6X/xQEEPCuxhQYIy+i3YA4TKDufvcJCA14cB7xd3qpgPzSo7CXQJdq/V7DnwEjAS+Pal8TKQK4fgiwFvp8Md6BwwEB13DMCZOx3xxb2O2P3QHWiI9gc+OgR0UE1e4JwJ+L7DiNaOdhgsZga8hKXEoD/0T3As74wmhWrZHsxWqLXBri1JjUVypWAwTbe1tcJoInQ1osNkxNfn2xnwbj9yGsGzV2Bs3AqMiH4OE2ZtQlDKPzFhxgZ4B2VjTuYG7DvSzpLyvm41osPciU7jtzjbegLnzpyEoZ2gVwJv6YSp9JmpF+oQ/1BfK9/v3x5QgLdb5FcArgK8Vweq1ysqfWXA68GAl+CX9huRP4wV54/JGYKUlMfwZsY4nJjIgbfNkUewRNSszZFDnYEAzI7MFvHpb9MEWFawkoCLYPdiwGtQ2VuB97N7HPD5T+8Gxrjji0H2XYCX/ErboG2TTwV40DQtI6hmMEfR4H7sf3HuAnblwCuW0ev3Gwu81xgZvtaI8nX/nu36EtdZT6PwddfRASaHgTAOuBOGO+7A2YGO+OpuR+y63xH1kVOAY7VA21kW/aQavOcNFhhM1NuOqt7aWhpfP4QgehXAy0mWQJb9iYGkB9YyZFJFBgq9ShFhSkCj0mntFuAcgM11p6Bd8By8wjPhHJyH0CX/RPCS1zF5wX+gTv8vPIJXYnhwDtIK/4p9RzsYIJ8xmnHefJZ6zaGt/UsYqIwb69jGCZ8OiR0D2+f1O3tlS4oHbrQHFOBVgLdLt7frBa7Xup2rBV5Nnhc0eR4I0TtDAO8bEvCevNeeaUpNpNuz46+M2ySgI3hj0cjLaP8upQu8HT6jV+jsNTpFcO047MqBl6QNBAnkLzKKJFKEl4D31M/uuQB4jUwKYY825m+uqxTyCPKXAGr5fm8HP/6gcxDRXQcpEk7+trdnv8elgJeua4udo2J2DrBcxAT8dgVd8aDlALP9IJgd72LQe26AA74a5IA99zuiQTMZOHIAaD+HTjPQaQI6KGGNSpEx3Ls+0V0eIia47W42BYMtAswDrRRsJeDk3zHC3MkbYBAcnz3XzmD3DFVjOHwWcSlr8ajPHATO34ipC/6E4KVvYeri9zBh4buYvPBtBC/4JybEl8PdX4eUFX/D3g/P42sT1/22W9pgMFPUuBUWs9RamfZNldnocCUIv9GQomxf8cD18oACvArw3mLA6w61nox3XROthbtKGkjW4IGwXA9o9V7MKIEtNtsJi1IfxxsZvjjh9zh6Al6uk1RZNaoCxvrjyCCtW8KSAFG5PwgWBBQT8J61t8On9zpy4B3rwSChTZI0GFXct0LzS9vhr965TEI8eNBIyVLy/fTHafYbyICXwI0grTvwfnqPHeDvg49/ZIcvB9kxSQMH3ovD3sUg0LZcwPIdsNj1RXMEVF2t+wNAd6kHf6sj3u44wGR3J4NeivS2Og7AN4McsPc+RzSqJwNHa4HzZ2A0WWAwgpmZAS/VALtet2AiR3lUl6MsbV5EkeWjWJPvnr5LAG6AxWzEmbNtaDeCRaTf3Pkp4lNL4R6QheGRq5mEITxzC8Y++Rb8Urdi4uLdGD9vMwJStmDSrH9gdHwlhgXr8GT+y9h/sh3fA/imvROthg50GM7DZOZSCRln34gQ9/VyqrIdxQM9ekABXgV4+zTwanK9JeB1Q2yWCxYuGsyA98OJj+PjH1GEl0DLFuGVAy+BWH+ErK7nLMCTj5cEXiZBsAHvF//zI0AC3naqFsBeVxPg8iQ32hbtqzvwCthVgJf7ikXQJegVEcnukgYGvAGjLwBesf61jwNgVvVdI0253LoDbvf5LsDLznsgLKo7Gfi2OXDg3X+vI5o0U4CjdRx4jWYGu5QcxoDXTA0nrkeEk6ofkAmMJYDlmxaQKz4R84TD8uiuAF5aRuu2WYDXt59EQmoFhkxchrHxFQhY+C+ELN2CcfPewZSlezFx6V6MWbADk5cewKRFuxC0eDv8F70Od+1qeETkIm3VX7C1+TSlreEsRbfZHgmsqWoDZa5JjE6Lrhv498gnykLFA9fVAwrwKsB7SwFvdymEiOxeMOZ6IIIivDpvZmE5wxBDLXAXDcHry31xzG8wA97WATwZh0o1GRxULAOepgkQLMx4CS4qw9XfTJ6dT+cuHgwISAUUU8RWDmQ0TVUaqDzWqZ8T8Lrjq7vsWcSRRyc5xFHkktaV+5n2x34HKptFv4FU/qy/+V2cr0gSE/4V/iKfCeClSDlpeBXg7RnKOexybfnVgC8HYdrmHV2A99u7HHHgvgE4rJ0KHDvYBXipzwR7n39DgJco8kLgtYGuCSaLkZULo5JhvFwYHZABBlMnayxxxgy8ueskYlPL4Ragg++0DZg895/QZu3B+CffZbBLkDs+ZTd8U/fBb/EB+KXuR2BGLcbO/Tf85v8ZI+LXYvCUJVi08i/Y/WEHvjgPtFKpMhhhRissxlaAzp/vmh0ySSkuZteVVpSNKR74gR5QgFcB3lsaeLsDsHVe7wm13hNa3fAuwLtg4RN4Pd2PAe9H9/AMdwtVHZBKk4kSZLBzADc7wK7/moBegjCapuS+qwLecR5dgJc9RNg58HqwdvZMWyn8zH8HXqaMpsn6s+/p3IWEgUGv9MZBAC+raqFSgR7aegJeehihdX+YieoOBH803bdG/tB6Y4HXYDTDSLpVFtUlOYMkabgu0U0BujbYpc0K0BUpciZLJ5MVmKkKA5lUI5fWo8YSVGnpvwc+RfzSMrgF5WD89A0ISHkDwUu3wm/BZkxO3QW/lF0YNfcDFt0NzGrAqAXbMXr+ToxLoWjvVkxd8hamLPwr3NVPwzMsDwsL/4baE5349HsL2lhkuR0WSxuHbQJeUjlQgFoB3h+IYcrXb5YHrMA7icqWpG1EYlULIssPI7qiBbFlDazpRGx5HSIrmljDCXXVMdaAgpXnKqdOZV3r2IoqB319vF5VGuIrDiO2fBdrPPHkq6usdXjD9K5Q57lCqcN7kWoQek+Ek+X1/DnX+XpCk+MNjW44QnWeiM0axiO8EvCSpEEe4aWoojARZevvo1kW2SbgZRFYeV1WldTQQKrFS5FbqrlLWtLP/989gK8X05RS5QXYk6zB9pqeRSxlUVyxfSW6K3+bQMmTPLlPRHhpnmmm7TnMttqrcOo+RyBoLCtLRhpe8jfBnnhws4GfeHNxtSNJAwQ49qXxYufJpQ7dJQ22eRExHgizaiCMqoFotXNgGt599w9AS6Q/cPwQq9IgKnLRyCOrUuKaqKBwDXdrAYk8qsthlyWjUVKYMKrGACOMJkpKawcs7ehsOw3D+TOAsR0mgxHfd1qY9ODPmxuhSVkNT20ePGPWYsqi/yAofRv8Fm6B38LtmLBoJzOC3gmp3Man7sS4tF3wXboTExZvxoSU/2DSotcwdcFfMD6xCp4h+Viy8h94f/8pfNdBsWQTzrd9D7PhHGAyoPVrqtNrYm2JjUbjBe2JqS2xyWRi7YmvwUXKVxQPXHcP9Ai82rIWRFG3tSsCXlnTiItES/si/F4v4I0rb2HAS62F5cBLoEud1hTg7RloGexeAfCqc7yhzhXA62oF3uMTbJIGEb0UsEtjfwddcf43E3jF70DgK/avjLcC8NLvQeDY18aLwa4AdhG97mm0AS/BrsFuAEvGpCoNBLzNURLwtp9lAEqRVA6+PEmMQJWg9Vr/ugMvbckKulIVBovFJNXVpQYPHeg4/x0M7WcBYweVi0B7B4kMgFfea0Jk6joMi9DBJ7kcAelvYMqS9+C7aAv8UneyyO6ElB0gm7iIxm3MfNO2Y2zaNoxfsg2+S97D+JS34JfyJqamvA6/2X/BmJgKeAbqsLjoT9hR/zmo8kO70YDvz3wDmDrRee4cTn/3DTo7O61ukEMunaMCvFbXKBO3gAeswDt5zkoW4aXGE1cPvLYOZ9GVfW265wi1ArwXAdGLRFwvFom96uVSZFd0U+sOvjyyK6o4uCNC54VwvTdCct0Rk+2KRQuH4I10X5zwHYyT99iD6u7KQUtArwJaHDovC7x2tlasVCKLoo8UNT95jx2++J9LRHgvotMl2FWAtyvwk3aaEvhE5Lv7PI/w2gPBo6UIL+/ERtfwD4/wdj2W2+f/RVcYtkV25fA7gEV2uwPv3gcc0Rw1FThRB7SfZXLVrsBL7/M5oF7rPVwAL1X0ZSb1deiaBMaFsiYTYW0Ha/VrNHSgs50nzZ03Av/aeRIRqWXw0OTDM34txs77M6Ys3wKfBe+AgJaqMUxM24VJKdsxcdEHmLhwCxsnpHyACWlbMT5tM8Yt2Yzxi9/FuJR3MCH1XfgveR/+KW9j8py/YVRUMYaH6vFkTg3qTpxlel46KgJbo7ET58+fs5ZqM5spysuj3/RAIOZptEWy5RIOHtm+Vh8q31M8cLUeUNGlSPqfnoA3rrzxCiQNfQ1wux/v9QNeioqTFIRBv9RprXuEN6AwCEEFw6FIGi4C1FcEvLxkGcFvWK4XwvK8EaQn4HXBIqrScAXAq0DX5YFXPCgQ6NLrdgV4bwwcEuCSiTcPNw94b8z53BrQfCXA6wCjiqB3AKgRzVkHFdOjXxJ4qQEDQ+DrDLysmYRU9YBJJ+hWzoG3o5NaSBhgNBvQ2mFkpcfOtANb9p9C3OIaOAUXYExSNaYs+Td8096BX/pOjJj/HkanbJOAdwcmp2zFZCpBtmizBLzbrMDru3gLfBdvxrjU9+CbuhmTlnyAqWkfMOj1n/d3jI4pxqiIPMzJqkbdiVac7gTOGah6A4tzW8HWYOjoArwUoSYAVoD3arFMWf9GeUBlsADfmQCh4RURXgI3BXgvhOGLtRYm2O0OvDHVtRAaXiFpIOANzPdmwBuhd1EkDSJi3A10RYRXjCJSLOrzaqQ6vVSPN7jAE4F5bojKkcqSpY/Hx75/YIk+1GlN3IAJckWEVwFe7heCK+EL4R+KNnZZJsGYHHgpiUpEeL8YZMci6VzDy797MT/TdsW2xe/S38crAd4v7rUHgnqK8PLEv/7uw8ufPwdgW4KfiPReCLz77ndEC0V4j9dZNbwiwss6jknA+wMUDdZELxHh5RFfWSCUqSWoHBqlixnZHr9t78QZI/CdEXhzx0eISymBe3ARPCIr4J/yJqYs+wBjUrfAd9kejF+6B75Ld2NC2nb4pW3B5JTNkm1h0d4JqTvhm7aTfe67eCuLBo9P3YbxaR/AN/UDTEjdDL+U/2LygjcQuOg1eGvWwjtYj6RlVdh/vIPphr/rBFo7OkGgK6CWRjn4KsB7o9BN2e61eMAKvPIIr6aUw5sCvFcGvJTQ1x14CXbJ5BHeua88BwV4ryyyK0BXjBcCrytrThGc54GgAk8E5BPwOmFhyh/whgK8Vsi/HAhcT+A1O1DSmgK8l/N598/JZ/LfgablEgeSNCjAa3tw7e6/K5u/OPCS3IHeYJCfvxmkwr4H7C8AXqGx5S12eZWGHw68JGcwwEJGkWMS8RJZC7omDa/ZwGCXCiKQsOErM/D3bScQv7QCLlMzMSqqElMXvI3gJTvgm7oNoxftxMiUnRhDsJu+l8HrxJT3MTn1PQ68qduYrtc3dQ/I/NJ2g8PvLvim7cL41B34/+ydB3hUVf7+79RMJj10e9n/rmKhVymh95JOR1dXVxECBBClBQKou7q6rkAattV1XXd/q65rwQIIAqF3QpfeSa8zyft/3nPuyUyGBEJZYuLwPMczt5977o33c9/7nu83bOIP6DrxO4RN/BZDZ65F1yc/xZBJX6Fd9CIR7mzyHz7BtlPAWWafY8gyXcXlORB4aXVQAKyWeS0N14Jn3m1udA+UWxr6PfOy8PCOYTSGxbsxLGn3VQxa87QJ1KbpS6GWg+yq6+EdnrJTRLCoCnhHJe3ByKS1+O1bH0EBLy0N3kFrHuB7icLbXlgVLge87MPBia0wcEFr9J/fHMPmSOD9enoYjvb4NU74UXnUH5RUFlUsXpNXZVSQ4A5al1N4xTIRBkuquZUpvF7gvTYoq1ngrfjp//qjPdzM/bn+tss4ELIagyFdCq+KES2VXgW8F3w1bA42Yt+w/sBBqfAKsKXHVthP9by6goCv/XEsFV0nSsuKRGGmNDhLXUnXnKUoYwGQXwpccAIXAPxr7SGMfD4VzYbOQrfH0jB04jcYMnkNeo9bha7jf0TPqdvQ7bkt6DRxDR6ZsFJYFAi8fSZ9hz4Tl0s7w+R16DZ5syg94zehx8QN6D5pE/i7+5R0dIlfhc6Tv0fX+O/RJe5b9HvuR2mHGPcl2scuQfPBCRgzYyk2Hc1HVqH08hJsi4sLhbqr4JbzvMB77feId8sb3wMa/6Cyy4D+4/4ggHd06m5ELN6F2CW7MHLJDuHhHZ3kCksWk+oZlkzBLcGRv2tTXTnsXhvw7sVwPZwbt5cK7zaMXLIbIxavqwC8QxZ0RFRiK8TOb+W1NFRpabgS8MoIFwp4ByY2FwrvxIn3QQBv9/sqAi9hzS0s2c/lszrb4d4Wz2mCqZrnvt7l5rsvU2DrWat93kjgpaVB7a8yS4M6pqo923Tt065YtJ77IOCIcF9V9LNnn16yvb6davOV1vfcvjrTNQu87klYCKvumctc/XopKCqwVeG/ZIIX0U/lSV3Uvriu62VArcN9Xh9gu/bpvv/L/a7sPNQ9QoVXAq/ZA3j1EWUewHv9Ci8TNhB6i0VqYAm9DsBRIjK6McJBgdMpsp2dcQDLM84jKv4NtIyeg06PLcKASZ9hwIQV6PfsOvSZuFGCa/wWhMVvFDF2u09dJ3y6wp4w8Xv0mrgcveJWocckhiPbiO7xW9Br8hZ0j9uIrhM3ovvUzejx3CZ0m7oOYVNXofu0H0C7Q/dJK9E7fo3w//Yd/zmaR7yGlhGJeHbB+9h64Cwu5uQLUbrI6UBBUb4YhEfoFbDrLBHgXnEw3o0HGe8evT1QnR7QSkqcOFckPbzdpryLkWm7Ec1QWmn7QLV37JKtIPAOT96NmJQ9iE7ZL+qRyTsxKnm7Kw5vyh4Ju7WpZlurKMNT90CUZNoVXGVk8m6wjEqSNbcfnpqBEckHMDxpv4DeYUm7MCxlM2KTNktLQ9J6PJr2IZ798M8Y8lI4Bs5rJ+LLej28VHkrhmaLSGwNUea1BdVdFaVBRWeISWgN+ndjE+TAtUHzpcI7aF5zjJr1MOLj7sOy5+jhVVEaKj4UFbhc7qF4Ncsq7s/zAe46tlpP1TwGf6uMWupBXD6tq1UESAGPZpkpzqkr1QreVfY47kvtk789gdPzuIRArsP9q/NV21XHw8sIGCdvDQB6tMdpX6OIC6uAVx2btWe7VDtUrY59rTWjFPCTdAmzk+lJGNS+eIxCA7PAmVFsNukJNTSUmTSRiKS4ikgS3F61T5wL+0oVt3NSx7neunrAawYGVRaH9/o8vDxPcZ1E37EvVbguC5z6y4K8J2VSEiYmUesTjtX6nMd7sUgvvCZlmkUv/C0V2PL71mgSA8Uk9Lruwevty6q3V3+bCsJZy3nuwMusgSIOr/DwbgcKs2X4MaXollLh5WAyPRFFdZ6ylayjPLvcrSq0LxQW5aAMxcLmUFhajHzdyrAm4zyGT34dLcKn45Gxr6Hrk29jQPxX6B+/AQOf243e8VtF9rTeUzehW/x6dJmwStgTekySVgcRlYGRGURZrcfiXY8ecevEdoTkR+LX4ZH4Negav0aALsOa9Z28Gj0nrECvyT+KbcOe/RK9xn2G7k+8jzZD5mHqwvewae8pkYPiZFYO8h3FIiubw1mIwoIceXLugRl0pVyGeKukY7yzvD3wP+wBjfvmqMveTy0EgXf0WxmISdsnog0Me3MrxizeglFLtgjII/DGuAHvyOTtIPi6A2Fd+U1friiEV7cyImkXWEYukTWVcHqehy3ZJ0tSBmKX7BSwS+Adk7wfo5LWY2zK3/H0+3/CgPmDMGBuW0QlttHj8Hp82leK5y+mvgbgTWjtAt7ENsLSMHhec4ye+TCmTmiKb6fJsGRMPKHCklX9ILy+h60CI/XwdClkfJi69q0e9K71pbJWZDKCRQGvmiZAcHsFvAJsacUgcOmF65QDxlUCrwLr/zXwqj5wP2/O85xW611NzX2Ug6hB+jA5T+2Dv8uB12QWyRzE+V4OeLm9m6rL/bOvHPr+Oe1+DHWs66nZJnWduR/+vtTD+z8GXp5zBeh1fepXwMt7jcDLade6TNxgERAsgNcs70kFvEzqIH9XBN4iAq+BcF3x7+Tq+1GBrOu6V74PtV4lwKvfRyJKg68JG0Lc4/DqwKsAl2l19UFrhLZr/aeAly4GWnaz8vLhgFPAYlb+RWQX5kqIzHVi/f5MxI57Bff1mIDev0tF2BPvYGD8F8Km0GsSfbjb0GV8Oh55ZjXC4taCCSW6T1yDPlPXQwEvB6KJwWg68Ir4vJPXiti8VHw7T1mLTlPWostz69Dr+Y3oN309+k9Zgz5xq9F/yjp0n/gjukxYgW4TV2DgG7SzhgAAIABJREFU1NXoN/5rtBj0J7QdMB3jZqZg6+ECnC1g8DQgqyAP+YV0HDtQlJeP0iKny5vMk9X77Tq671q73bvdL7wHtOJiB87lAz1/N19aGnTgpcJLS4NSeKlqxqZmINbd0pCyo84DryfAeyq89DozMx3VXaq8I1L4skC1e5sov116GI8t3YLH3/qnUHgHvzhUKLwEXplpzQu87sk3qqXw/oyA1/Vw9XygVnyQE5LcCwGZD3sBbLpqSIBlBi1CugArpUC5bes6ntxfdYC34nHldhJc/vcKr2qvakNV02r+1dTsL3q0Wfibxyj3curgy7jBhFVCFxVLZoJTfc9lAtzcILl8ewXlyhIh1Ex5za6mjdVZtyaBV8A9X67c+0C8qMn7WV031uxjCb98uXCpu0LpdXsR4wuZ7GMJtQKQ1TH0e1m+QHAfFf9OqtNfFddRf3e1EXiZSU3ynx6NF8WlTuQUloh4t1R3Nx4oQL9R89Fh6HwMGfcPDHj2Pxg6dRWiZ2xEj/Gr0Cd+I/pO3Yqek9ejWxwzpqWL0m3SjwibuLoceF0Kr0w6oYCXCm6vyatEdIdOU1ai67Q16DF9HXpNYZKK1SJRRY+4NSIdcd/pm9B32gZ0f3YFBk1ehdip36P/b99BuwGz8MzM93HOAZzJB/L1sXd5BbkoyM8VFo3yExVvCZR8mTRZxez9hVOY9/RvWg9oDkepCHUycPwr6DX9A2FpiODn+tS9YIgypfBS1YxOpt1hn6hHJBF2t4O1uwJaV37HJu8Ci+f5eCq8BF6RqIN2BkIvgZc+5rStAniHvyk9vI+mfoTxf38DQ1+OEHF4RdSBhGZVps5VUQnqfl27FV7Xw1c9eJWCpKblg9gdHMRvHXjVfO5HAK9ZJhUQAKenjpXpeiWolSklUwexXzrw5hN4LbLv4AZVCuRc/cvroa4Na35ulyB8CXS5wZ8A4nLls+I1dV37K8HW5ZfXNPCqvuI9p0pl58a+lIq3Al4Fvcbyrw4CnPX+47p8gSvUX0jUMrkftQ/26eX75/LLq3tN1Hru94Ccp87r5iu8jMJQhGJHAQqdRQJ2c4scQiU9lQNs2l+Ex+JT0X7QPHSJXYKBz/wH/cd/j57PLEefuDXoHbcWXZ9diR4TfxRgSxW255R16DU1XYQcE8A7ebWI3qCyq8l6jci+RujtH78KfSavEAkousQvR5f4leg6eRW6TfxBAHXfSenoEy8jOtAmQcV4wNR0DJi4EgPHfYO+j/4DPYcnoWvkQgwauwDnSoCTOWXILqZaDZkprkxXxYUVhBEuGJmCtg0Zyfem0Y73QL/4HhCWBsb2o6Wh7dOL0XPBcrR+4Su0e+6/6DD1U4RN/xRdnv8UnV74Eu1mfIV2M74VdacX/ovOMz5H5xlf4JEZX9W50nHmV2DxPLfOM74CS5cXZN3h+S/QYcZX6Pj8t7K8sAwdZ/wXHWd+go4zPkXHqV+i64x/YcDCNzE2ZT76zRsgFN6YBe0wbMEvXd29Rg9vrVF4XQN53B/aEqKkusWHrQABXVH0BFgYqEJKJZbr0osqIMJoEGqb+sws9qPvQ8CyUtKU51KfZjvEA16fT9hSbVPzb6SH133fqo2qDe7Tar2rqbl9MX3QJunLLQcqniPPj5/g9eI6ljvwqN+Xgy53UFK/XX12Ne2tat0aBV63+45gypcH1kKlVfcm7x1dQZdtNYiMe0o9F18LTLK/hcdcv195bdwV+PLrI7zrN8rDW91rotZT15y1nMd7g3BeE8DLhBJUOp16rN3zuQ6cyQF2HCpB9BN/RLMeUzHgsaUY+NT/YfCEbzB40iqEPfk1wp/fLKwG/SavRN/4leg9RVoWGFKM8XR7TlkjCkOVsSjQFSmGJ64VHl4Cb++45WAUh57xK9Ft6kqExf8g0g33il+HvlM2oNeENeg+4Uf0n74JYXHLRXze8Blr0fPpL9Hv6S8wdvo6DH7iX+g1cgl6jXgRQx6dLcKnZRUDhaUOEXbN4SyA01GgW0EcKCstFtArrSFUe73/vD1wc3qgHHiZeEJ5eGOX7seYtw6IQWuPJm2T2dZS9lRqaWBUh5GpGXWujEjLAIvnuY1OzQDLGCbm4GA1fdDayJSDYHFXeIenbsHopH0YnbwBj6X9Qyi8atBaeEJLRMx52KvwXsugtVoNvPwsLh/2zO7kArFLLQpcBpNR+DmZ9EGAhUUqZoXi87L0S3K+glyl0HFb8SB3W6aO5T6/NgMvgUUOrDLBaTLIT+4Glx/WoavlBSYNHKAmlVwOxjKjWDPrHlKp9Er4qQxk3UGJvytb5/rm1SzwSqWV3vE8i4Zcq4Z8Hzmoj4o5QZcvDYRgvlyJ+0oArQElGl+6TMKDXqQPquT6hF6+NPEeJfC6K7xcznuU24n7/xdtaXAiLz9TZFErE2goM5jtOpSJ3078I3pEPI+ukQsw8LdpGD71Cwx+9jNET/sOAyd9hbAn/4Wh075D37jP0fPZz9B74jL0mbpKRGXoNsmVSMIFvGuEX7cnYdcNeHuN/0GGKotfJQCZsEw7RO/J6RgwZSMGTN2Azk99jb5TlqPPlK/QO+5T9Br/MXr87gMMn7YMo6atQP/HPkTE799Dt5hEdOj/e/xu6gJkOYDjF84LXzJVbEaiUOHKRLxh76i1m0N43qNU6IHyQWu9nlyArpPfxqile8Sgtco8vJ6D1kal7JBRGqqIdFBVBITaMF9FafBs66iUPWAZnSzrcuBN3V8p8I5ashcjl6SLQWvj/vY6Bi0Mx8C5HbyD1soH5dUNS4P69O36bK57dN2AU8KSHABVaLKAxWk0gSpumdEgoK3IYgALAY6lxKIhz6QhzyCBw6l/vickSXDgfDmwil5JKlUsAkx036WC4boHvFTp7HBqASg025FrtCFf80GR5gOH5osSsw/yrCbkWDTkCyC2ivWLNV/kmGzIs9hRZJSDrq4MvAp8rw9uKwPmmgNenpMvStlXBh8UmCzI8TEhz8cAviwwmkUx+85HQ5avhmxfqrgGcW+Jly+jEU6DFQ4OCGQUDLMEZQXJ6msFgdn9fuS2YtAaX/h+4cBLCGRossJiWhqAA8fOIWbsBHTp/yi6DHoWbfpNQKfIWegUNR9Ne0/Dg/1notmQBLSMmIsHBs1C69jX8GDE62gz+j30iFsmgLdH/I/oPtWVNU0qvNIC0XNiOkSZvE7YGvpMXI/ecUw+sU4UDmALm7Aa3SesRR8msPj9MjSPfQ+/GfIqmkYsxMMR89A6eh46RM9Hq0GzcX/YNHSNehntBj6PblFT0b7vWIQNHo7Js+biZCYH3xXCKQIY69yhTMtlAANeeP95e+Bm9oBWWgpklcg4vD2m/VUAb1SKKyzZlRReptr1hMK6MF1d4OWgtmEC+DlYTUa3kHF8t4AKL1MLC4V36d/1QWsRAnjpzZVhyX7ptoa6DbzqoS9hk4AhB/Lkmn2Qa/ZFkckOh9GOYqMdRSYb8i02FJqt5SBRYLMgk8qbsDNoIPASIKhaKtWNwCtsDjr4egIvAUMBhwIuNU27hPs8AdGe9odKUgtXNyyZ+75lH8jjqeOr5ddWE5jscBgCBLxmma3I1nyQr9lRovnDYfZHjo8ZmVYDcsxUxm0oNvqhQPNDptkX2T52FBoJxzKSQOXwpUBX1a7+urY2X7p9TQGv+NKg2VFk8EcJa6MP8i1m5FsJr/LFK9fXiEw/I875m3HRzyJCvDmNZhSbzCgyW1Fi8hOl2CxfMLiML3Fqe3fLDa+5ikJC4JUD39ivl/ZJ9edV97qo9X5OloYylDpkhjXG9C0oKcXv46bidxOeQ+zjkxD9xHMYOW4+hjw+G0OemIuocX/A8ImvYeCTczHoqQQMfGoBuj/2GlpEvYLWI1LR/dnPhTVBpAieulrYE2SqYDn4jJ5fd+Al5PZmwom4Deg+kcC7XhSGKus1YR36TlyLsKc+R4vYJDwwZC4GT1yM8Al/QtiIqYh6eiGGjfsjIp54CeGPv4joJ+ch9qnZGPXMcxj9zGSMfXoc3kx7C0dOnEZBIQeoycQdTkZ006HXC7w3E/W8x2IPaPxDy3FKD2/3qe8J4I1M3oPRS/eXR2kYk7xNqJoqSgOVXoJuucIrkk1UncRBDOKqZetUN9OahFvG860IvGLQWtpWMNMagffxt/8BhiWjwjs48RHQ0hCTSNjzAm9diNLgqfBymuBJtZZgSggg2EAzw2Gx4aLFhiy/YGQaQ5FvuwW5tiY4qwWiyN4EBYZgFFB5s4Ygy+6PizYbSmw+yNM05PNzcoANF/2MOOujIc9iRIGRQCcLgUIBL48nlTgJvO4QoYCzNgMvz4H9yj4psGvIsmoostvh9A0SSi9hlmrlaX5eD7bhgllDlsEHhdYA5NnsKPH3lxBsssFhtIKwJvZJX6sOYeq6VqjdBrW59+m1/q4p4KWlINNiE/BfZPBFmUH2AdVaQm+WzYwLQXYcsZlwzj8A5y1+KDASjv1RYgpEsSUI2aZgXND8kc+XDs0GWIIBawBKrFaxD0Yc4csY70l+gcg3GZFrMgpLCe0oVSvr1YXg6u5DrVezwCvDkTHhBIvbV35G7iqVqXqZRpjhvVirOLwM8pULIIeJovSaZojle50YNv0jtBuZjN4TPhceWyq83aasEqV7/AqZXjhuLajmMkEFQ5mJdMKTGJZMJqwg/DLNcK9J64TlgXDcj97f33+GVpF/wsRX/4uMHOAiw5jqbXFvG9tHl65quwreVugog8704nx5zuXFy2DeHrjJPXB9wJtKEK5boKvg/PqAdwdGpG0WkRq8wHsloK/9Ci8hiQ90AZp6JAACEh/watAO0+5S3YXBB1TDTlttOBbcEIdC7sKBBvdhX6P7sDPkbuwPvQeHQ+7AocBbcaheE2wLCcbe0CBk+weJ7XI1DVlGDcesGk77GsqBlyomi4ILBW51HXjhY0K+QQOjNRB4z1jMOOcfguN+9XEitDGONgpGRn0b9t0ShL1N6uPQLbdhp28Q9lhsOO0fiFxbAIqt/igx+8JptpS/LJQPFHQPH0erCKfrCPDy5SjTx0eUIiNj4sqBXA6TCXlWs3jROmI04aRvMLJCb8eZoNuxzxqKo/Vux5F6t2NvQEP81OQuHAq9FaeCbkNO4O3I1Kig25FjtCDXYkIuX8jM/FswgcfItliQZTaj0GAWiUJ+6cDLwAUsVDsdzjIwWxkHexXR5oBSFKAMBVR/y2SK4QIHUOAoQ6ETyCsDlm05j/AJKWge+Sp6jvsEPSYtR7cpP8pMacLWsEIkjCDAegIvleAe8etE3N6e8T+KUGQcyMaBbVx/wKQf0PeZT/Fg/wQ8PmMpth7LEwPSCONsR3ZBCYp0SFc1bRklDEvG+MKlQHEJzwvCtuvmZhARHLyOhptMe97DXafC6wVekU6ZcDwihQPYlKXBC7zVV67rLvASKOgfZXGazTpQ2JBvtuO43RcZt92O7Y/0RHqvCGweMgw7o0dhV9Qw7IiOwdbowdgc3R/bhvfD5k7NsMc/CDnWYBQZ7Cix+OOi1YosmxWFJpNQzc7ZDGBxj+FLOKtoqbhUNVNKJtVJAe4cUFRrLA0coGaAg2qsxYQi/0AcsoXiXLPOONEjAhl9BmPP4L7YGzMQW6L748eh/ZAeEYW9MaNwIWIYdt9+L0771UeBxV+8TBSbrOUJJtgXoqhYyeUvNHUHePkyxEFq2T7SH6683qzzLVbkWPzgDLwdZw0Ncdx6O/Y0/A0yevfD+qEDkB4xENuGDcXGAb2wo0d3bHmwBTYFNUFWyL0osDVCttku4dZiFHYSwi4V9yyrBOwCMwet3Yi+VMrtpfd2RcVdrfczU3jdKJCqr7PMIUKVOcoK9ZBlDFtWguISJ4qKHWDcfBZHsROFDmDF1jOIGr8ILcP/gN7jPxPRGrpNWYuuU1ZL6I13AW/fuI3oE7dZKrz0+cb/IFTgsCkr0HPKCvSeshJ9Jq8sj7+rgLfloHkYn/AOMo7noFiHWdpyqdQ6HMUi9JioHQ44HA44nWUC4LmOowSgjUGNUSPkypjDTLNBf4M3SoOXQ29eD3gV3iqsFtVXeHfABbwyasOIVAW8m72WhitaNuoe8FIpo9pLtTXTqgkPLj8T8xOuU7Mi12zH0cAAbL//18AXXwO79wH7fwIO/wQcPQYc2Q8c2g78tBk4shl480Wc/c0DyLM3QL7mB4fBD1maBZkEWqNFAC/VXqX48jO/+gRf14GX58nYxCWahgtGH+wKvQtY+BdgzzHg2Gng8D7gBMtB4OBeYNsO4NBJIH0bLob1wzF7PeSZ7Mg32oTqWKzA9nLAe90DrSrCWU1ZGgTYmjXkWjQwygLDuKkoCnlmH2RaApFpvwOH7XdhV+OHcbhfNPDfT4ED24C924DDe4C9W4ANq1C4cB7Wtu2AbSF34LhPQ1z0CRK2kSwdeAuMZhSYJOxesFnElwn6fK9fLVcgW7FPK8Iul6n1ahZ4+Wh32Rpcflb5yFcJGYpRWsbIBkUCJp1M11vigLOkVGQtY+ayshLCJZC+6xyGx72JtpF/RP+4/6Lv1DXoFr8OnSf/gK5TfhDxdZl0ggPTKgLvKoRNWY6waSsQNuU79JzyLXpP/U7E5O09kZEb1ghLQ4/f/ROtBidi2osf4PCZfJQ4gOLCIinjiuYWAc5CwFGEspJiIefSl1xGsiXwOkrBcUKlYJIN/pegK0OxeRNP3DzQ8x5J9oAXeL3AW8M+4roLvFRbL/pouGDlaHezHIim+SLL5IuDQUHY8PD9wN/eBQ4fANZtAP76N+BfnwD/+Qz45GPg84+Av6cCTz+GoyGNkGMMQI5mRYlmQ7FmRZnFVyiSeWYTTvgZcdJffkL+pQAvYamI6q7VLKJcnLXZ8W3ILcAri4CDR4G9GcC3/wE+Zb9+CHz6KfCPT4FPvgGWfoATv34YZ3wJvPSg+qLQwL6lEk84UgCl2xh0a4MLnNTy669rCnjZfwV66DACr1OPzEBLA6NYHPcNwa4Gd+GHe5ri4G9/Byz7Eji+H1j1LfD6G8CbScDn/5IAvHMd8OE7WN70YextdBfO+dXDBc0k/Lr07TIiiQLeTB8TCi0yEsQvEXjd4YOf/YmB8r8lIkZtqbMIspSgjCtQCGWhWipKqRjs5nQC6TtPY8SEP6Nt+Evo/+zn6Be/Ft3i16NT/I/o+hwjNSiF9wrAO3UZ+k5RwLtaDG7ry8gNv/8MzQfPx8TEd3HodJ5oqZBsS6CnDC4GnEUCeIWcS/+CAN5S0XanswSlpeoE5ElI0FUuX6/C634/eH//b3vAC7z/E+Ddpnt4vQrvla0NdRt4L/hqOEfg9bECVjtKzf64aLIjI9AfPzz0axT8PQ04fwT48AN80jkMPzzSHVu69MDGdh2wqVUL7G3bAvvuvhNHTTaU+IWgwO6PIpsvilRaXM0EAu9xPyOOBVQEXqrMVHg5aK4qsKjNlgaeE2O98hz5OzM4BN/ddg8cbyQDJ04B+zLw4ZhY/Lt/d3zdoT3Wd+iCHZ37Yl/brrjYuh0OBQQj278eCq1BKDL5iSgFDmZgM0gvq+wbqQwqxbyuAW8Ro3/osXadTOAhEpqYkGv0w2Hfevg6uAHWDRwIbFwFHM9AwT//im8ihmDdA23xf/63YmX/och+Jw04ewA4dQDHJ0zApnvuw+mABiJUHC03MoIIgdeCLKsF2VZX9rWq7kvXC8eVXiiUclvd9Wpe4XV/pDtKS4SNgRAoSmkxSgmJLIRH6QEQNVVTKqbSNlAivLIbdp7AmAmvo0PEHwTwElLDJqcL4O0ylbaFyoB3nYjmQGU3jMkmpqxAr/jvZazdSauEf7fHxE3oPXkDej/7JZoPWSiA9/CZXAm8RHQnUFLkkJ4FBxVfmnVlKW+/swhOEYOXrl8CroJcgi9B1wu77veC9/f/vge8wHvDgJeRGpSlwQu8VwZdNZitbgPvRT+ziKZQZLUIRZbAe85sx+7gAKxs8Wvk/XMxcHY3it/4I94ODsXyoFuw3b8J9tkb4ZA1BKf8Q3HK4osLBhMuWAz4ya7hRICGTD3BAkGMwMswYUcDTSKOKhU6lYK4rgNvqa5M0sd73t8f3ze5CyUvvQ7sOwSsWYtFDzXFx02aID2wAXZb6+FI0G04bA/GcbsmIl0U+YWgxBqKYmMASvRIBagEeF2gW13AuhKAuZbXpMIrk0e4x3Fm2DxfFGoBOGpriB9/dT8OTx4HnD8AbP4eW8aOxgf2YOwP/RX2+d+LZb73Yv3AEShd/R1wYg/w3ZdY81AzHLGHSMXcqL90cSCcmS9kJpHgQsTmZSrociX9Wn9X93qo9X5OwFsqM5HpsCtkXKbhJU0qHtSBl4PaxCCwshIUlxWiGAUohAPp24/gsbg30CXqVQwY9wX6TCTwbkCn+DXoFL+qHHj7TKDCu1mP0qCAdznCpqxFWPwa9Jq8SlgY+k5ch94TNqFH3Hb0mrwFYU99gYcGLUTc/Hdx8FQ2SspKUeIoQIlTeXWpSjukUZftLnMKcKcdw1maj9IyDrtj7AYddpVnWZ3f/55xvEfw9kB5D1w18MakHkB5WLLUbVBRDepaffUeXgKvXtgvIkrDz1XhJWQq4JR1xLy2iEhsrZeWoi5fZ25bRFZVPPZTvk2159c14NUTTuge3lx/q7A00MPI4P5FxgCctgRiT736WNP+YTj/+zZwageK//Ia/nbLvUi/42FsC7wTh4Nuxxm/hkINzuVgKj87zts0HAjQcCxEQ45VA6wyMgRB4rSfSVgaOLpeAS9hmAODVBzeyuCiNiu8PB96d5kggcB7wmLCiga3Ai+/ARw4CqxYjY86dcMnt96NHY3vxeGAW3A8sDFOBQTirE1Dgb8BDhsjNAQJyCvRrGJgoQReFZpMgZJnfa2Adul2NQW8/ALAwWn06zJqQpFmEqVQs4nQeIf9muD7pk2BdxcDZ3YDX36Irx+4H5tDGuOEbz2c9W+CnQH34JMm9+HAoj8DP+0CDu3Fps6dcSa0CYo1m0hKwfuxyEwPr0VAr3vmtcruyaubp67Lpf1acT9qvZ8X8NLTqgqBl0koZCYyKYCKKA4cIKZHPSiBAyUoQgkIvaVYsXEfRo9/DZ0iXkH/Z74U6my3+I3oPGUNOk1Z6QG8GyXwMhJDvFzGAW5hk9eC0RmoDotEFBM2oeeErSJGb/dxy/DQ0Jd04M2Sw8zKeGR6cgGn0ymBt5zQSbLKukAvMoGXWdYIvDwR3bfsBd5yCPP+uHk9UCXwjnprP0Yk7cDYJVsrpBYm8Ean7hGgO8oLvG5RGqoBvB+8gkELh2Dw/A43LA5v9Ny2kKU1oue6igLPiLktIOLczm+FyPmtEJHYEuHzmoua00MTWoOwG/ViJwxNbIs+M5ti4LyHEfliG4TPa4HwOa0RndgR0QkdMCyxEwa+0AJR8zoien4nDJmtg3C14bYiZMs21n7g5YOVn2bdP8/yN0GTcXIJpIRdJkgosjbEcVMINvs3wJq2bYGv/gGcPYxTi5fi1XvbIeXeDvjHPW3x+e2/xpY778aRoEBk+/uiMNiOC3YTjvhpOBdiQ7GPBRxgxU/GeWb1qZifj00inmyZwQyCm/gUf5kwWrUdeMUneH6W1zScsVuwIjgUWJQM7NyP4tWb8WbnQVjym3b48N7m+Pzu+/FpvfrY2LgRTtcLQq6PD5xWP8Dsh1ImoFD9pYM0zAwl5wlKctrzeleEqyvBV8XlNQW89Jif9PPFSX+7gF4CL8GXUUSyrCHYHdgYy1o1B5b9Gzi3G3lL/4DltzTE4aAAHPU34Fg9K7aH2PFJo1AcSJwFZGwDjh7Btq7dxWBAmAPgMNJvLl/2HMxsZ/FBntEgvddCSa/YF1ffj+r6XGk/ar2fE/DyQa/IT9VVP/xlFIdSYYFgNAci5JrtRxH79KvoGPE6Bo77Rii8PeM3oEv8KmFXYDxeZlrjoDXG4KUCLEKP6eovl4fFrxZZ1mhhYIxeEYN3whoMmLQaXZ75Lx4Y8jImJL6FAycuCpW5tJRtKIWj1CmAl9MsajAePbss9O8S4FWp+sy8S7w9cHN6wAu8N9TSUIXCm5IuE0/UAPDKpA6E3BYCcgm8EYn8LadjX+qAIXNaYVBCK0QubIeIF9tgyIIWGDq/BaIWtMbwlzoicm57DHmhJYYxYcactoia1wERczsgfG47qfx6gVfArntYJwW8TApRQJVVs6JY80OhpQFOWRtiV0BjbGzVFvjnh8DZk8CKDdj7UjL2v5SEPXP+iL2zZ+DU1HHY2fYh7GkYhCNWI84aNeDWxjhj1pBJKLNakWsyI8dsFjUVNFfKVvVgVw/6yoGg1gMvocliEmmWT1o0rPb3B/74KrDnILB5HzJe+ysyEhfj+CtJOP/qX7AnbiIyIqOwrv7tOOLXAJlmPzitviJObKH+4gKGyyr/1K76r2J/1hXgPeHni+MBduRa9Bcoqr5mOy5YQrE1pAm+6tYZWP8tcC4DBYtfwvrQ+jhts+G0n4YTIRr2hVrwZX0/7IobB2xIB44exdYBg7E/tLEITZZnsYFfKAqNviDwFlttIqoIPeh8wZAvFJXfm9WDX3V9rrQPtZ66jurYMjQa1e4ck4azviZsCLFgz/ABMlJKYbYAOT6OZaII5UOVQuzNeUzLoxAoFVyyFsC77RhiniHwvoEBz34DWhJ6TklHl/iVLuCdtEYMQqN6KxJLxP8Ixt2lv7cbPbzxPwjg7RW/QWRaY4SG/nErMHDScnQd9wXuH+oGvBSgRUiyMtB/rNqjYFfVlc2/mX3lPZa3ByrrAS/w1nrgdam6VHiVsqvqqMQ2Yl7E3FZg4fyoRNYtEZ7QHFSAYxe2Fcv6vXA/Bs56AJELmiNqIZXhFohZ0AZRCa0wdGYzxMyT20XPb10O0Oo4117XfoVXwa1M9SvDkSnvbL5JQ77JoAOVWVgaqJ4d8auHHfc/CCQlAfv2Aps3A2tWA1u3AIe937LLAAAgAElEQVT2AKd3o3TnChR8uBiHRw7BwZD6QGADlNr9RZphhjvL8jUh02xAtsmAHKNBgEShwSiUXz7Aqe66FMrKgcAFdlKhJrTXpji8VHZhZJQGDRetRuxo0ACYlQCkb0HxinRg0yFgfQaQcQQ48BNw4DCwfD3w4jvYcndH7DYHIM8vUAwELBAQZhQDt2DS7RKXgK/q08r7s3qQVnHbmlJ4aXch6Gb7WFFoMcu0v0aTGGyWbaqHLUG34Z+9uwG71gInM5D/p5exLegW5BhCRPrrXJtFJO9YHdIAu556Bli/ATh+GPuem4Q1996B/aF+OBHoizM2M5j2ucDqg0JxLBOKDQRe9mXFvrj66epeD7WeF3gJu1cDvE3D/4C4+W/j4MlMHXZlaDWqzApwWat/7vPcf6vl3trbAzXVA17grePAq0C0IvBKCI6c0xxRCS0QObsZIma3wKiXHsETr3fDiIXtMHTmQ2LZkOcfQnRCCwxb0BpRc5tjaMKDiF7QAuGJDyI88WFpl/iFK7yERAIus6rJEelyFDoBmPOYXphxeKkgMvQVA/qfsgVg/y134kzMGJydNA1rYqLwbeQQpD/5GE4l/RkF65cD5w8D5w+i9G9LcbB5a1wIaIRzmgaHzYy8YB+c8dVw0aIh26TpwCvVZCa8IMxUhF4XWBDQFVjUbuBlXGPG4aX1QEO21YRDofVwsd8gZD03E+seH4dvhozCxz0GY/Nzc4Bl3wFnzgFHTgI/ZuCnsZOwOiBUJAHJsweI0FkM6UY/MAfD8bqqfnK9OChwcl92fb9rCnilx1v6a/NtRpGGuYj3qdmOi6ZQpIfcivf6dQMObwZO70fBn17F3uB7UKw1RJnJHwUmX5y11cfakNux/+lJwNadwJGDyF38J6y49zbsbRSI4yF2nLGbcN5qFWCd72MRKYfV/enq32vtw+peD7Ve3QLeH7dLhbfD/0jhDXv2S1QXeN3htrLfNQU53uN6e0D1gBd4aznwKqCtqqYHN2peO1EiEtogktMJbYTvl8ptzJxWGDm/PWJntUb4tOaIeK4lIqe3QtTzbTFiTkeMmPcIome3xPD5bRCZ0Azhcx9C9IJmCE9sKqBXWiYuVZaras+l82u/wquAVym84mFulMDE0eiE4RKmWGUKXINBDBQ6azbjpF8IztzSFEdvewA7AhtgT70G2BV6C7b+qjmWdx2ME39+S3gicXg/Tj/7LNYEheBcgD8KfE0Cdk/5aCKxBTO55ZqZZU0CN4/nMBngNBkEtKnP76pme9Xv2g68YGQFjWlxGVPWID63H6nXCEf/34NYF9QIm33rYWdQYxz+1QPY2LI1tjz9hIwle2Q3HH9/B+tbPIDd/nac8fFFvtGKUpNFDIQrtRpQaq3oyyacqX67flBzAV5NAa84HyYvMZtExIpzfgbkWU3IsdpxyicUy+vfgr9G9QFObweyD6Ng0Ws4EPIrlGqN4DT6I9NowzG/xlgefCd2/34SsCMD+OkwMmfMwOZGTXA6MAAXA/xw0deMs1YLzvnI7IAcWFlsMkuveYWYx64+qX7/KpC90rZqPS/wXo3C6wm8FHKVmEuFV1kXqqrdwVdBh7f29kBN9YAXeH8hwBud2B4E3ojZrcqBN3pOO0S/0AaPJnbFyBkdMWx6B8xe+gS+2PkWNp79AquOfoTnUkaIdSJntBRKb+z8Voia3xzhcx9A1MJmXoVXhyAOUBOg6WYlIBwVE3I1DcVUe6kCM1mC2SRsCNkWX+QYQpGtBeOi0SwUyhyTL07YmmC57dfYM/I5lGzYBeTlwrk0FWubPYifgv2R42fBCYuG0zaZFjbPwsFxusJs5rEMAngJvYRbd8BV06qu7cBL2HUwKQT71aSJdLanzRZk+tfDBVsACnzsyLfYkOcTgB02Pyxr1hT45l/AuT3ArpXIeXosdjesh9M+figw2FBmtgpLA68XkzFwvwq++Lu839zmq+XXWtc48FoM5cCb42NCjs0Pp22h+KFBE3wQ0RM4sh7I2S+U271Bd8Kp1YfDaJdfKoLuwNeBd2DT758Fdu8Cjh/D1gEDyz287P88q1Wkwj5nsSCTnl6zD4pMdpGIRdpuXH189X2oQPZK+1Dr1T3gjRr3KpTCy0Fp7h7eHhy0NmmNPmhtvT5orfqWBk/glf5dl6WhKtBV873AW1No5z1uZT3gBd5aDryuUGIqpJisVRgxpfDGzO2ASEZVmN0W0QntRYmc2RbDZ3RB5OR2eDyhL/7y79nYdPJbZOMYcnASp7AXL38Yj5jpnRD5QjvEJLRH7LyOuk+4BaISK6qzl6q31VF+K+6j/HwYJm2eKxyaikARk9AaLLEJLRE9tyUGJbbBwAWtMXhec4ye+TCmTmiKb6f1wOEeTXEkwAiORL/6h+iVHp5uywk+Kg0ta6FYyYeq/GSsIU8HXgIZM4PRalBiNMFp8oHT5Icyux35gZqI18usbFnBjbHDdC82tIhE/n9XAWfPAf/4AGvC2mGtWcMFfxsu2M3ItpvFZ+hCqwaWIqaIpZpMH64H6BLW3IGtHNzcPJRqeW3y8DISBS0ITqrcRg1ZhDezCZkWK3L9fHGOme74EmC14XRwA3x3xz04PvN54PA2YE868Mpc7LitMS74h6LI4CvDkvGzPjOQUQmnrUGHW/aP6lv+vlH3VU0Br7je9HybDci3GpBjMyDbRuD1RZY5GFuCG+Fv3doAW78AsnciO+kl7AhsDIcWgmKTDYW2AFwIvAvf+N+KbdOnAPu2AD/tw9rOj+BoUGMUar56KD6LGLTGbHb5Bj8UGP1RYAxEkcEu+vv6+lGB7JWuh1qvbgHvqh3HUBnwdp0sB61dL/By0Fp1LA0KcFXtDrrqd2UA4p3n7YGb2QNe4K3jwEvwpaWBkBsxq40AXkKrmJ7RDmPn9sLslKfwxdZ3cMK5HRdwDKeKj+Bc6Slk4wxefv95xE7vhpFzuiFmziOInN1BbBuzoJ0Y8HZtkOsOwnUDeF0PbQ4WM4kR6IRahg/LJShZjEId5CArJwdHaRbA7CvVX4sZeQEEWZmG+GJgQ+wLbop1LQeh4OsVQOYZkcL1+4fuww5/X+GHpFpGkCOoEHSFIqnDroJZghShxrNwefm8Wg68PA8CutOmIY/nb/eFMyAUuUYbsi0+yPS3ID/UD7lmXxyzN8Dqe5qh7PVkOThw7zbgD/ORHhqIi36hIm6sGARnNokXh2JL3QZe3gd8Icy3mgT00pJDhTfXxxeFxhDsDWiMj9s+BKz+J5C7GxfffRVrG92C8/YGuGjxxUX/YBwJvBP/DroVu1+ZCxzaAGxfh70D+uGgX33kaXYUaCbxcsfwZCWaXSS0KNACkGsIEOmcZaSGK8Hq5ZYrkL3cOlym1qubwNsuUkZpUAovgZcRGAi8PXWFt2/c1Su8lQGvitJASwP/KaD1hF33ZVzH+8/bAzXdA17grePAS9gV3l03dTdqTjsBv0Ofb4NnX43CqsP/xHnswcHcTfj39x9h3e61yCrLRg6ysPDt6Yia3AXRMztj2NyuiJ7TGVFzOoo4wozW4AVeXeFVEKnJwWIyPq4JF4waOFCHA9ZgtMApgvsbAXsIMrmuTxAKzTZwxLuIuGC247h/E3xqaYAdT4wDDmwD8o8j6/mp2H7H3ShocCtyNSsumKzIsfmgUAyI00CvsAJdBbMuCK8IA1yultVmSwPPQ4QSo7rNFMomI4otASgyhaLI1EBEEzhutOKE1Q9FIfciXQvFl3e2B/6xHDh6AfhhNdZ07YSDd92BLGsQyjQ7YOA1kul2hYfXQ+EVKr2eylj14fXWNaXw8lwu2IyicGAlLTCEX4YSKzKGiEQdq7s8gpwPFgGZu4A1n2BNWGdsbnIH9oU2wHbfIGxodC8+vu8hHP14CXBsPfDDl/iq6X041Og2nLP6IdNiFvt0mMxwaD4Sek0ByLP4IYcJVa47Fq8CWdc9Xfn1UOvVDeAlQDIs2aqdxxE57lV4Ai9DjXWf9sMNA14Vh5fcSuAVCSeYfqKsrEJxh17CjVpe06DjPb63B9gDXuCt48BLhZe2Bqq64TNbC3V26IxWoMUhNqEjpqeNxTd7/4pvdn2AxOTJGP1sLP5v2ScibWUucvDH92ciZmonRM3oKBReAi+TUDDqgwxvdr3QWzcUXn76VqDJQWsFehpVgqwEXgbbNwJWfxT4+OOY2YJjIaHICAjGzuBQbA8Nwd5bb8X2Brdi1S1344v2j+Bc2iIgay9waB1ODYvCweBGyDUFokjzQZbZhjybTfiBHXpEAXfltvKH/qVQUNuB12mSnmVGqjhpteJQQCj2htyK3fVvx55b7sLBX90jYvNua3Iv0h/ogG8GDAf2HAP2HgW+/BZf3HE7ttvtOK/5AJoNMEoPr1CNCbZ1HHgzdeCluqsyovEFrNgQgpO+t2Dl7b/G6TkzgOyD4j7cl/g8Uu++Df9ucgtW/uZ+fHDHr3B09kzg1Fbg8DocGP8k1t3XFLv8Q3HGHoBzevQHwjS/fBQZfUQUCIZDyzXJl8Pq3quVr6dA9tJ7u+L6aj0v8PaaXH0Pr1J4vcDrBca60ANe4K3lwHtFhXVuWwyd3Qox8ztg8MwWGL6gI8JntRQ1oXfE3C54+tUheCKxL8ZM7YOYJwfib5+9j+zSXOQiE6999AJinu+AyBmtET2nDWLmdpSZ1+a2RPQ8PYvbLzksmfLw6sBL6CTwUuEl9LLwN6x+KNTMyDbacMBswU/33QX8+UUg+TUU/2k+PhnaEwn33YYFD/8Kq6c9g+JlHwNHNwHH1yLv/T9i24P34ZDJF5ka9+Uv1LHzwhssky4Qtt0tDBUf9lXDQG0GXn6mZlgyZu86ajZhY71A5I9/DPjhU+Afi/DN7wYjrdXtWPJQI/xrYAecTH5RxDfGsV3A2tVI79kLm+64FSdCglFg8RPRHpTayv6E2VCuhLM/Oa8uKbzyfGTcaBFhxGySqYCNdji1esgyNsHeoP+HLZ37AutXAad2oezwBhz/7wd4PawdXm7eFBtmTwd2pQNnd6H4w2SkN2+DHQG34bi9Mc5a7DjvY0SeTYN6KaMKTxWZx6NPmm2o7r1a+XoKZK+0H7Ve3QTetlFvoN94PdPalHSRTIIKb8/4G2Np8AJvXcA97zl4gbeOAy8tDQReqrwso17qjGHzOyA2sT2GzGgOqr0DpzbDoIkPY/T0Xhg7OQafLf8/FKIQF3AKr340DTEvtEbUrGYiDi+zrjHiA2E3ep43SoMY0ESfrJvCy0FpjIWr4uEKO4PZFwUGKzJ9/bHLzwfbH7wLWLIQ+O7/gM3fAzt+BI7vAPamA/vWAT9tEopa/l//iE0RYdgQ7Ie8oFAgqB5KrDacZUzfAH+p8BKyNYMEXj1iQeVwcCkU1HbghcEHxWY/nLLasTE4EOefHA1sXwGc2woc+AbI+BKOr5cAK/8GHN8IHNsMpH+D44+PwerGjbEzwI4zvr4osdiFustBcOw7gi8Hw7n3I+GsTg1a48sCE02IlzMZnszJJB6aDaVaIAq0RjhpugOb69+PnSOfAA7uA84fQtmedcCWNcC2dGDDD8C+zcCGZdgQ0R/pAbfivP0unDXVw3mjH7J8jMj10QTgMuqFAF2ztOCwL73AW30IoT3A3TLgbmm4HPAyc1qfCem4Hg+vF3irf528a/58e8ALvLUceFX0AlV7Kr7R89sKn+3gWc3A38y8xnTBQ+e0wMAZD4GphUcsfASj5j6CsTN7IuqpPvjoqw9QiAJcxAm89MF4RL3QDNEJDyMysZlIJSwGwQng9Sq8Coj44K6sEJzKjPyca0S2puG8vz/2NwzG8iYB+Kbtb1D45wUo/jAN+O4TYM0yYPknwLf/Rslf/oC9o6Px3X13Y8Mt9XEiJBDHNE14gnN8zSKY/ymR2IKDgmR2NUIvPZEq4QSPqYpqp2dd24GXg8yKNQvKfENwxhSEU01+jeKI4XD8MQH4/u/A5n/Lsuoz4O0UFL0wG+nN2mFb4ztwMKgeTvhYkWmViTpKNanoguqugddMAa+rH9190p59ea3TSlXmvrkPTtNSoSwqeUYNpwPNwKAw/OSv4YyvQfiVqXCznSzXcmy+kGX6WHDBZkGeRb6glRH4DfTb+qLQEIh8223IDLkP2233YNfDYShd+Afg738D/vYe8NH7wF/fQdYL07Cqc2usaNgQZ4PvQoH5FuRZGyLfFoxcC+P6ashjJBGCLhOiEHT1ci3trriNUm7VtaqqVuvVPYU34tlXURnw9pwqFV4v8P58AczbspvbA17grePASwBmRIUhs5uLmpBL7+3gWQ+J9MCD5jbDwFkPI2Z2Gzw+rw9ixvXH+/95BznIxjkcQ+J7TyHy+QcRPe8hEX83IqEdIud2EOmGpaXhF+7h9YiEUOFhTGghNFkMKLPQ3mDEGZMJP/n7YnujYHzXJBif3H073rmtMd6+5zYsvrU+Xq8XgA/vug1f3X4rVofUx67AEOwzmpDj7y9G0Bc38BdJJ076aMgK8EEeLRN6OuFiPdxZZdBboV3lIMcwai5AILATRmpTWDK+TBDQYPJDvmZFrhaK4wG34z/1GiD59np44/6G+NO99ZDcqAH+Zm+A7Y0fwl77HTio+SLT1w/OkECU+dtQajaKhBMyioYEXhglJMmXBlP5y4NQJXUrS1X9ejXzaxJ4mQzirK8Psnzk/akgW3yloN/WFCTiRBf73IVDpiZY1eBOfHrr7fj0zrvwccPG+KxhE6xo3Bj77r0Te/38kO9TH7A1QY4WgGzNV9yfTIxy0UfGjHYYrXBqZvFFQh3ravrq0nUVyLru40vX4TK1Xt0B3iJ90FplwNs9/gdUBrx9J66D18N7cyHLe7SfTw94gbfWA29bkTVNKbyqVkpvxNwWYDSFoXObI2pBawyc9SDC5zXHkISHEb6gFQYntsCQ+a3FPkbMDEPEM/3xz+8+Rj6KcMrxE+a9+3sdeJuJuLvhCR0RmcBIDe3AY6njXHtduwetEYZkXF09la/yJOo2B4a2yqH9wKTB6WtBkdmKHAMBIwBnbME44VsPB+0hONqwCY41aoyjwfVwPKA+TthDcc63PvJsoSIqg4NxZQ0asgKNOBeg4byPAecNDCtlQoHRjEKDLPxEzXBoLAp8heKrK9CeMFCbgZfgWWJl9jo9WoMI9RaCLGs9MWhqa8NG2F6/PnYHNcThwLvxk/UOnPf9FfL87gD9zwznxqQgtJwUG2Us3zKLBg6EI4SKFMPi+sq+rKCW1wHg5T2SY7Ih02rHeUYJscmU2A6Gd9MLbL7I00w4rxlx0TcYh/yDcCC4Ho4G1MNJv3oiQcUZWyDO+lqEfcFhsYv7HZb6cBj8RLrmTLOGE34GnPGzotAUgGKGJTMShi3ib8fznry6aQWyXuB19/B6gffnA1nelvx8euBnALw7MCJ1B0ak7MHwVFV2YUTqLoxK2SHK6JQdGJ3MaVm4TBYu1+el7KmwjdqW6w13K65td4ljVpgu369rG8/l6niqPWw798/2l5fUbRiRtlmUUUl7MDolHY+//Q88/cErGLRwCAbP7yDCesWIxA3XCY1zrwy84QnNMXROM6HoMllE1IJWiJzXQiRrGPpiWwxObIPwOW0xYlYPDP19b7z/3/eQizxcxCkseO8ZaWnQB6gJ4J3XBcLL+zMAXrZ9cA0mnnABr24lcANeoZZaNQGq/JxLqCqhomqwolRjSlyGwQoAzAEoNFgFtMLIeX4oMxIMbOI3Q5mBKW9NGs4aNZFMgemFCy3Sf0mFVxUJuzKxBUFXwK7u6xXKpJuiS7C4duA14PQtAUCP9jjtKwc+wWiWoKgnveD5K3jhsd2P7zmt1ruamvvgiwShVyqz/LzviwLND2esATjFxAhMdGAIRoFWHwVaQ1HnaX6AzSZAl+q7Sv/MWMYcXMXrxH3DLFXd8j7ULSJX08bqrFuVwqs8rp6WhtM3yNLAFyJml2N2P9oaCLwFTF6iZ5kT/cL7xWpGkdWCTJMJFywW5FgZtsxHWElofXAabCKDIOwWFNCSYbAClkAUagRcCy5aDDjhZ8QZPxvyzAFgHN4sk58If8b7tTp9VPU61wO8PLbM0sdrzH4+62vCxmALMoYNAA5tBwqzRWgtPrJFKNkyOmdl/NnSmxxa9koe3r7jv0GvSTLTGsOSUeHtNXkVLmdp6DFlBQQcT16HXvEb0HPyehG3t3/cCgyctBIqSkNc4ls4cDxT9EGZE3CUOlHmDUv28yE5b0uq1QM1DLw7MCp1myjDUzMQk7ZPL4TXHRidshWPJm/GY0lb8diSHXg0aRdGJ+/BKMJxGkF5G8Yu2Ymxi3djZJKcPyZ1O8ZyftImjEzajGGp2xGTtgexSzMwbOlujEzbidGpLBliGwGpbqCrAFdBsppW9c8OeKuMkCCVU9oXqPKy8DdTAxN2o+Y2R0RiS/RPaInwBR1F9IVhz4chelwvfPzNu8jCaWThGOa/+zSiX2iNmHlthP93EEObze8iBsBR1ZXhyeQy/g5P4HElxHNa/a6qZrY0lkuWz3VlWWNoNaVcqzpG347AS4WaCvbomc1FprVlz1Un01p1H5QuaKvqoSvgTX9wqnUU0NEeQIASsKsnSXB5bemXJPwSrGQpI9xqZpRqFlE4LYt8MBOCGCmAXkjp3TWg2OAqAnIJnExx7AGZqm1V1VyfkMrC31yPNWGS89Tn/gKzhuP0kjbxB3q0E55SpksmlDOphnoJcAdedcyrbZParrLavb3ivOlfNlpRZLIh3+qHXB9ClR0FFg7u80eh0S4KlxfrySUKGELOaBZFquP0Q0uFlyDq3l6l8LrXlbXraubxhUMdQ/UXp9XgOFo2cs0aTgWZgcFhOByg4YxdDjBjO67Hw8vtS8wME2ZDvsWKfItZvEQxJi/jO7OU+MjkKUVWs1hOC02+iX3meskSfWhmKD5NvoRZzCLkWA7bbjGJlNmZPibhF84z+6DA5ItckxX5RnONKbyuvzcj6Fvm/ZNvMuCirxlbgq3YN6w/cHArkJ8lgJdwy/izcANeJ6dv4r/KgHfNjuMi0xo9vH0mfIMe8evQbaqM0tBryg/oM2kV+sS5Bq0JII7/UdgaekxegZ5TVkDUOvD2mLxepCLupwNv2DNf4MGhr2DSvHdw8FgmUAZI4C3xAu9NvPbeQ92YHqhx4CXUEnoV8EanHUBMWoaA2dGpm92AdxceXZKB0ckZUg1O24HhadswOmknxibvxai0AxiZuhejk7ZizJKNeDRlIx5dukNAblTaHkSn7hG/R6dtx6jkrRiZvFusX9uBV6XivQQY5ymrQEsBu4TKmHmtRHQFwq4oia0ELA6mUivSDHfFsLhe+Nd37yBXAO9hzH/7GUROkyHJOOCNSnB0Yke5z/mEacbjdQGvO+S6/760fRKKrxd4h8xTwNuyUuDlA1r69zzB9cYBb2WAoyBGqXcEXzUQiWBDoGFsUsY+FQN5dDWWD16n0aUsiqxshFfdOiHBTiq3BF21X1Wr46q6srZVNY/bsG0sanvW7sBLQCPYnPDTcOYWO9C9IvBy4Jd7W7m9+/HUft3nXc9vtT/2i7BxGK0oNllRbPZFkcUXBSYf5FtsKDRbUWCyiIgEhHOm02WILC4ngBUafVBktEg7iB7xgtfOs208N1nkNfJcfrXTAnjdoJfb85yuBniv9phqffZZscks+kb1D/9eWAiqeYRcH1kKWFulz5dgqIrI1GYylGdsE4krrNzeiByzBF6xL32/vAaq8AWDfanac211df+O1XryxfJywLs1yA14C6TCK4CXiq4CXgJwDSu8jjJg7fbjiHlGDlrrFXcp8PadtAr9Jkjg7TNxvVSAdeDt6QG8PeM3oDLgfWiIBN7DHsDrhKM8sYRKMOEeRYKIoubfGFzx7sXbA9fXAz8j4N2D2NQMxKbuQ2waFV4qv1sxOmUbpKUhA6OSD2BE8gEMT+E6O0QZlrITI9L2YvjSA6Ielbwdo5O3YEzKVowh8L61D1Gp+xGTshcjUzNABZjAO1xsR7BW9oiKdW1ReK8EvErdVWAZldACEbNliDHaG8IXtMTgOXLQ2uiZYYgZ1x0ffZWKfJxBLk7ilfenYMQLj2BkYicZu3deO0TOaYnIhIcQk9hcgK8nzBKAOe9mAe/QRCq8FYH3UM+mOBJgFA/vmgReBaIKdl2ga3BBF9UxowQwQhgLR9CLefp8TqtlhAxRjFR2K4K0O7CKAV2VQFtVYEHQct9ewWRlwCsUXjfgpb2CloaaBt4Sg0UAL1VcoVwaraLmp3XpdTYKdZepnqnyEoxLTH4oNtnAbfmZXwCtB6i7+kyBk6ovhWLXuldepoBXqcncVl0H3iu8hvzUfirAVB6lgZYGQjvv62uN0MDjEHgLmdbaahaAq9TYbKtJDGLjQLZsmyw5PiYxaDLPHWb1lwZ1X7JNLLwXeH/mmg3i748A7R6TushsBguzr91s4FWgy7r8Wusvk5UqvDrwkm0F4LoB703mXak0l5aK0GQESS/wXh/8eLf+5fXAzwZ4aRWg2kqlV3h5dQWXQMp5I5IPYXjyT4hNOaRD8Q7ELN2G6Ld2ICptN6jiEpjHpO7E2JTtQumNTdqO6JT9iEo9iNjUAxiVnIExyTswJnmbsETEpkmvbmXQW1eAN3KeVHgJuqpEzpEKL60NkfNbCn/vyLntMfqFMIwc3wcff/02CpGJXJzCi29NQeTE9oh4rjWiZrfFsHkdhb1h+PyWAni5Ty/wXgo2ChYV8CqQ9ARehoOiGkY4EIBg0WHWLD+xuitpXFetL9Qzs0HYG9S+3Wt1/KuCr0qAl/t0B16CmbI0nL7VLiwNp22cZ0SpABiXwkvrBtvh3oZraZf79p6/1f6U8k2Vl0otgVdYG4xK2SXw6rGRLWYo4OUgq1KzXVghJAC5VMfKYVKBrqornp9n+6407Q68SlHmOalrWRnwMiyZJ/Cq/bgfj6YdqUwAACAASURBVPPcpz1/8xiE0xwfsygE3Yqwa0am1SAGoxF8FfRmW43Issj7Vb2cCXuNeIHQoVd/QRPWBxOviSxUlFlEqmGT6ZL7w7ONV56u7nVwV+Yl7CrglV8H5N/bBbuHpaEgm05VfskXwCsT+jrEtBd4vQrvLw8Za/cZ/wyAlwruNjEoTQ4Ek4PPCJwEUtobotMOISbliCixKT8J4JXe3C2IeGcbIt7egeilcsAbYfbRJLlPgnN40j5Eph3BiJQjGLVkr/AC/zZ1O0YSlIWVoqKyq+C39gOvtAwoDy8hl0VZG4TSO+dh4eeNndcKI2d3ROzkRzBq/AD886v3kFt2EVmlZ/Dy2zMwanpfxDzfFTFzOov0wrFzO2D4/NaImPOg2B+VXKXmKnuDJwRXNa2U50uWV9PDS0vD5RRepi+VqqMcoEWokIWJGmSyBtc8taz6tQKNqvahwEXV6lM1a3pxCbCEBwKs/ASsQ60OwO4gLMBYB2R34BUQrVsR1HFU7dku1d7Kaq5Lxdjdw8r9FDEurJuHN88iPbwKeE95AC/7VfmLuU/3Y6n2qHlq+lprBbyEaxaGZiNcUdFVhZExWJhgQSQDsZhRajHBaTaj1GxDqZEDr1xeavcvAqqdrppA7yrX2m61ndqvmibgXQ54j9BKogMv20Eg5vpqe9Uf7vPUMs+aL2P05lK1dS8KbFlnifvTKECYSrBSg3PEC5m0RAgricEkLSU6zMoXDxMK+YXCKO8H3hPCvmMyCdsOrTuebbr66Yp/x6o/Va2AWdpQ+P8CF+wSeF0vShJ4z/uahIc3Yzg9vFvEoDUCL9Vdena9wOvy8HotDbUb/n6Jra9h4JWRGGhZGJO8q7xQ7SVwcrBZ5NIDiEz7CZGpxxCV8hOGJR/EcPpvUzZheFo6ot/eiGHvbcOot3cK5faxNzfh0UUbhMo79t2DGJJyEBFpxzAi5ShGL96P3y7aicdTtmPE0u2ISt0iIyxUYmuoS8BLlVcBL328sYkMQ9YS4bMexvAF7THnwzFI/X420r5YiLf+/Ro2ZKxGEfKQj2z8Z91HSPv6VSR/uxAL/xmHMQv7IGJGO8QktJWeYD2xhTvwXgKvVQ6sk+1gWy7Z5hqBN35CU3DQmrI0/NyAVwEla6ZWVZ9+FdhS0eW8cmXX6PJLEo7d1+c63Af35akk/y+AV4CvgZDuBrzd28IdeBnKi8CrBtIRYBR88Ldqp5p/9YDjgjuxDx32FPCWaDIJhwjTpkOvBF6zVBlN0srgNBuFf7rU6KPDLgcJUt2VMOsCJVf75Xm4YJfrXnf79f5R+xHHqErhHdi1PPEEFd4bAby0dVDlVTYE3l+qcB4HqIlBam62BNoT1MC1cth1C98mQdIkXj7UfaDA0rNW533t9ZWBV7wElHvgLwVeBeI5Jg0E3s1BFngCL+MyFJcSfItRRu8qAOdNJgbPQWteS8NNvgDew9X6Hqhx4KWiSsAl8D6axEgMjM4gQ31FL81AeNpBhKcdFrAbk3xQRmNI2oyxST9iTPJKjFr6I0a/tQ6PJ23Ek4s24Ok31uGpRelisNvwt/Yg4p2jGJJ2FNFLjmBs0mE8sXgXxizajNiULYh9a3udB14FkoxuEJXQStgRYhNl1IPwWS0xKTUSn+1NwnFsRiYO4gKOIrPsjIDdiziLHJxFJo7hOHbi26MfYWLSKMTO7YahL7THoOktELugoxi0xugMAnp1UI1IaCOysjHCQoXiAb83WuEl8H59BeAV0KUrgtf+oK0IXp77UWDnWbsDr4ReqYAVCAB2gSJBQQCmGzwKdZKj43XVjLXn/jyPp6Y92+c5TdDiPLU/tdxd4VXAm2vVcDRAw0lhaWiLk75SqaZidzXAq45xY2qp8CrgpdKrQrWVf04XCTqk2sjzEpYLAbmEXeknVcCrIN2zdrW1Imi55l/+vvBcT+1fzVfXQX0JIKwRxE4G0sPbFYf8NaiwZHyx8FR4uR+l8qp9Xq4W/aAPolTXXtYulV5Cq3yRYL+qwr4m3Do1Y4WilFPXS8j1vxhUdQ7qGFUtV31BhVfCtlSiCeqySNWZ0KuAl2HJ9gzrJxXegiw4y0pRUiaB11la5AVeEZasBF6Ft9bz3y/uBGoceGk7oHeXwMvQY48lbRM+WyqsAniX7hfQSy/uiCW7MXbxZjy+aA1+t3g5nlj8DZ5I+R6Pv/kdnvzTcoz/8ypMW7wWk5PS8diSNRj6l3WI/fAkBqUdQcSbh/Bo0lH8bnEGRr2+HtGLN2LEu7RB1G1LAyGUNgNCLkOLqUIAjpjdCiPnh+Gl/5uEpd+/jKXLXsG7Xy/C258vwjufJ+G9ZSl4a9mbePv7N5D03ct4+ZPnEJvQE8Pm90Ls3O4YML0Nhr/Y5RLgJezeTOCVYcnkoLXLAa96KAoIvEnAS/+rKjxuRaiQ0MWHNtdhLW0WslbwqOap5eohL7bz2KcCXM9anXtVtQIt1T61XnWBlwq1J/ASkLkfd6hTfaD2f+Nq2WfF4rq6oIxwRrBhzfawEOBEO0RyCQMcot+Vx1MquKpdqu2qfzif26prota73lpdL7UfXgdaXtj/BLHjBN7BEnhP2eVXgMsBr9rP1dS839wL96+sAJ5Qq6apivM3+1D1I6fd71F5317di0B1262OU9X6CniVsuwCXQm84l7Qvd3sZxWH1xN4qe6WOEohgZchubwKrxd4f3G8WOtPuEaBl7DLgWYclCYU3sXb8diiLRizeBsYfUEALyM3vHVAqMC0Ivx+8VrEJa/C6AUfoeXwF/DrgRPRdOBk3Bc2AY+EJ2BEXBqeTPw34t5JR/ifVyLi3X0YvPQwYlKOYWzyUfx20R48tmibiPPLSA91HXiVwusOuvxNAB7GBBgvtEX4cx0RPqUThk7sgoi4boic2F2UiLiuGDyxM/rFtcXgae0RNbsLhs7qhPA5XRAzrwei5oYhdkEnoewydbGA63ntBOwyfBmTU8hUxO1dKu8NVngZUm0ww6sltBBhyabEPYBl03viQK+mOOwvR4krT2b5w49+VxUGrArvK0GjOkXt07OuzrZcRwGFqmVb1UCcijXX4XK1LuvqHket59lONa28o+7KIpc5TVJFZgg1Qhj3Q4X3WKCu8HZrLRRewgL7VEGSUJ/52d1t0Bp/c3seQx1Xtetaa7UfVZdfV3V9K6vVtRXLXJ+43ftV7c+9Vm1k+8uPo/Z1nbWMraz3j7K7mGQWuGyGgQuxAAMeweEgI47bNREijC9JvD4s5W1zs7cQotX8ymp1bsq+UbGueO+54kGruNC0VMiiLAwVa6Wo6vV19k9l7Rfz1PXV96/OybNW14vAKwfMyYFz9HGLrykmI7KY1IWD1urZsDu2r1R4cy+g2FGCImcpikuccDgLhY/XC7xehbfW098v8ARqHHg5KE1EV6ClYfF2/Hbxdoxdsh3DUnYjOnU3hr2zT8Dv6DfXY0LaJox/43sMmbQY9VpHQ7u1I7T6HaDZW0GztoTm3wH2Rt3QbshUPP7qf/DE2+mIfmcPBi09gOiUwxidfBiPLtqDsYtkhjYR0qyOK7wKeGlnUEkbWEsAbofYhI6ITeiC2FlhiJnZDbEvhCF2RhfEzuiMYTM7IWZWR0TObI+oOe31+LvMEtcREQldEZ7QCTHzHxGgO3QOk1u0QpQOvAp0VU34FdaGGwi89CaHL2yP8Jf+P3vnAV5Fmf3huS256XSlWRYLovSu7iogolITkkAgFBuKDbDurgWSILpucW2QCpZ1d3X37xbXXV1dFRu9h4Tee2/p5f0/55v75d5c0iBIIBme5zzf3Jlv2pkJ885vzndOb4Yndif+uS5Me+w6/v1MfwW828MdKgG++ZnaVBpFCVJqmkfh1cra2baVKUs13Z5XKSyv7prxoVpt9Lan9/fGxdZkn5Udr1Yw/RXeaoG3f09EcRSFVwOvHKOEaEgIhuxPq6Ta97IPfRw1Oeaq+ujt6FYrfpW2fkDo38/r34qPUfvH/Mx/eh7kqo61omUCbQLQArxa0ZV+uQ6P2QyOBxrsCLfDsJ+xUUJJQs0ct3IMviBY0farm6f9dnpr3o/6PtSKrn8rYQ0Vmw4x8bT+fj9Xv/3+jk8/D/M6ll1nXXbbM7iuxCW5l+0Uupxq0KgA7/ImgayNHQSblkPOUVVVrCKF18rSYGVpaIDMeFGfcp0Dr6i4sanZjJuTxcTZmdw7W2J51yrgjZbBa3MlxncZD8xZwFOpPxD9eDJteo3CcF1N8FUDGTThRWInv8HYh96i+y2TMYyrMQKu5frh05jy/hJGz13F8LS1RHty+EpVNtmPxA2PSpFqbfU7pEGAV8XWTu/ByOkSx9uTmIReqpVpNW96b2JeuJHo5/sy8vmeRD3XhZEvdCJ6ekdVoCImUWC2CyOTOquyxJGJXRk6vReRM/oQO7NvBcArKq8Abl9PK9O1B16Bdg3uUmlNgHdwQneGSGnhpO6Mf6Ebjz9+A//55W1suv16toTbKwReeSjqB2BlD8jzMV/BiH5g+7UCXnKM/q0+bt3W9jg1jFYGvAJUStn1UXhl0NruMAOVpaEK4BWVV45P70Om5Zx9gbe2x1/b9bUfVVsNhHlh1xuaIudztscg6/oDr8Cv7EcDr0DwCbdNVVgjsh8bQs2cvKJGStESWV/UTN9jMENhys/zXa6n9bWQ/cl0Rabvv3J+8okrLwN/v/u3rH8l2/Xdpz6es2n1fqpbV/fTIS5a6RXYlXNQwOsw2O+2s6xxAGuiB8KGpeCJ4S0qPj2kwQJeC3gvavprgAd/wQBvvJQOnp3JPclZTEhZx+jUbKJTMhmbvpqJyQuYmvodU3//b9rfeg9GRGdCrxlE9NS3ePr1//LC7G945lf/If7h2XS+eQJG0LUEXtWfuJc+YELGUkamriY6OZu4lPWqDLEA9fh0M41ZfQdeid8V4JV4XX/gjXpBilB0VcpvbEJfYhN7q1y9kQnXE53YnugXr2PY9GuISryBEQntlcXM6khk0g0MkzRnSX2JmdlLbb+spLAnjKE88Pb1Aq8MYvNRec9k0FplwHtXUjeGJHRl7PNdmDrtegW8Gwd2YHOY7YIGXnlIy4NYQ4N68FYGDp75vn1luroHfXXLNYzWBHgFiASuNPDubxMCFQCvhAbUVOGt7vh+/OU1VyA1pOlW/FHb4/MHXv2CoTJ4qBLSpsK7LdyGAO+mcKlyZ+O4AK9P+eizOQ45fjkXnelDprWVwW8l950eOKnvRw2UutWgLMel/VS2TR8IPpvj9l3Hu7+qr4Xup9TosgFrEtttDg6VbBUSlqOBVym8W6S0sKnwKuAtLKa4KM+stiYlds8zMFhZGs6zw63d1TsP1DHwmqnHYtOy8ALvOsanrldhDjGpa7nnnSzGv/EVj735Gfc+P5eAtj/FcHeg/8SZPJ0yn8mvfqkGqT0z5wee/M2njH3sVYzQ9hhh13HThAQemrcEKV88OmWNKiccP8ccFBev8vZWrO4KBNeXtGQxSR5l1aPwirqrFV6pmBY1vZMqNyzgKiYqbmRSB0bONIFXYDfmxa5EJ9xAVFIHYl+6QS2X6mxS5c1UkM1wBgWyNQZeU6GtKfBq2NWtVnhH/OpGhrzUkztndCbmlzfw6GPt+eQXAxDg3RRqnFZprSy2T8fCeiCubP4Z/i4f91h1on//vrJPgRadFklaHWtYWevbV6bP9rj1er7HpI7HLxZSAZgnVlSARfqo0sJhBhp4JUuDDmnQcbACEqJASn+9D7193xhemXcuTe+rJq3vfuU8KzL/PvJb+vnOr+20bE98ov0iftYgKvMkhndXIycM/RnbGztVzLT4WyDOf98aBv3nV/Tb3K+nGIfOkatjYnXr5xd9nDoEQ35X5A/xv+xTx5zrVsWd6237AHtFx1ejeZX8HZddf7snk4VPlgaJNdaD1+QeVdDuAV4pPLG6aRAbRt8FW1dDzjFV2UyAt6iohJLi/DLgPd/EawFvveMv64TOswfqHngzJHwhEwFRUXgnzsn2AO8GBITv/0M241/7gqmvf8Lg+xIxgq/DCOnIPUl/ZUrqAu6ds4jxbyzgkdSlPJH8HQ9Mfwd3m94YER1o0zeOJ95ezL1py1WqM1XNbc5yxiavUIPWGkIMr8TUSuyshC/ocAYv9HZnxPM3KOiNnN5DDTYbkdCZ4QkdGJ54DSMSr1NhAwKyUqgiJrEzo168XoFv5IwuKjuDLl2swiZUCjIdviCqrg5p0Aqvb1jDuQHewTN7cses7tyR0IXR07sw7cmOfPb8IBXScLEAb0Vwq8HAv/XvWyMoqAIqy8DAAygCL74AIwAlg6NknrTSvxzwegatnQnw+m6/tsfvv77v+dRk2nd9fVy69V32Y0/LPvV+lc890CvwK8rj3sYBcOdN7GzsVHmPJUeuqKi1PS65nyp60Sq773wAXx+jtBp89THr4zjd5+UHv8l2y1kV96beZpWt3p7fdsqOwwd4zXMyBylq4JVBayp7h8OuVHPJw7uqiZt1sXfAphVlwFtSAsXFUn2iAEqLTHn3PEu8FvCeZzqydlfvPFD3wJu2GqmaNi55tQLeCbOziU8R2N2kCk/cPW8lE9/8gmeSP6PrHQ9gBFyN0bQPk3/3Xx5IWapCHiJnL+PedzJ5OH0Bk176kFadhyiF99LOw3ky5XsenLNEVV+TssNjk5czJmW5Ki0s+63vIQ06B65WdcvAd0YfTyiDhDR0U3AanXAzI5N6q8plUUldlYIbNf1GIp/tS/TzUmyiBzEzOhGd0EkNepNtq3CJBE954R8JeCXFmVZ2dRujShp3Y9CMrgx6sRsS1jAmoRuPP9WJT5+7nQ23myENp5wyqlweul6l0ZzWD2L/+ef3d7mHv354+2Rj0MqYbv37n35eZ3/8AhYaajRkVAa8e0INDrQOgX49VV5YKYIhacn08VWl8GpIOhfHro/zXG1LtiPbPBfbq+k29Dno/YrP9ed/icfNc9nYG+GEO25ia4jBwRAHhQGucve1b2iKTNd43z73nL520ur7zdcfvsfpO131vvTfWWVtzY+1ov2UHbMHeP37aL/Iy4GYziRRbHciJuWwfYFXFF4B3qzo22HdEjh+iMLCQqXuisJLkSi8FvAWlVhZGuodDTaAE6pT4B2Ttoa41NXEpa5UCqwMXIufs5G45M3Epm9gdEYWcSmLmPDG5/wi9XOu7BWJEXoNjtY/5b7ffMU9MvDsnW0MT1uvAPbuOd/z2Ov/pn3/cRiOy2ja4U6mvfkl97/xHRNTVjI+fS1jU1ealp7JmPSseg+8OifuacDrCW0Q2BWIlJhbgduoxD5EJfYiMlHm9SQ26VZGPn8TMTNuUmnMoqd3ZuSMzibwTu9BWcEJictVwGumIzMHqfmmJfOou9KnLIa3m4JtBdxl88wwCe+2eqnj0KCrWxN4ezDyVzcx4tc3MXRmdxXS8ODkdnz0+M1kDWzP5sYBHA3QhRq8A4FyZAS83WaaZzS8jpk8/615HBITKcd05q15XrU5bllXr3/SKeEJ5X0lCqOouqekn9PgqNtQ6bF2XhYGA3uxM8zOsUC7WbHLYSfPYSbxV5XhJLWWz/bF98r/OgtBLf2vB3fpVp/HRdM6TZ+Kf8XEv3Iu2k+nbAYngx3K3wy5mcwgg/2NAzgV5KLA5Sy7bhWdr2yrovmyfe/86u672t5fP+725W9GVSes5H6SWPIcu0GOtKqKoUOFOeW5nOQHuCgIDFDlvXMCnRwLcnCoiZuVrcJYM/oO2LAEDu3hVG4OuXlFFBQUUVwkCq+nxpql8CKqs6+VlJSoEBCZJ//0sgbAUtYpXgQeqDPgjUvNZGzKahN0U1cp8I1NXktM8lZGpe1gzNyNqlzwqJRF3JPyDb9I/4JGV92MEdCMsHY3MfHV7xmdtoPodw8zImM38enrufut73nwN3/nutvuxnD/hMCr+3HfK3/jvje/YUKahDGsVUUuVGnilCw1cO1iV3i98OgBRX9w9Pw2ldjuHkXWr1WFIiSzgplDt1xfNc87X0IbJIxBlSuuZF++sFpuusL+ZmiDPg+JC1aW2EuBt6zvq/DGTO+OWOx0c7Dd8OndGfxCV4Y834kJ07vzy2d68Pn0wWwc3IUNLdzsD/U8yILsHAi2sy/ErtI6Sbs/1KHmyfxDnuUNrT0cbEPbwRCb8on4RVTEw8EO9nt8dijMxYEQB9JKTti1l7pYf00jGNqX9c1d7AxzqKT98klYVDJJVSamfav9qrcnrSzTppefaavX1625vpSIlW37txfedRY/i1+Vz0OdHPLYgTAnYvJ7e4jB2kudMOEOFjc2WNPIYGeIoXwn10i2caZ+q11/f7/q3+ffv3Ld/e8lmXcoSPxjcNBtY3+QaVKhbn+wg33i72C5nwM4FOZmr9vGbrfBnkYuNjdx8NWlLr6L6QfblkPeMfJLS8ktgpN5heTm52Cqm2bhifPJvAKPvkBplRa+CAjLOsQLygN1DLyZKpRhfIoJvNGpWYxM2U5M2k7GpK9jTPpKYpMXMyl9IQnvLyLk8t4YrnAat+vNxNcWEZexjxHzjjA4ZSdxKdlMSl7ElDf+Q7ufjcUIvxZXu1u499d/Y9Kcb5iYvhKBbMn+IIUupLrbuLSLX+HVoFiTthzISuYGMU9VtLJWzz+tNUHXC7vlQbUm+6+4T/ntnCnwDpnejWFJPVUYxvgZ3Xnk0ev409QbWfizK1kabqhPwHsCDZWsX8Bha5jBZo/J9LZQQwGFLGuItjXYQGx7kNluDjEQ2yq/xUIMNkkf8ZnMDzHIjjD4oYXB4quCOTmoM0ubGmxsZGdnhJPdoQ52hTvZHmFnRyOH8qnAmZiv/8XvDd33yh8hhioXLCWDxbYFmybx52LycrEuwmBpSzs5Q7ux9MpAVrR0sL1FoOrrf8/q9f3n18ff+h7ybcvOU/6uQw319UFyGIvJPSm5uU1zsSPMxY4gO3vDXewJc7JRXiwaGXzfLpTFE++CzYspPrqXo/l5nMwv5lR+EfmFBZSoorpQKArmeXycW8B7Hp1t7apeeqDOgVdnTRiTtkoNUotO3YaYDFgblbyc2OSl3J+2mOfmLaDx1bdiOMJpdnVvJv7ua+JStjEkeSdDU7cxce56HkpdzIOv/I12N4/BCGyHu10/Hvrtx0xO/o570iRTg1Rwy2J0hqQ9E/jNvOhDGiqGyMrV3tOg9wyB11R2NaRKW/m+arZMb8vcTkXAKypvWSiDR+EdNcPc910v9WbQy70YkNSZ4c9dz31Tr+EfSXewb9LtFPTvAkNuhbtuhaG3wLBbYcQAGN7ftGH9vNN6XkNrR/QDbZH9IMpj4iexqNsgsj+MvA1G9DfnRfeHsf1h8lCYMQmG94URt8LwATCkn2nib1lP+3OYZ1q24TtfL2+orfg40sfK/C6+H2DetyN+BrE3Q8J9cPdAGDMAYgaqmF6vf/uB3M/+1hD8KveUPk+5D32tnD9u9fhH/j/oD3f1g8EDYdCtcGsf6N8bRtwCowewbnIM+Z9/QOH+HRw9eYoTOYXk5BZSUFBAaWkxEtRQZAFvWciCDl3wVaCFmPT8eklP1klddB6oc+Adl5zJeEkZlr6aUelriU7bTEz6FmLTMolNWU582iruTV7MlNe+5KreozCcjWl+VW/u++3njJ2zluHJm4hJ38Q9czOZ9NZ87k/6E10GPYDh+AkR197JU7O/4tGUhUycs4wxyatUXHBshuxnFfWhtHDNoNILpfUNeAcmduO2l3tyx8s9iJvVk4efuoEPf3ELiwdczeJQgx2hns/moTYOhtnVZ2L5VCyfkMX2hjVs2x0ieV0N9nraHWEGYpLrVWxXqM1UacPs7Ag2lVqp9rWomcHynwSTO6AjKxsbbG/sYn9EgAoh2RvmVMra7gin8q+vr/eEO9DW0H0v5699LH4WFVzKBouJUilqrfh8R7MAlrYwKBzek++bG2S3CmBbI3PdfeGmjxuqL0+/l2zsDfOa+Ef5KMKp7jsJvTHNxe5gJ7sMgwMuCX2wqxCcLREGiy8LZtno2yF7ARzdz6mcPHJzisjNKaQgL5/SkiKl7J5PdVfDoy9QWiENFx1vWQdcxx64IIB3QmoW49LNbA3RaeuJSd+gYHR02komZGRxX8oKHn71K7rdPhnD2YLQljeowWiTRBVOziR+7jrGv/E9D77+BU/+/hNuuPVuVW2tVZcYXpi3mMeSFzPhzcUqZnjs2+uJmbuWyNQVjJqbqfLtVhTHe7Hk4T1T4JX+p0HvaeELfjG+nuXmvrQiq1svTJ/NsXgV46oVXimHLCqvit+d0QNReGXeiFl9GDqrtyo8IaWFH5/Sgf/8XPLw3sDmcCdHAuzlBqfl6sFqnlYGsjRkkwwAYvmeNifAhpikvRLLD3CS67SXtTJPBvdsjbCzu20Y9OvBrhAZtOYg1+kwfe20q7y8MmhNDRQq52vvgCxzYFbD9r/4V/vatxXfKbMbZf4WVXd1oKHA7YhLBrnZlTXk+/f0c/e/v/RvfZ/ZOWW3ccrm4ITdQVFIKLnuAI657CpmfWe4wbKWblbEDoSNS+HUMTNDQz4U55VQUlAIxaLtnv9/VkjD+fe5tcf65YG6B945WQjwipIrgBudnkXs3HWMzsgkLn2VGlh2X1omT8xZwo0jfoER0BojqA0PvPR/PJq2lLFv/MD9GSu5742veGL2lzz1239gb9EbI6wrnW5/hOlvL+PR5CWMe3NpGfBGZ2QyPG0Fo942C0w0NODVYFpT8NX9Lzzg7cYwgd4Xe6rQiruf78bTU67ni6cHsH1gJ/Y0cqtR2NidYLeDzQY2h9fUfFnWcO20NE5l6Z28qakkNZakfzJL1toUZO0JtXOoVRj078uRICf5npRkks5K/KnTRZ3mW4cdxOR6KGu4vjf9ZKPEkzJL5dWV3Lo+v6XPCcNQXyOIHqRirWUgllQGk37l/Ot7b8t0g7yv9X1ltt77W+f/IZaD4QAAIABJREFUFb+JuSgyXJwyJIuEvJjZOWg32OY0WNrcRXbcXbAjE3JPmEkZJIZBmZRYKzm/wbse5rCAt37Bl3U2598DFwTwjk9ZyzjJ1JC2gpj0TKXAjsowAXhc2jruS13H1DnLiZz8FiHNu2A4WnNz3LM8+OqnTHr9Kya/+RWPv/U/nnz9U4bd/xKG/WpcrW8l8tG3eGLOAh5KWcaEOWa1tVEZ64jJyCYyfQ2x87IapMJbHmBrq9DWdv3ySnFlMbyi5opJLmAxU+E1B6wNn9VTpVGb8GwXnnikPZ89cStb+7VnR6hTqTmS11RM5+Q029qX5dXbvZhbLxCY+Wf98/CqwgQqf6ldFZ6Q6l6iPO4Os7O/tQBvb5UlQNRGgVz1QuFwlVWM80KceQ10ftmL2Wfn9th1flizyp5+UdAFIeT3SZuhQksYeivbw2zsc5tptmSZPhZ5GanI9PL625r+03l2/c+z1FN4otTmUHmidf5d3Qr04gqk1B1ErtvF/iCD1Ze4UaWFNy6HnBNmsK7AbonHBHjPc/yu2p+VpeH8E5K1x3rlgToH3vjZWYxPzkZlalDAu5qYuWuIlQwNkjt37ibuTd3Ag68v5+FXPqPVdXdghF1LWIe7GJfwPr9Im88DL33E07//hIcS/8g1P52gClM07xTLlN//jwde+5YHUldxtyjHyZnEpGQR44He2HTJ2FBxeeH6HNJQr4B3Zi+GvNiDoTO6Mu65Ljw19Qb+98vb2Xbb9WwLtXHCbj4QVVJ/u81UFx125AGo4MKvQpNvQv2GMO0PvLqClj53KSYhRSSkGpdUWtPAK7l3FfD266nSQsnneKUoepRFWUcS+ouPRfWV/cg2/YFa76fBtp7CD+JfMeUvm0OVZZaytwKxhSFuD/DeorJlSBqz44ZhgpoGOvvpVdcE9uq9Xz0lg9Xfsviysr9nn9LCusqaFKHAHkiRYSPPMPP0HnQbrGweYBaekNLCuSdN4JUoBgW9pVBSjKSZtWJ4i8oGpenBab4xxhrSZZn1z/LAheCBOgNeCSOQUr/jUtaVAe/YVFF4VzIyfQUxEnIgoPrOVuLeWMPE3y/n2XlruHPCTALb9MUIvZ5L+sTRL+557v/FXIbf/RLtesdjBHXCcfntDH0shV++t4p73lzAeCknnCKFJtYzOn2jslFpG4hNzVbpyRpqSMOFAb5nr/BK/O9dL5oD1qTSWtwLXZg6VcfwdmBLuJ3jAVJKVGDCLIea7zAQk9/nsuKXLzheTNO+VbkEFsQnosJqcJDfqrSwzQReUdJEzZVBVvtahcKA3irPqRSbEOCVz+wCH+JzMdmO9ofevuV3r0/kZUB8Ki8Syl8+vzUAS8EPGXglCq+k4JJ8suJvFT6iwnRsZT7Wvm4IrbpHPS8MVZ2v7qfvSbM1SwxLaIOEOAj8ip8lf+/SxnbWSUjD5pWmwquVXdUKvJUo2C05zxxnhTRcCMhkHcPF7IELAHizTwPe6IyVSuGVtGFj0zYS+9oaFdYwJXUFj77yL/qOfJrADndhRHTBcF6LEdoFw94eo0lfWvaMZ+Ck13jwta94MHmRys4wLnWNWXAibR1xqZvKbEzqBgt4a51WrO5CGgR4B8/syZ0v9WBIUhfGPu8dtLZpoAZeuwlxFvBWCEUW8PrCZ11MnznwSiEFC3g9L2VnALy+L2K6xHCVwLtlpRnDK2DrCd1FUa4JvFZaMkvhvZjhryEee50Cr6nyZjMuZa1KTSZxvBK7G5uhU4ZlE6egdAP3zttM/Ks/8FTaYh7+3b+JfeFdeox+jst7xnNF97G0u/FeekQ+y+jnP+DxjMU8/oe1CnYnpEvKsywFtlJ0YnSKlC7eSHzyFuJTNlnAm6gVVt3WFmDPdP3y+z3TGN7hiT0ZPlNKHHdh4rOdePrR9vzv6f7s6N9BpSSTeFOl8PioltZvr4LrD7yFDoMCu3e5qLESmiCKuCi9EiN5yiXp3rwKryiOovr6KrxaURdfa/VNpmV7WuG1roPpZ/GpqOraL/K7wGb6XHwkpZ33Rthh+M+UwitxplI2WPnWE9LQkH2p/VadD9S95/nyIGENAr2+wCt+lupsSxrbzUFrVQCvRDdYwGsBb0OExov5nOsceKX6mYQ2SC5eBbwymCxjjcrJK9XWRr6Zyfh5W5nwzma1/N7Xf2Bq2kIen7eQaWnz+XnyfH45Zz4J761kSvIPPJS8iAlvLmT0awuJfm0hEzLWqbLDY0XdTckmLmU9Y5PXK9iNT7EUXm9asPLgef7CHcrv90yBNzKxu6qyNnJ6F+75ZSd+8Uh7vnyqPzv7dVDpsgTENHBV1Fb3kKz3y43TgbTCkAY/4PUPafAHXgUXHnDWfhdf+s6v976t4UtWRcCrBvd5/Cef2hXwDrOAtzb3jALjsnCbMwNeJfL6hDRYwFuo6s3p2F3dWjG8FzMO1v9jvyCAd2zKasxMDWvUIDIpDCHV0CTmNmr2GsbM3agyK4x/bwNxry/intkLmfjWfB7KWMgDs7/h3te/5tF5K5n41iLGpawg3hPCMPHtzQjoisWnZpe3lHXEp5glhht2DK8GTv+2EqU2oRdS+azMah0Sofdr7u9MgXdYUneGvdiDEQldmOCn8Ep+WH/g9X9gahhrqK2vwis+EP8IgGl/yG8ZOFXWGpL/1SxWcbBlsJmlwW1+YjcVXnNd7We9Hd1WNl8vb4it+FtMfCPnL9dE/5Z5ArwHwu0w9GeqXK4MrBKFV/qaqfYaZgzvmd4rpn/Lpycr9YnhPROFV0XyWjG81qC1+s+I9eoM6xx4R6etJi51NVJxTUIbJDvCKJU9YR1xospmbCAmLZvh6ZlEZWQhirBkdLg7bQn3ZixhfPoyRs1eRHzGGqJTVhObvo5RczcxMnk9MjBNVN2xKVlq2xNk3TTTBIDHiDX4LA0aOP3biwN4h87szpBZ3RiW2IVxz3Xiqcfa88XT/dk+wAxpsIDXC68VAUJ1wCvrCPBKqyCsAuA9FGgjv2zQmgfaPOpmRfu05pW/JlUBr/gqxwd4pRqbAK8MvJRlFvCW92X195YnE4bhoFSZd9BadcBrpiVTqKsgwBq0ZoU01CsabAAnc0EBb3xyFqNTs4hNkzZbmWRSGJm2lsh31zMsYy1x8zYSl5JFfMoKYuYsUgUkhs3LIurdzUS9u52hc7cyYt4OIlO3MDpjmwphkO1OTF7DxORVjE9dybjUlYxJW+Oxhp6WzB909e8LH3jNQWvdGTyrG0MTuxD/fCeemNKe/z7Tn623SR7e0xXe6h+IZ/oAvbj7+wKvKGDy2VeHNIivZJ7E7qplHugVhXdXqMH+VhUrvHo76hOyTwyv3p4s19bQr4f4QYDXP4bX97eZpcEOw0yFV8fwiu8aOvDq+0i3ld1PerkeuGamMRPorSHwlmVq8BaeON/ZtqwsDQ2AyKxT/FE9UKfAK2quVngFSssDb5YC27i0bCTEYfi8TIbPzSImbb1SfgVYR89dzci31xH53kYGZ6xn+LwtDE7dROS87USnbiU2dSNjk7PVdscli4q8UoGypD+LS11JXLpA77kCXgmP0CESaxiTvlxZfHI241IXce+8D5n8/m8YOms4w2b2JXJGN2KTqoHLWocLVAKt5barj8G/rWRd33AGmS63rUrWqbKP3q+57pmENFQHvNvDzPKslT0Erfnm53PtB4GCyoBX5vsOWhPgrSgtmf4UL/39gbcMOizgVeqs+F18UhHw+g5a8wVeSUtmAa/3JdP/ntL3sn9b1s8vb6+ovGZaMrsaHHgg2M6Sxs7TB62VAW9xWaU1C3gthfdHpTNr4+fcA7UGXhlw5mvVAaQu6KBbUXRFsZWwAzEJWdAQLKEOArYCpqOUrVUKsPRXlpqp1GBRhH1VYVGHvaEMZhYI8xi1qmtuU46huuP1X67PVcIvZFqWq+0o2D0deMfOyVLAO+nd/+PBP/ya4S9HMjSpD9FJ3Rk1szvmoLGzAcX6uU4Z8Cb1wHdag7VvxTWZp0MaROGVkAat8G4ecJ0qPCFZGnwffvrBZxZDKL/Mt19Dma6JwqsqeJUBr1lpzczSEAYD+rAvyK7SZEkMr8Cbhl1f4NV+928bip+rOs+KgPd0hdebh9dKS+YF3pr/HZt5izXgmuEM5jyt+pohDQK8ntLCW1ZD3nEVp2om3pUUvHq4GmaGsnP+SK58g5bCW7lvrCWWB2rigToHXjNzggCqCbsCvAK6SoFVoQerGJMm5oFVBbMCvOY6JihnEp9csWkw9YVTAdRR6ab5A63+rYFc/9ZtxcAr8cBa4V1nHquPwhufspD73/krk979FcNeGsHghF4qjVbU9E4W8Pqpv76Q6ztdFfAOfak7w5O6MOGFLjz9+PX875cD2T6oI7sauVQZXF/Y0MBV8wel78O1/k2fPfDa2SOlhasBXu17r99NH/r/1v0aYivAq18U5Pz9AdhUeC3grfje0CBb3d+m7qdjd6X1AK982bB5FN4ghwd4B4Mv8EoqXhW+W+hJSGYBbzGWwlsTyLL6XDgesIC3EpX3XAGvKLxjkxdw39t/4YH3XiHylZGMmHUTMTN7+IQ06M/6DbEtr1T7Qq7vdGXAO+zFbgjwDkvsTPyz1/P4Y9fy6VO3srHf1WwJlpyx8lDzPgy9oKUfgN5lvv0ayrQFvHV//S3grc01qOnfse5nAe9tT3zPwMe/57bHv+a2J79mgLSPL+S2J5Yw4PHFDJj2A3dO+Zoh0+Zz60P/puPw3zAt8W227jxqFuAohqISKy3ZhYNx1pHU1AMXFvCWqbxm5gZReU11Vyu8a83MCirDguTV1aEQFau7WvWVssKmcpyJVG8Ti00zW63c+rfnCnildLLE8EpIgwCvhDQMSezNiOmdiXzheo/C2xBBV59zbYC3GybwSpaGjox/viNPTr2WL35xG9sHXc+uRk5VJMEXXi3gLQ8XFvCW94fvvXK+pi3grc010CBb3TZ0Pwt4LeCtKR5Z/eqbB2oNvP6gWN1vDZK69YYC+KYJ88baloUyaCVWpxPzDBAz8+t644glhMHXBHYrAl4NvpUdb9nx6f162jMNaRCFV0IafIFXYngldjc2qYsKbZAqYZaZPpB8uhWZ9o8UmBCLnm7218A7NOEGxj7bnmmPtuPTp37GpgFXsTXErArmCy4W8JYHAwt4y/vD9145X9MW8NbmGmiQrW4bup8FvBbw1jeMs86nph64AIBXx7/6Aq8MBvOHXs8As9OA1wu7AqO+sKvhVFoNthpkdavn+7eVLdfb1LHBsp5kkvCCe8UxvBLSoGN4ReGNSuhKdILE8Ha1VF6f8sZRSd2oyMzBfd2ISTAtNsFUiIfO7MLQl7owfOYNjH/hep6ceg2f/+IWtgy8hu1hMsCq/IPQAl4/f/iFe8hAM/+0ZBUPWrNieM8VEFvAW/6ePDO/apCtbhu6nwW8FvDWFI+sfvXNA+cdeP3BUhV/KINY30IQJvCOTc/0wK8/8JqArAG0qva0fXoyKwjUVrTMhFhP9gW/Pno/NQXeuzM2MT5tsUpLdv87LzPiV1EMf/FGE3JndCQqoUsDt64K/uUFQCwysWLTy6NndEUsZobZ/47p13Nn0g0KeCfOuJ5nnriOL355qwJeS+GtDgJqlpbMAt7q/XhmkFZ+exbwlvfHmflSg2x129D9LOC1gLe+YZx1PjX1wEUBvCb0nh3wninQ6v7nSuGVkIa42d9zz9wPePSD33NfxiQefW80T78fwzN/iOTJ96IauI3kyfe89sQfRlKR6T5PvzsSsWfelXWieOqvo3nq77E89WEUM94ezO9euZXFLw9j513XWyENPuptZRBhhTRUB0o//nILeGvjYw2y1W1D97OA1wLemuKR1a++eeDCAV4/JVWDZ6WtRxXWiqtuK+1/hts/V8BbWeEJGbQWk9i5gau7om5XrfDq8IaKQxq6okIaXu7EiBc7Igrv04+353/P9mPboPbsCLdieCsDXT3fAt7qQOnHX24Bb218rEG2um3ofhbwWsBb3zDOOp+aesAC3kpA2ALe8xVqURvg7cbwWd0Y9nJXBbwTpndQMbyfPfNTNWjNTEtW/kFoxfD6+cNHBRbfWDG85f2jXwx+zNYC3tr4XINsddvQ/SzgtYC3pnhk9atvHjgnwKvhUNpzpbDWdDuVKbu+x+Q7XdPt6nX8++v91TSGt2YKb3no81c9G9Jv/xjeqhXebtyV0JG7ZnZkaGIHNWhNFN4vn+vPjjs7sLuxFJ4o/yC0gNfPHxbwlsvT/GOCbWXbtoC3/D1ZmZ8qnq9Btrpt6H4W8FrAW98wzjqfmnqgVsBr5sg1B3dVBoj+wHiuf2sA9d+uPh7/1r9fZb/1ev7L9f7OFfDqT/VWa2Zd0IDr32r/+GdpGD6rO8Ne7s6ImZ2ZMP0Gnpp2LZ//vB9bb7/WytLgA7MVw4I1aK0yv5zP+RbwVgerVS3XIFtVH1mm+1nAawFvTfHI6lffPHDOgbcyUNTgqIFRt3r+2bb+29H7r6zV+/FfT8/XrV5f/9atXs8CXl04orbtuSk8MTypkyo88cSUa/js6X5s6n+NNWjNAt46V29rAs4W8FYHq1Ut1yBbVR8LeAc9tohBUxczcJpUVLMqrdU3kLPOp2YesIC3kjCM8wO8XYlO6t7ArSfRSV4bObMnvqbLC1dWWnhwYmfukly8CZ0Y99wNPDn1OlVpbdvtHdgZIZXW5IHofRhaIQ1eX4hfrEFr5f3he6+cr2kLeGtzDSzgjX3ot/SKfp2BUz5nwBML6ffUIm594hsGPvkNd0z7ljsf+wELeGsGRFav+u2Bix54tfKqWw2qo9LX4mt6vu6nlVr927/176+X6/XOjcKri06UVzk13DXEVgOuf6t9EZPQA7HYGabPIl/uzYhXehH5YjcmvNBFAa8ovJsHXOdReC3grQrcLOCtDWydm3Ut4K2NHy3gtYC3fkOadXbnzgNlwHvHAzO57cl3GZ++lujktYzL2MDY5FVMSF7BxORVSAnfUWnriE3fQIxUFktbQ3zaKtVqOPRtNSD6txoYdeu/vLa/fY9BgNf3t0zr7Ve3f72e7q9bc7016PXNftnEpa3z2Fri0lcxJn05Y9JXMjYli3Gpi7h33gdMfv/XDH1pKMOT+jBieldVNSwqoTvKZvRsmK0+f08bmdidyISeZRaV2AuxkQm9EOj1B97BiV25K6kbQ2Z0Iv7ZjjwxpT2f/3wA2267/kdXeHVWA8lsINNVgeW5XuZVqmu33wsFeL3nowGm/ItKTf3n3U5lfvHdrkzrmE7vfH0t9bZKPPGf0hZKJTrHub3eFzPwah+Vvz52xFem3yq+DhWvV3Hf8tv27yPXzXvtKu+r++nrLa05T46lyGbnhMPgQJCDJY1dZMcNhi2rIe84paWl6omrmpJCoEj9LjFnn7uncTVbkuMoKSlRJtNFpbBg9S4s4K3GcdZiywMeDxiUwoliEOC9/al3GJe8Eql+JgrmhDkruTt5ORNTVirAM4FXoDdbwVx82krMgWv+ZYAvjN9x6WdzHF4o1pBrtr7bMkFfoN9Ukc2XgVHp2YzKWMWojJXEpa9kdNpqxiSvYXzaAu57+8889P6vGP7SXUTO7E30C6JS9iJmRh8T5hIaZhuT0AsxBbRlrfjCtKgZvRGLnt6b6Bk9lUl/8Z2sMySxB0Ne7MHQhM6Me64TTz3Wgf8+YwLvjnDHaSENlT8Q/R+k1f8W0M1zmibTvts+7YEuQGwzKBE4lr6e37rV81VbDTyrB7TdTCEm0777PdPpugZexCc2gwJlNkrKANRBiWErOzflT+1jWccvHEPgRQBLwKXYYaPULubrGw/cGLJdL/xghCBWagQqAJL9FDvNa6RStBk2imyyjotCu4OTAQYnAw0KnZ7rqY5Fb09a332a56aO3XOe/svl98UKvHJe4m/1wue5p7HZKLU5KbQFUmA4zRdBfa8r35jXKU/+ds7Ji6L2fXm/n+5n3c8CXiuG1+K/huoBQ15SjyngncXAp98hPsULvAK/dyevVAqvQPCotA1K4RXgFXXXBF4Bwcog8SKc71vmWE+XnZ8v9K5BgNofeEXd1bAbl5qpFN6JaQu43wO8I2bdwcik3kQ/35tR0/t6gLdPGeBp0GsYrQm75YFX+6IvIxP6EjWjjzIFvB7o9QXeoUk9FfAOS+xSBrxfPD2ArQM6IMCb6zwdQk5/GFb3sKx4eWXAq0CgEiAVoNWAINP6WHzXkeXafGFJpqW/tKIyngul8UIBXqWc2gWGTLgUyCz0AKxWVeV8le888CigqP1jAq9DAVih3YQwf+CVbct2FaTZzH2VGsGUGEGUGIHmMvG97MezL7OvF3iPBxqIybGoa6FA26sW6uupW318utXzfduLFngNO/l2B/mieJcD3gDybUHk2QLK3evqnpbraneYL4oW8J4Rd1gK7xm5y+pseeA0DxjyceZwCQx88GX6P/0+AmkStiCf6MemZDI+ZY1Se+NSNxCbtomY9E3Epq8jPlWHNEjYgHzSry+mQxMqavU56lCJbMakrmNM6gbGpGwy27S1xKWuVn6UcAbJwzsxdSH3z/uQR/7wa0bMuouRSX0V8MaWAa+/wtlwfgu8SpiCV+HVwGu25RTecsBrxvDWJfAKxPiCqUCMnifQJearWJqfz10UiVpoM02UQzE9L8/hIs/hKLN8h7mdMiVNF4eoJ8CrQdCEIbtSBwVKcxx2Tjrs6oVFcilrE7gSAJZWFMICp0391p/QxU+m771wbMKlqSzq5XkOu9qGqMGyb/Gz6WvvemVA7VGEZV19bYqUIhyorp15jbWC6H2J0feDPkdpfUFXT1+swCv+yHWaL5XiQ/W1QhRru5M8eyByL6vr6rlX5WvIKaeDU05Ry+1K2dY+OPu2Yr+fvj3dz1J4LYX3NA6yZjQQDxj5wMESuG3yLPo98z6jUzNNqE1dT5zEn6as9cTvbiI2bYsyUXoFeMelrlHhDwLHAn71o92AwL1AbFlbdn4CuCb0SisWn7KO+JQNjEveolp5SRibslopu6roxOz13J2ymPszNPAOYURSH6Kn90EBb9ln/IYDuWVw6xOTWzbPE8qgFW4NvBL6YcKxCch60FpVwLs9zASm0x9+FYPHWffzARkNvBqgTBhyUWq4wHAjimKhLYhTDtOKtLpoBKnPwPkeUNDgKwqaKGICF2Lqs73EkWql2GffZ3P8da3wahgUMFKhCKLAOpyccNo56rRx0mVXYSnS5gTIi4AJpqayaKfA6VDQW6rCDrxQq4DYaYKx8pXHf+JDgS0diqKV8lwBaKfHr9LHsCmT61f20mJzkm93IdfIe91cnpAHDVTl7y19frqt6BpdtMBrN9Q1kTCPfKf35U+unwZe7V9ZnueyKeAVSFaA7Bf+UZFvqp9Xsd9PX0/3s4DXAt4GQnfWaZ7mAUNC8A8p4J1Jv2feU8AbnbaRWA/wmoOzJJxBA+8mBbbjU1apUAcBYgV+GgAv9jbFhHcBWYH48m22gn8J4dCD1salrGNc8gbGz9mi2vjkTOJTVhOfLOpuFuPmZHN3ykLun/sBD7/3MsNm3cmIpF4qJnVUQl8zfjWx4WZpMAehSS5fjw8qeQHwwu6FBbwKZDxxuTpEQeapz/Dyab3s07mLUlsAxTa3goGjAUGICTwJDMsn9TK1V312Nz+/m8BlgpyOPdVqpvm7PGCd/qCvenldA68cr/hLAFXUWgHXIoejDHgFjhTc2h0UOJwKhtULgKjinr7SX2BZQ6XAlICxQPLxALMtg1nPi4JAr4biPJfAmAltxRLKIMql3UmufJI3glSMr8T5FtuCyXO6yXWYCqVSLxVICxSb18jf//qYdOu/XH5fzMCbJzHNnrjmUwGGUuTznQHkO4I90Ot5uXCYLycCw+pvwkcRrsgnNZ+nQbbq+1wPUPMOUvSGoci1kWOyBq0tYcDjixkw7QfunPI1Q6bN59aH/k3H4b9hWuLbbN15FBnzU1oMRSWFFFOkBvRJqIU230F1Qht6/mnkYc2wPFAHHjCKKOWIhDRMTqL/M/PU5/iYtPWMFiWzTOGVz/QbFPSKuiuAKwPZZECbDnkQ8K0fJgP2KjLP+XmUbVG3lUnfZFF4N6nwBaXupkp2hkwEfsfNyeLu1AXcN+99Jv9hFsNmDWT4zB5EzehJbGJvL+hp4Gtgra6cVjPg9cnSkGAWvKhLhVdDjICPnpYHtXqAekbzC1SJCZSZ5iTP5eSw26VMPpFrWPJvzYe0fpDrB7upUHmVR5mv+5x5W9fAq30lPpKXBBlsJvAh4Qyi8op/BHAlBKTAEVBmxQKjNhOCJU5bgFZvQ1r5dH48wMHRQAfHAs3P7rJ9dZ3KYoNl+3ZyJWQiwEDgrUC2I+u6XBxzusl3hFBshFFqhFFshJDnDFbbFsVSqZdKbTcV+IpeQPR9oduKrtXFDLyF8rIQYMY1HxPwdTnIcbnVi4G8HMhvefGQFxe5fvLSh81BqY77rcW9a/pS/11Ud+/rfpbCaym8dUBa1i4vCA8YlBZyogjumPQCA59KIz5lBaNTszwxqKsRJdc3dEFidSWc4e6UpdybvFiBr/S5+G2NgncB+IrN5xxTVzI+dSXjUiVbgyeGOcV8QYhLXUlc2jKkFfgV301M+5b73n6Hye8nMeylAQx/sStRCV2JTexpAW9CN5WeTZcOLgNfP/DX6ci0Ihx7oQCvB54EaHxhRtReASKBKRnVnxNogkG+BxCOuQ3EZLnAkzb9CVhBtABgOSCQh7Z+YJthEuWh2L9/9b/rGnh1DK1+aZDzlWkBUYnjFeCVMIJcR6ACqVMBAeS4ApTSK2qhgO2xQDuHgwyOuk21UYqNnHS5OBHg5kRAsFpP+op6rGBLgMujthfZQpQaeSLQyXG3jRy3neNuB/uDXexzB3AsMJgcZzDSr8geTL7DrVTMXKVmashuOMCrwV232J1KeVfXwelv3+aOAAAgAElEQVRULwonAgJQFujkqNuuTK6H+E6+cCjfO2yImu7/d1P+fq/+/vUqt9X1tYDXqrR2QTCXdRB16AGDkkJOFsJdk15g0JNpjEtZpsIaZPCafJo3gdcEO0m7JcArqcgmpi5WsakTU5Yr+FPAKyAo8HuxtRUCu4CvQK5uPeflgV05R8lSMSp9DTFpWcSmZhMrbfoKYtOXMTptpVLL5eVgfPp87vUA75CXBjDMAl4P6JuwKypvzYHXXEeAV14ahs7sXmmWhh87hlcUPXPUv47zlIeq+eDVwCuwK2B7PMjglNsLvXoQlgCvjidVKqUHfgV8NfRquFCtJyuAhD9IKMTFDLxyfqJ6mwOYJCWZCUCixEoMdK6EHXhiZiUmVJTDU2IBptKbL4AV4OCwAGqIoexYoKi1TgWpOc4w8hxh5NtFofWAlt2JqMMSSy3KbbHRmHx7BMcD3RwNdnHCHcjxoED2BQeyNyiAI0FOtb0iu4SjyH7dajCWXLcciUuVmGJPjHVDUHj970VsQeplIM8ezEm7JzZdgDfQoV4g5HpIWMkpZ6BSy/WLg7yAFNsdFvCewcPfytJwBs6yuloeqMADBqUlHM+HwZMSGPzzdxn91hJGyUC1jGxVeEIDr8CupCOLychWkBefvpRxaYsV9KkUZZ6sDTp7w0XVyrGfZhKnaxaYMAehrWasqLY+JlAbnZFJ5NwsRs5dR6zYvNXEzl1JbNpq9eIwNj2LuDlfc/fc93joT7OI+u1g7pzRmejE3j4hDb7A17CmVUhDoq44J+decTyzV+HVwGuuU+fA63B6BpWJamjGBcon6kLJKyuj0gNtyEP/aIDBIZkXGKAgTj7RF7ncZqyow0VxcBCF7gAFv7k2g1MS2+nyfDb3fO4XEJTP/ZVBtobtM2nrUuGVc8kxDPJsdggMUv6Tc1cvABLaIKqsw02B4QBnGAX2YHLsbnIl9tkZwCnJ5hAUxD4pGBBq43CYg1MBDk4aNghsTqHRhAKjMUVGE7A3osAIJM8eRIErjFwjFOwtKDaaUeBowglXCCeCQjnmCOKw4eaQI5ATwW6OOQ0IM9OcFRoGuJuQb4RzynBT7Ao34dewUSCw7lMAwdevcj00KFZ0beoypKGq46roWKW/elGRe9FwkSd5jB2XUmA0I99oTKmzkbqn84Ps5LoNCgNtFNhslDjCKLQ34qQRxklbGAQ3UYM0fX1W0f6qn6eVW0vhtUoLV0A41izLAz4eUIUnjuXDnZNmMvDJt5WqG5OSZWYjmLNS5eUVyBMVMyojm8iMbKIyMhmdvoy4tCWMyVjNmPSsembrGJPua57zy8hkjI+Nzsgket46ouauIzJjvfJPdMZqYtJXEpthhjqMTs5iYsZi7n/nL9z39izumjmIIYm9iHvpFka+YFYP86qbDQt25bwvduCVFExmFgWHSscksbUywl+gtNDp4oTdwUGHg6MREewNCWNvcAQHQ5qzxx7BXldjdgY3YXtwBNudQey0B3DQFcgpdzAFAW4Fg6J+CuiWN2+qMgGQ6qGg8j6+YKZhRmBdb1fN84QZCNQVGzaVNWFHqJ09rcNgQB/2BZmxsPJ5W+CtDIh8QjJkO3qbcrxquwK6wSEqPrfYsCtoPOmUkf82FUdbYBP1NJDS4CYcsrk57I5gX1AoB0JC2Wc4yA9txHFXEMcCXBwPD2S/KMMBLkqDGnHYCOWovQWHnS056m7FoaBm7Bc/h4Yrf+8xGpPjaEmu0YgCRzjHAgI5YHNyyt2EnKBLOB7UwrweTd1scBkcDg/gUEgQO41gcoMvI9/VmmLXJeTa3OrlJM8w1PHL+SululwoSv0AXnXNPIP+5BqL+p7naspxozknHW044WzNEXdzthkODjZ2szvExuGgAI65gsl1NeeUowX5wa044WzMCcNFvhqs6f0qcnb3sQW8VqU1H6KxJi0PVOEBo7AEjkrhiYd+yy3T3mXC2xuI9qQYk0wEZpzqKqXsRs3dwIh5YtnEeiqKxaRnKhgWIK4/lq3KJ0sJZdPMc4tOXYuytDVEp61hZPoaouetJ3ruRkZmSI7iDQp0zZLGZn7e8WlbmJi+nEnv/I1H//gGw1+OZPCMG4lOuJWRz99k5p+tRNWsTO2sP/NrF9IgwFyXCq96QKuMCmYaKwW5kk3AcCJ5WkucoZS6m3PM3ZyNQc1Ye+nlfNOsLV+3uJIlba5nQevr+Oqyq5h/WTu+bdZaLd/WuCU7AyI46Y6gKCBMqWiigolpkJRBWXqQli9Eng0w1CXwyjnlG4YyBdOBAZxw2zgqwBvgIscewFG7mz1Bjchu1JTFTZqwqE0Lll/SlJ3hzdjvDFGKrIQ55AcHclRgU2JuXeHscLcgO/wyFoa2YmnrdnzdrAnfXXEJX/6kBd+0bcmaplewI6gVuQERFMgAtSAb+wJtHAlpwm57U3a627ChSVu+bhXOl+0i+Kx5CMvatmVti6vZ3vha9jlacdxoSl5AKBJaISqmKNaqwpsHdlXVMR3i4gf8vtfqYlB45T4rB7wOmxp8eSwwhN2BTVjrak5W03Ysb3EZP1x6CT+0asTS1k1Z2eISMiNasi3sCg6EtuNwQAsOG8GcMkTVd5V7CfL1Sc2nLeC1gLcKwrEWWR7w8YAh4QwHimDQw7/lxscymPjORqJmryFujgy4WsWE5GXEpy5Tn+ij07M8Cq8Zqzo6bQWico7OyKpnto7RGaapcsHpaz0V1XQrFdZMi03LVPG7Kmdv+nrGpmeqUAhVdGLOeuLf2kD8mwuYkPwBD733e4bNHMHgF24iekY/4pIGmBXWGizwStYFE3q9KndNQxpMNbwuszTIQ1kgoAxYVI5XE3glRhRHI/KMxpxq/BPWtroaEn4FH/0TPv4PfPEDzP0TfPAX+Ogv8MGH8OH/wZO/4MiV7dltD+GYIWmxJG2ZGSqhgUPiewV6xWRezeHg9L51DbzY3eCUamd2igOdHHEaHHTKqP8ADriDWeFw8k2jRvzQrRO89hJ88RG8l8q+7n3Y4m7GQVcExWFNOCzXIiKMQ4adTc4gVrZtx6Z+d8KvXoW334FP/gqffQAfzobfJ7G2/118HXoJO9xBHHIYatDbnnAH2RERZLW+ipIh8fDS6/DnefCfP8GXn0BqMiceeYqvruzCxkZXcTL0MvJDGlPoDqTQZcakNgjglWIdDhs5gS72hoWwKiKCb9q240DceHj19/Cvv8E/P4C//wnefhum/pxPm17Fyubt2df4CoqaXApNwsn1/P3U5v61Bq3twgJeH6KxJi0PVOEBI6cUDpTAoEd+xc1TUpn0h2zGpa/mnrSV3JO8gHve+oa73/qOsXMWEZe8jNiU5cSmLGNM8gLGJi9QMCyZHSqzMXOWcfHZcsbMMW1s8grGJi83LWUpY/1tzjLi56xg/OxVjJ+9gglzFiubOGcl97yVycQ3MnkwfSkPv/0Rj//5NSJnDWPkiz9TCu/QZ3pawFsGvH6g65eP1xvDa6YmuxCyNOgHtf6ELfCpwM1wggCvXWJIm3GoSTu+vfwa+L+/wv7tsHcb7NsP67Nh4wrYtBQ2LIONa2DOmxzs2k19dj8RGKriHH0Hpql9eNS22sKuAnafT++yPVGRz1dIg/hK4mIlnOGkhEI4bCoXb05wAHvcgWQ2DWdBp2vIvGcU/OePsPp/sHc5/PBvdo+5mx+CW7IzuCnHQ8I5Fepml8NgW0Qw3zUJZ8mQ2+G9ubDge9i1FVZ+A3tWw/4VkPk1/PPvrB0Zw3cRblWC+lQzN9uauPj+urbsefR+eP+PsGo1bN0AW9ZDdhbs2A5rVsA//8Widp3Z2+hyckIbmXHZnop44kP9AuT/MlHZ9dL9xfdyTeS3XAP5LeucshvsC3fC0FvZFmqwP8imBsvJfSEqsq+SrO/Jmray/cqOy3cbup+6P9RgQxvH3E6yG7tZev2VbH1sEnz5H1jwtXkfb1wFmzNh6zpYtBDe+4C/tuvIqmatOda4mXpBkdRkvvs4u2lL4bWAtwrCsRZZHvDxgKq0tr9Y8vAmcMuUN5iY8gPxs3/g3tnzGffKx0xN/Zqpqd/wcPoCBW73Zyzn/oylPJixgMkZ3zMpbQGTUhfVI1vMpFSvPZC2BK8t5oG0xTyQvrDMHpm3nEfSl/Fw6nImJy/lodSFPJy2iEfTV/JYxloeSs5k0lvzmfh6Cg/NS2Lw9H4MS+hB7MybGfPizxp4SIOvwns2wNuDOld4pUKYQI7PJ1+J4ZWsANgiyDMi2Oa+hPntO8Cnf4Odazj43SfMf3Emy2cmsXzGE6z95YNsm3oP+VPv48TQQaxv2YJtQYGcDAxS8cFnBwI1gwl/KFNA4wNBAjpFP1IMrwladghwm3AXIAOcDI4E2FkdZCez27XwzpuwYj4c2gIndkDJPti+luWj7+OL0CvZEdKKQ64Q9rvsHG3VhKVNAviu29Xw9b8gexV89z3ZTz7D54Pu4K+33UTWq8/Dkc0mBP/xDyzscj3LmwSxJSKQHxq5yLp7BGQuhOxs+Ohjvhs7gb/c+DNW3v8w+1LSYcdm2LODoudeYmmTyzkYGqE+7Uu2Bom3rs8Kr9wbGnjl68L+YAffNwum5NnHYNMqWLUIPv4H88eM5cvbB7L+kckceC8DNqyBXbs5+ZtX+bL1Fax1BsIlTSl21gy2q77/LeC1gNeHaKxJywNVeMDIK85lb04ud07+Of0efZk7n32b25+ZR+wL7xD55OsMf/RXDH/kFe567HUGPTaH26akcNvUOQya8gZ3PPY6d0yZze1Tkyu1QdNSuLgsjUHT/E2fwxwGTTvd7piSzKDHUrj90WRuf+x1bpvyOrc9lsxtj83lZ5PTueXh3zH42Wd54k9JxL96F9GzujLiuY6MnN6l0qwE9SdO1w9k/cI3tHJbdr5+yq4uOeyttOZReGeY261L4BVgUyVTVWEJKY1rxtZKmVqJ58UeCu5L2BbYiM/btoK/vQeblnHoj+nMuaEb71/env9ceQVftWrG4pZN2di6BXtbNOFAsJujkvLKAxiS4kxZOWW3pg/6qsG3roFXYFr5UWJ5bYZSeg80CmRdxzbw0pOm+r1rE3nv/IFlM38Nx4/CqiwWRE7mi4iObHFcQl5wMw4Hudna1M0XLQLgvddgweewYT2r4x/hk9Y9mR90NR+7W/PRjT9l15zXYPs22LwVfvcaC9pdxXeXNuPrjleT//abcHAn/P0z1sTcy9eXXccPLa5kfssO/PfGQRz/SpTiA/Cv+XzbrR/bIpqpogoCvJJiDYdXdUUA+CKP4dX3h3rx8dyP8lIi2TS2hbhY0qMjvJsOW7fC1wv4YcBIVrS/kSUhrfi++eWkXnMN7NwM2zbBPz9ibpOm7Lj6OnZLUQ+7VBj0+kj76szamv4d6H46j7UZJiQquTo3q9Iatz1hVVqrgpWsRfXAA0ZR8SlOFhcw4ekXiHn6Jfo9NIOb7n2OkU+8TPSURMY+mqBs9GOziJnyClFTf6MseurLRE+dxchpv2LktF9XalFTZZ2Lyczz0+fpbfU5vEzU1Jc95/0rxA8xU39N7LTfMerxV4l9/BU1T14Uhjz8CsOnvsawaTOJfOYRHpn9GJEyYG1WV2ISOzNqZndP/tmqobAMBv1g8dzP988S4XNcAqJl+6+iX1kfn3WrmHcxA6+oXTJ4TAGuKL0CvFKQQAOvFCswItjfqCX/u7wNfPJn2LKKw6mv8067znxxWUdWtbmS7GZN2NwknJ3hwRyUgVtOOzkCuQE2pajJfuShXM4qKWV7ZrBgZhTQ62ioOW8hDTYD5NwEesV/gXaVAWJrsJ3vWgbCcw/BrGf5avBg5l3ZmY3TpsOR47Aqm40PvMC/Q6/lQEhbciVdWVgY2Y2C+PSalvD9p3BwK/t+/yr/urwbWc06c7RRRw636cbfmrdlbq/esHgx7N4Df/2EdxpdwsdXX8XG+++GDRLGsIU9j0/ns9YdWBHWgt3NWrG96RX8vVEbDr3/FzhwFFas59vIsWQ1aaEKZOTYbErhFYCT85GwBA2L4l997bSvfdsLOaRBXRvP8UuREFGxc+02TtoM1oUFse/B+yFzNew+xPyxk/ny0k5kB17OyeC2bA5qzj/aXEHBnNmQuRLWrWVZTCzftmjFvpBm5NuCkKwmvr4482kNslW/2HljfS3gtSqt1QNys07hrDxgnDhxAFF5tx06wJ7CIjbmFbLuVD67S2FnfgmHCuBwnmmHcuFAHhzIh30FsK8I9uSVNhjbmw9i6twL4EABHJRBf3nmfPHLkXw4mm/O35dXyoGCUvbl57CnYDepn/yG+Jm3qzCGEdM7MbIs/2zN4NALnD79/RXRMrj0h1L/3+Y2NHDq9GAC4mL62CRfsKmy9vGEX8h2uhKdcANRiTcQldSVqKRuRCX1UOaryEbP6ImYV531QrO5ju8xmcejtxOV2Asx7/a8ym7sjB6MmiGD3eo2pEEgRg8eE0gUcJHR+pKmTBU6kMIGtmbsDGnFJ20ug3/8GXZtZNvrv+OVZlfx7yt7MP/Stiy/tDnLGrvZ07Y5BxuFkxsaRo7LqaBXtq/z78r+SgV8PVXcZL6a5xOHe6bA4A9l6pP1eQxpUPv3nJe8KBwLcXOweRjbL3WT3dTFspBQFke0YkGLazg0aQoc2AobVrFm4qP8r8lVHA1voZTCE44AVoc3Yu2o0bB8Kezbxd6kBH5o2ZZ9EZew33Cyy+Vmy0/a8VXP7pz864dw4CB8/AVf/XQQGde0Z+0zz8DBI7BkDfsnTmVZy6vY1aoFuxsHsCskgK/Dw1n8yKOwcg2sXw/vvEPWpa3Id4dQ4AxS2TkkbVuJCmmpGMBMINaQJq1538i9I77Xv89XDK8XBCs7Xs8LiU3uQzsC9lLp7qjDwYKAQNZPmQbrNsHOAyyKuYdljX/CkeCWKq/x7pBQvrm0Nd8NHgHrN8CKFfD+n/m4ySWcuuQysAfXunBKdcfv/XvQPreA1wLeM2MlKfghpv/pAiDyu6SkRM9Wffz7lS28SCf0ufueV12dij6W2uzfKMw7Rl7+CU4UnOBw4XEOlORxgHyOUsoJIB8olAtbCsUlUFhq/s6jlDzP8gKgvlo+pWir6BzFHzI/h1JyKaWouJTi4lIKigrJKcylgHxOlRziZMkuUj56kQmJdzDihV4KEkfO1MDnA7BlwFrDeQp4PTBapQqr96VbDby9FDgKPArIxgiEJwnwSr8e5qC6hL6MVNbHo/JKv46MTOqogHfEzNOBN3p6b5T9qMDbrU5jeAU2dX5cmRbThSFKjECKjGCKnC3YHtySv7dqDR9/BLs2cOQfH5F80+28fUNf3ruyHf/p0I6vO7RhYdumLJUcpu4IDgkABbi8sGtINTe7F3hVbl7zc6z3oV4xtFS1vK6BV8U+e0BPKqsdDArmQONQDkc4ORRk52RoMw6EtmZD2GUcj78Hdq6FdYtYcc8DfN78cvaGhXPKbiM3PJwfgkPY/sTTkJkN69fx1xv7sqxxIw4HBlLcOIwDLoOtERH8o2kT1s9KND/Df/ktnw4eQdrNfdj85u9M1fez+Wy8LYa1jVqxPTyQI02cHAtxsbZ5M/7Q+2b2/+NfcPIw/O9T1rRsRX5gmCqbK8VEZABezYBXwOvCB148WUAkx7KEbOQ5XOQEBnLUFcCC4HA2//wF2LgTVm3kh2FjWB7elvwmbTgZEsS2YDcLL2nLR517wcLFsGkjfPgXPm3Tll3uYJWOTvugqnu06mUaZKu793U/C3gt4K0NMlEObKsCwaqW1e4Izs/alR1/ZfPPz1F59+L7suGdW/WUUZx3kmNHD1BcWsDBnMMcLS3gOMUcLS0i1wO0RQK8xaZp6NUQWEgpDdWKKKUYlMmLgYCv+lcCRSXF5JcUUEgBuRwhl728+cEMxs24naHPdif6xR5EJkoMrwcszxR0df8zVnh9QLoMlgWY+xCZ2J2ohO6mYqsU1j5EzhC7kajpNzJyhuQN9iq90UlmXxN4BXq956LV3R9X4a174PVWPfPEAnqKRAj45tmc5Ac2YltEc77pcB38XUIaVsOmNeR8/z2sXA1Lv4fPPyIn/TdsuWcMKy6/jj0Rl1EY3NSs3lWmtuoHtkflVWDlhaaqoaByGKhL4BVFU+JBlVJt2Mk1HBwKCOJgSAjHg1yqoMQJd1MOBLVkS0gr8sfdD7s3wcbVLL5/Mh9fegVbwhpxqlEE+8JD+apJKGsff9TMfrF+PR916c621j/hWGAYx9xujoSGsSOsKcuv7sCqJ6bB1o2w6Hv+NmYkycNv5dinf4K9m8n/1ydk9byNXY2v4EhEIwoiwjlpBLApvBUvN7mMnR/+HXKOwLf/ZVXrVhRILl6Hu6yARs2A13s96zKkoSYKqdwj8rJlhjM4lcJ7zBnAEncomfc8BMs3wPaDfBd7N4tbt2eNLZjDLZqxrnE4/2t1Oa+2ugK+/w727oT//ItPWzbnRMsmlAbW/gtFTY7f/NvQ/raA1wJe/aCuWSuAJ3Clrbi4uEzZlXkaAPVy3V/6Xaz/5By06XPQ5yXneb7++frXf59ncxwGJYUU5B4HisgtyiGXEqXcniotUQAnl0yJ+XKOJfJ241F71RqyVikl9djk3MX8z7G4tARlHtVb1G4x8U9pMWpZQWkhecU55HGUPPaR/GGiAt7I6b2JmdmDEQk+SqoG2DNtfYG33LpayfW2UQldFdCWhUaodfsSldCXqMQ+RCb0NKFXwhPKgPdGD/De7AFeUXtN6DXDD7rRYIFXikGowhOessJSDtcTblBgN1Ss4xHDzjqHi9UCvK/9Gub/l9xP/gZfzYcFi2HZUtiapTIPkLkEXn6VZZdcxW5HuPpUL9srUXGu5YtPmLl5L37glXjQPIeo15K/OIAjzkCOBgSRGxBIjsvNYVuIqki3NeQSSu6+Hw7uga3rWTZpMv9q1kYNGtvndLAjPJhPG7s59vorsGUd7NzF5116s71JGw4YLk6FhLPL7uRA+KUsb3s1W37+NBzaA+vX8NmU+0iNGwhrvoQDWyj61z/J6tGPXY0uY79kyggJB1djdjf6CW9ech15//0Gjh+AHVmsbNOagoBgpXxKKIsZkypwVfFLxsUY0iBALsctL3G5ho1chwsVQhIYQtawMfD9ShXDyx8/5NOO3fi2VVsWt76Ur1u35MN215Hx036QtdYcuPbZx3zethnbgwwrD6//E7ya377AIdNFpbBgtZWHtxq31Wqx9rnAq69pEJNWTJYVFRUp8+1Xq53X0cpyzvq8fc9Pn9ePdVgV7Tc/P5+CggJlhYWFyte+/c70WAyKC6FUghYEXYsQNVeUSlEsZVrBrkl8CngFeuWfBsEz3WF96y83RGFpSVnoh/KP58WuWF4einMo4gT57GPu318mfvpAlZJMgFfHyZYBaDlg9VFiz3i+F3J1QQel3Ip6O6OnMh0bK6A7IqkPwxN7MjyxuwJepfR6oNeE274m7M64iagZfTzWWwGyqLojZkocr2n++4ue0V2FTFR+jvpYzfM9sxjeOlZ4/YBX8qGqsAZRLgV4HQY0imB/UBBrQsLY2b0nO+8Ywr+v6cwn7brwf206sn7IOEh5F779H+zeCF9/RWbsGLKatlTgJ4PiBHoFNgSo5LeYCm/wFKSoDK5qMr8uFV4VE+qwqywHUqK5yHBx0hHASWcghc5AilzB5ASEqhCPraFNyB8/Hvbvgk3rWHPPJL5q3IoDIY04YhjsDg3ky6ZB5M/+LWxcC1t38tXVndka0JgCZyhHDQfHQ8LYbASyqk07Nj4xFQ7ugnVr+OapR0mLHQRZC2HfNvjnx2y65Q62Nm7N/qBg8twhFNtD2RNxOe+27ULRf76DA7tV31Vt25DvClJqvrz8aOD19atcBx3ycqECb9nxlX1RMIFdQhrkXLCZL3VyD0plOYnj3RIYyvL2PeDtD2Dvbli9BD7+K/+6sx9/7nUDSyeMYfcrr8G3SyndsAUO7oNvP+ezds3ZE27DysN7Zk9DXwiRaQt4z8x/Z9Nb/Oz7T/+WVk9XBLx6me+6F8O0Pi8NuvJbpgXmNXDq8zjX5yjb831xkH3qeTJfjqO2/wwK8hCVtzg/R+mYBSXFKkRBYFfiU0tlH/q7vbQSzFviI3d7fuv59bktLS5BW0lRMWJyvqL+ir/EfIFXlhSV5lHMSXJL9zDvby8z5tn+xCbcqCqMRasQAAG+2sCt/7oaIMu3FQGvHhg2IqkHw5O6Epko1oWoBB1qYQ4MU4POZvQhZkZfFZcbNaO3Gowm25R1/IFXK8myvF4Dr47Z9Rk8JuAgldAESk86DRWLezwoiB2OAHY3asb2Fm1YG3YJK93NWeZuyfLwdnx+RSe+eOA+2L0B9m2n5L13WX/dDRwKDCa3DHgd5YBXKb+1rLKmQMxHidSwfr6yNEgoQ46UqHU4KLY7FNSfctnVgL0SeyBiBc4QjrhD2BoWyskJY01I3bKezIn38l1EMw4GhfD/7J0HnFTV9cenz3bYpahYo2k26rJUlSpgoW2hiSSiJDYQUOrCFkBj1CTGkvKPKUZTjBqNLcaOBUHp0gR2Yellge27Mzvz+39+970z83aYLcNStrzn53Lfu/2eN+v7vvPOPbcyJgq7Y634sGMs9ubOB3ZuAfIK8Mk13XEo6QK1oKzMbsNRmxUn4ttj40Xfwf5FC7RNJdasxgf33Y3nRt8EfP6R2lzC++/XsW3wLfimTUcca5eI0mgXii0W7I6/AL/t9H0c+MfrGrxtXI31F1+CSqc7ALx+gqHuqcH4whEASpUnn9c1TXBTMGkIjK8W4FVzsdrhUaYNhF4X9juj8Zk7AUcmTgFe/CuwZgWwazOwYRWwdS2wh67fDgAb8lD49Ubg2DHgP6/gtaRoFLaLVl8ujDI6tXORZXiNerBNKWeaNJgmDQ3HJkKWgB1jYyCECZARBiUQ1E4HnDV8lKevJOfDsYdqc5kmACq9nUIlZ/AAACAASURBVO45sm8Ba5Eltbvl5eUoLS1VMa+Zx3IcY6SHpuH1eYFq4hrRTePbgAaXJ7o5g4olw4wD8B8EXr8yZ1Dky5cC+FCNSlSjCFX+A/jDy8swKXMoxmQSGHtoC8Qaa8Or6teEW9GyGu2DFZxmd8eYrOSAhlfTpnbDqCWdMUYFzfMCPTCk5XRW3hrSs7siPbsb0rN6BIIaexZhVgdkXbsrHhsIwRr0hphQhAV7GXvz0/DyYapAgYuudFAg7BJSi1w2nHDbUBztRHlMDI7bnWrb2+NuN8rbJqGoTVsUJ3ZASbuLsbJtR7yXehOwYTlwJA/46nOUDxuJYw4uhtJtgy1OZSOqtL26P9TTAb1GTeTZBl6On/JiUH07LCiO0oLX7oDf6kKlxaZktzMhCoV3TQBO7ENJwbdY/6M78GliIg4lROGI24K8BCv+k2DDsceWaaC1dw/e6NwFW5MScdxhRanVgqoYJ/bZndh40WXYTY8M9MX71Xq8PekOPH9zKkpffRvYcwjFb76LlQOG44voeOyPd6E0xooqlw27OrbHLy6/HAfeeQcoPgasXIFvLr4UVU63sj+mhrde4A0ApQBY0/DSUBvw8jeufCWrFyMbvBargt5Kmx2FTifyExLxdYcLsDa5N/CLXwGvvQK884YGwC+9Bt+fXsFLU2cDuw8AuwqARx9XLwkFuv02+w1C6amcB+VYdztSzgReE3gbjkmiWRTgE9BiOs+ZTgATEBNQk7yG93TuSwrMc04CvJwH03lImoxU0uW6sTHbYx+hQCtppwOwNeD1VipVro/mDZyYXwNfNU+jhpfnCoB1Q15qd1sz+BJqqfXlm4luFEL7XXWhfiMa8Hr8J1CNwwp4p+bcjDELUjBuSQoyDIu8Tl3LK8BYW6yDJLWxIcDLPscu7YIxS65RgW7GCLvpOdcgI7szxuV0wfisrlpY3A0ZWQw9ME65BUsGt/cl2CqtcAB6uyutb2sBXoIuA70NUDPKzSLKHTaUuhwocrvUanbCLhKTFBh5XJq2sMhmUWUOWazYfeFFeHtACnY9/xRwjBC2HFW3jMWJ6ETVFjWhdNJPLwDqkziBV9/sorHAcC6Bl2On72IF7gQgpwXH4yw4FmNBhZU2yy7AEqVsefPio3D4x+OUhrcwbxvW/Ggqlie1V6YMx2NtyI+z4qP2ccibNQP4dguwJx/vDBuMNRd2RJ7FgjKXFSVOGwpiYvHlJZdi1+JFwIGDwHuf4qX+I/BCnxE4/Pu/A0dL4PtyHdaPzMD68y7CsfZJqIhxoMptx4bEGDyd0lktmsOunSj/29+x9aKLUeV0otJi1TcbcdSt4W1mwCsmDYRJbqRBt2sE+2qHE6UOJ463aYMdMbHY2rYTVsR1wMeJHfF+u/Px8QWX4eWO38FvruiKX1x3I7BzJ/DZx9g/bhJ2droMx6LjwJcazTTnVEBX6gjIynVtsZQzgdcE3oajGSGLz3fGhD8BWUkTSCOUsYyxHNOa08G5yLw4ds6V2lWeyzwJo5zj6TrYrvFg2+xP+mR/HAOD9C3jDK1rbKe2cws4eGW3oKlxQ/lVVQxNlGtjqzLulh7r5hxG0w3eJHqqUD9vAV6+DAQ0vMXw0Ib31Ufxo8XDkbaoj+YKTPnhDTVJiPS6btAVkD7Jdpe2vDk9NBdkS67FmMVXYlxuV0zI7YGMRZ0xMas7JmX3wMTMrpiY2R0ZC7pi4uJkTMrqhYzMHhi/uCfSFidjdFYXBbip3ERjqWbioDS8hPmlutcH9hOi3dV883KuMn4dzHV/vmJuIbbG4i+YPniDfnjPrQ0vIUAtKNM1rtRUiq2txxENjzsWpc4YlEbFoTwmAcftbvgTElEZFY1Slwuetokojo7DxsREfDz0OuC9l4F9W4GP3sWJoTdjvztOaYlLnbQHdqqFUQoQCE36Yra6tVq1PfyD6U0BeH0027BoZiBH4y04HEP/xlGotkTBb4lBmSMe+9okomTKFODoCZRv3YWNd96PDxPOx6G4RByzObDf5cKGjh2wKW0CsGaDWpC2ds50fHzFRdh7XnscsTlR6I7B9vbn4Y1LL0XJC38GDh8GXvsvXrqsC164tDt2LHgUOHAMyNuLY/fNw+qEi1HW7lIctzhRbHVj88Wd8MWD04B9m4C8b7Fj1oPY1q49quwOZW7CLaWNfnhp0y33RzSocm30LnBuTRq034KMT+LgOIO/FS1NWzxJExTa8vK3fNBix2F3Eg642mO3vS0OxJ6H/dHnY3P7S/HSpd8HXvgLsGsDvG//DR9c+T3ktTsPR60uwM77SxAN7SOSawHZ+upIORN4mxrwCsAQJwhZPASqCD7Ga3Whaxt5zrpSRvK40IkH2zBCEYGJhzGNmlkezJN0aY9jqajgUnTtkHxph/llZWWBfvjZXdozjoG1pY60zTSOT+YpbetdnTQnKc9YykosdRhL+9JuaGysI3lMk3PWl7GzPc5R7gnT5ZzlpS2JWV7aMY6ltnyWNY5XzouLi1U77EvqMk/aZrqcsx/jIeWNacZzS017BV2zqytyhV2NFZRGt0ZC67qgQI1B2fA2Z+BVvne7IC2rK9IXd8eErBRMXNwbE+an4LaFvTFlcT8VT1rQS6VNWtgbExf1wcRF/TAuqw8ycnsFNp4Q0JVNJVTMhXJ1Aq9Ab/MEXrWCnba8OvRqi5L4UI1CpT0GJ9yxyLM5sK9tEvLaJmF7fBtsb5OErXFtsCEuHhuT2uHdTh2x6o5xwLdrgILtOPD4Y9h42fdxJCZBAW+J2r3NqbS8BBIFCLpmuXGwoC1IkjbYtjIzCGghdVMDfVOEKrVJglXthlYQZ8P+C+OBwb1xMNoGbq1LX62Uh8iCsbHtwNjFFETfrEMgi3bPhfFOHIpx4qg1CkXORBTGdEB+XDusTWyHw3dMA0544P/2IL66axbe6PgdbI4/D/tdbXEkLhGbEtph+VUpwDvvA7u2wffOK3inXw98cH57fHvexdjY/kJ8csXlWHvbOGDFh9oiq189jQ8uuByvJX0HX4+dAmzeBhTsQXHOI/jsu53xdVRH7G5zEbYndMC7F56Pstf+DJTtAd57Ex/d0B95CQnKXRdfdDRtpYBVcO6UgcxR5NHcgZfz5T0vj4vF4dg4HOp4MfZ2uBS7oztib8z52BV9Pj6P74D/9u4DLP8fcPRbHHtmCf53QSK2R8eg0OpUphGmhrfhz08+d/igZ+B5S1i0ZoQroyQErJgm55y3EUAFeiRmWeO5tEftoBxGcJKylCUPea7zXPqRNI5BoJhQy/wVK1bg73//O95//32UlJSovglrAreM2YeAnHEe0if7MsqAZaU+zwWgZXyMjYe0ybJyznyeG69lrjIexhyDsYycS7oRcA8dOoQDBw6cJF+WMc6Ffcv4jePkubGcjEPKyDz/+te/4tNPP1VtHDt2TL1QcG6Ut5SROkxnm8Z2Ja+22CIZvOUNCVJe4obUaUll+EogQQnazzcPb1DDS82u16fZ954TDa8GjgGNqu62jNBpBE+5pj0uNbXjueXxvN6YnDkQ05bcjMnzBmPyvKG4ff4Qdf7jzKGYMOd6TJh3HabkDMX4BdcjbWF/TFjaH2lLeiqPDaNp4rAkGalLe6rAc3p8YJDxGHdd0za7aN7Aq/lc5YYQRvtNwo8DpQ439ie0wer4GBwa1B8nJqXj6+v7470ePfDmNV3x6jWd8fr1ffDe5FQcevH3wOHdwGdfYMfkadgQ0x7HrG5lD1xJDWjAA0BNkAoC1Kmln2sNr0d9KncAdt3UIyoWx9okYYc9FhsSkrC80wV45eIO+Psl52PTjPuBIi+w/RA+vH8O/nDZVXjnvCvwzQXfxY7YRHxtcWF50qXAU/8HrPpEeV34Yu49eHvUcDx3xeX4d6/e+HByBvDOP4Ftq4APX0PesAFY174dvko6Hyu69gRe/RdwZDeq//MiPr9zMt5NuR5vXtEVH6T0x4pJ44FNK4C9G1D+u5/jk2uvwO64KLW7ngJeeRnRNZahsjUCv/G+NSUNL19SahunjJn5NN854bRhR6wDFUOuQ97Ng/Hfa6/C+5d9F59850p8ldIfWydPQeHvn1V+p/HR29iYdjO+SIzB/jYxKIpzocKpufGTdk8tDv+CcXJbUs7U8DY1DS9ZgpAkoFRQUIDnn38e//d//4f169cHoIzAI4DDvL/85S/497//LSiiwFHgh4BkbJPp0j4rEFAJqn/84x/xzjvvBPpgOWpt5WAdphHQjGBKALznnntgsVgwePBgrOUugvRwpS+y4rnUlbYYEypljDynVljKGsuxHQKdHLzmGBg4PkL8pk2b8Oqrr+LPf/6zktfHH3+Mw/xqZYBL9iX9SVvGmHkchxFAOW7Kz1j3qaeewuOPP46jR4+CUM881pGDdTgmgWam85rt8GC+Ua6cGwPLi5w4R8pz9OjRNcoyn/dLDqkrY2Zc1xylHmPLqcKoNHKq9ZtrPYFdif11AG9w0VrQpOH2LINJQw4/54cAasTXYhIgcUh79QBvelZPpGamYFLWQKQ91B/3PzYBT72SjWdezsUz/1qKP7zxKH7/+jL86d2f4+nXsvCbt3Lw+/eWYvbTU5A6vz/G5V4Hbj88Jkd3U0b/vWKW0OKBV7R6fIjKA1Viu7I93RXtxoYLOgIL52heAF75B0784gmsnjkLK2bOwuE//Q748mPgQJ5aBFU+Zwm++UFf5NvboMLmRrmzphsy0RQSgDUIZn+nBrusFwplZ1vDyxcGavngiEapxYlyR1sUxrXH9rbtsKdvT+y9ZzJW3zcJK++bhCN/+S1QXAnsOYLdf/oLVt59L7bfPg0FQ2/B5nbnIS8+CfkdrsCuvjei4ulfAjvWAHs2q3qfzpqBrY8sAd59Bdi5GtjwudKqf94+DvuTEpAfn4C153fCoZ9OBdZ9DhSsAr75BDsWZ+HLqfei+KnfAOvXqG2NfS/+DlvG3YQPk1xKGy2wGwqKobINzZf71tSA1wi9xjmIiQbHS23/UYcFpd2vBJ5/FvjgFex7NBNrp92B9VPvRCUXsK38CsjPA95/Dwd+8lN8dsklyGsXj6LEaByNseCYQ9t0RORwarH8vdX3NyDlTOBtasBLYDFqYak1veCCCxT8zJ8/P6A9FQCkppFgZLfbceWVVyrINMIXzwlaAl8EJsIU4VLgLi8vDxdffLFq5zvf+Q4KCwsDUMXy7IuAJiDFNNaXfgh+48aNg81mw/Dhw7Fx40ZBItUXy/MQOGRbn332GX79618ryGZbRhgUoGN5HuyHfXMcHLscMp6nn34aN910E/r06YOrrroK5513nprP/fffr6CU5aQNqSugyJjjY/9yzjLsp6ioKDBntiFlKGfK/PPPP1fgLzJmzDqMebAO22G7PBdZ8mVB2uI558trmRvLso7D4cCoUaMC6SzHdLbPe8e6xhcPpjFIfzLX2mIL3yEEPsMVkjyWC1dW8ltLLKArMYG32h+i4dUXsgnwesQP76uP4vasm5C2qB80TefpBF4ddI0bURjORaMrNrOyqCx1UU+MmpeM8Zk3YML8YXjsH5nYXroapTiIIuzFcezCYXyLo9iOAv8aHMEW7MN6/PHjJzBl2U1IXUTfvL0CWw8L+Iov35M1vMn6VsbalsYa8AflEIBltfFFCpq2DS8fotoDVDNl0DwqKICzOeBxRaE4oQ22xsXDP3oU8Oc/Am//B/jkA2D5R8CmDcC6lcCKT4B/vIjyaTNw4HvXYZ/1IhRb4pRtaJHbAgb6rGW7hJFKuwWlDruy6yVsnRooaIBgBBoC2dkGXtjtaotZWN3w2+Lhs7XDQVsC8i/qBNw9CVjzFrDxbWDtG8CWT4EDu4GCXcC2dcCK94FPPwKeeAz7Ol+Fgrg4FLrbY60lHoXjJgDP/xH48B3gi4+AtSuBDauBz98HXvoLKuc8iPc7dkRBQqxyO8atcg/EtUX+934IPPBT4JWngE3vA19/CXz8OfDVamDlKvh//gS233oLVnRqj41RVpTEuOBz2OC3Be11w90PeVEJl9dUgVeN2fAyJX6mBXi5VfPe710ILH4A+PgVYMPHwJoPga8/Bb74AnjtHeC3f8P+CXerHdi2xLZX3knKYh047LKgKNoE3nDP3NrSCAV88DPwvCWYNHCunAuBUCCRn7XbtWsHp9OJ6dOnK6AiGBGKJRC+rFarAl62QXiSdtgWIYh1eM6YeWxf+sjPz8cVV1yhIK5z5844ceJEQLbSh4wtFLIIYQRkjo3jIHh+8803Cm45DvYt45F7xbYIyDJuAWS2LVpegTdCL+vLwXQenAPHyfzrrrsOqampeOmll7Bq1Sq88MIL6N+/P1wulwJqlmXgwTHIwbZ4TUDkITAp4Mm5M1/kJn3//ve/R3Z2Nvbv369gleU4dvYhbYicWJ9yZpuUFYO0y/Isx2uWYVnOR+4L7+mwYcNkuIGXDpZjkIPjoskDY9ZlLPNlGeO51GEcAN5AopFc9URJCge8LCL5rSEW0JW4PuD1oBKhwEtIPPfAS3+73TBhWV+kL+6L9IXXI/2hwVj2l3nYXrIex3EAq3d9ho83voWPNr2O5dvfwP82/QMf7PwX3t3xNyz952ykZQ7AmIV9wJ3jUnO4A1sfpekdnZ2ib0oRzqShJQEvodEAvGLLa7ei2uGA1+1GWXQsdlrt2NbhPBT07YudY0Zj0x1TsGranVhz3934duZ0bMjIwKrknljb8RIcdXSCz3Ieqi2xKLFYcCKqDuB1aL5rw0FUQ9PONfD6bZpbLtic+iK1Niiyt8Xu9onYO7A79s/7MdYvmop1C6bi6zk/wafT78ZnM+7DuvkzsHbmXcifcS8OTExF3vc7YXeUBaXOWBS622Fth05Y/v0r8e3Ysdh5151YPXUq1k2bhpUjR2DXqFvwadL52BaXqDajKLVZURUdg6KoOOyNboON53XEqoGd8cWEIVjzkzuw7u77sGX6LGzMuA1rr+2LjR0uxY64OByOdqIy2gW/0x4AXqM8RSPKe9FcgTf0dyQvRdxY5bjLip1tY5Hfuys2/ygNa+ZOw5eZ92Hl3Hux+e5p+DZ1AlZf1Q9ftb0CWx3tcCI2SS3WLHfbUBrtREW0S73IhfYR2bVobk0N79AZ72Hw7C8x8KGVGDB7OYY+uBzDZ36KEdO/wLDpKzHsgVUYOvNLNCUNr4AXOUKAhhCXmJio4HDevHkB+CMkCcQShgmP3/3udxWlEHCYJ+0R2ATEmEeoIngRtngcP34chLiHH34Y//vf/1QeIZPaSvZDgBLoZLsCVKzPa4Lnj3/8YwXdY8aMAQGa9dgHD9YVEJP6LBcVFYW4uDhl/8t8Kc8yDNI+80Qe7JuBh8zlX//6V2B8TGPZl19+WckkPT1dlZV/ZOxyzVigWuZI6Gaf7J/lJZ3t8pqylDTOkwfHK2msyyBzYD7z5H4wpmyNLyNsR+4b+2eZmJgY3HLLLYExcJw8ZExsn/XYNs9ZR/pmW3IYzyWNccCkIZAYSq0hQFujXOCi9ZxQkDWDpuGlt10vHRXThteg4a0LeI1+chtv2hCJhpf+cbUNJmjDSy1veuZ1mLBgKB59MRP5FZtwyLcbL/z3D5ix9C7MfvQuPPjLOzHrl1Mw/clJuPfJ8ZjyyM1IXXQ9xmb1x9isvlrI5vbEmnkDd24bvSQljA2vBrwZ2S1Bw1vzISvaV/rhLXXZUeZ2qlXsxVGxOBwdj4K27bApqSM+T0jCJ+064N2YNvg4OgFr2iZha2IC9sTF4IQlWu3qVWV3oYwuznSTBpov0GxCvDOwL/mUHhkg1ByzEdAEZs7WxhNKm8yFbW4LqhzaVrOwJMDvTkKh3YZNDgs+iHPhraR4vJ0Qjw/bd8AnHS/Bex0vwhvnd8QrbWPxccfz8FlsFLbEWXAgwYLCKBuOxcVjhzsWhy75LjbFdcSHFjfWXnwlvr7kB/gsPgnr4hOxxRWHfVFtcCwqBkXuKByPjcN+RxQKXW2x2+LCrjYx+KqtGx9c0AmvJp6Pjzt9FyvbXoItto44aO2Asuh2yi2Xx+kAvUy0VA1v6G8r8Bvh5ip2G0rd8TiU0BGr4hPxRkIc3rqkHd66NBFvt3VgVZso5Mck4JAjVu1WV+V2okhtyGJVu9+VWehmj7/rmr/JyK5N4M245wmkpP0azRF4jeRAgOGz9fXXX1ef6Am0BF7CDqGGkMlzmhNQE8jP3z/84Q8V7BGCRFNKbesvfvELPPDAA7jzzjuxcOFCBbVsn6AkoPXEE09gxowZoHkA0wWSdu7cqdJoHsBAW13Gd999N2hisWHDBtXX+PHjFWDefPPNePvtt/Hcc89h5syZasyEacIa26RWdPbs2bjssstU+fj4eGRkZCgNMeGeB0Fz27ZtuOOOO/Dss88GYJfz5kGw49ylrAAkrw8ePKjSly9frrTit956awDsVYbBewPrEWJpt/zII49g7969+PDDD5GVlYW77roLmZmZyraZsuJLASGVxx/+8Af85Cc/CQAsx3rvvfcqEw3mc/wcK+2v58yZg5///OdKRpw/IZf2xXy5uO+++5Qcf/e73ym5cE6sK7LnPaeJCA/eT46VY/zHP/6h7iPrP/jgg3jttdcC9dgvg7TBusZz1Zj+T4OAVzWgg2+gsoBxIKF1nFCQoYEmDTWA16sJnCYNQeA9hOde/Tlow5u6mNrQFN0lV4jNbcQ2vCH1DWYMYg7AuDaThvSc7qAdb9rCvpi48EY8+sJ87Kr8Bgd9O/DkC0tx+6xbcdvsEciYNRATFgxGRuYNGDW3N0Yu7I3Ri3ojPfc6TbOb1QtjslK0rYt1G15l1hCyWE7ci7UYt2TGh7XNqswNCLz0+cpQTtMDmwPFNicIvvQ/utvmxIl25yvPAgccsTgeHYdj0Q6URDlQZrWi2uUGQYqbHdB8QYGtbCN8mrwzCFSca+D1cqtku0Vps70uJ+BMgMcShSqHHSVRTuyPi8WBpI7Id8dhf3QSCtt0QkFsO2Umkte+PQ52vAC73S4cirfgSJwFhdEWHHHZcdDuUn6Mj9kScMLVDocTOqEgpoMGufHtUBSXiMNWJ064olESG4v99CkbE4+quPaodMaq+1YY5cT2Nm2xPj4R22KSsM/dHiW2DvDbO6LKEq38ItPUhOCugNembS1N2VKuobIlLIrcjXFTMmngGI3BOE41L/mKwYWUdjt8tliUWeNxPK4D8qPisSnWjfwL2mJP+xgcauNSL3zcjtjvcKKcrueiaNvuAFxJ8Fvi9S8k4eUS2nf4axN4mzvwGrWxpAguxOrQoYOyjyVgEopYhgeBTYCXWl7arxKKeBBaCZ4DBw5U9QlPtPPlZ/4uXbrgmWeeURAnz++LLrpI9XHNNdcEPo9v2bIF1MRKHm102Q7hmnH79u3x4osvqvEQENn+97//fdx4440BSGe5jh07KnAlzBGg2Q4Dx0Itr7TLNngQ5n/729+qPgRYOS/Ol4eAL9vjwTymiQaUYDpp0iS43W4F6yzDulKP5Y0Q+LOf/QydOnVSLwQcO+1maUrA+f3gBz/Ae++9p4BTXgS4MI/z4sEx7NixQ9WnWQi9VbBt3ieCc5s2bRTwsixfLjivrl274tprr1X9XH311bj88ssV+BKQ2YeUpYaX8ufYWXfPnj2BlwWabHBBG+/ltGnTFAizHufIYJyf8Vw1rv8T8NJgTFTnQe3wSVmtOYFa89DAjTpouODljhP82wsAL217y3WThiM68N6I1MU0ASDwhsDqmbiuBYCD/XP74BRMyh2MjLmDsPRPD2J3xTqUYCeefH4Oxt3bF1MXDMHk+Tdg3Pz+mJR7A0YtSsYtWV2R9rMUpC7ppQU19u7gdsnpytVZN6TldFVbKBshNwDhgbnWXGzXvGx4jQ9q3cbWalOfablDGgPBjbtScftcbm9LZ/30wVvidqtQRrMHpwNlLrtatc7yBAmv3aqFwKr5xtnqhoeFk6FMaV3Pklsy9sVP44T6KgdfFmxqAwdu4qBk53ai2OVSGtgSdzTKXFrgObdrZih2RSl5Ko26y6rkSHlSxqXOKJS7olFsj8JxZxRKouOUiUm5O0bllTldKHHaUeSy44TbjhMuJ0qcDHb1ssK0o1FuHImOwfHoWJS4Y9VWx9X2aFRbXWrRYIXNCoYqqxa0l5PI7lVTAN7afh/h0gWIOVduiFLmiEGRIwZHbC4ccrpQGB2DEzGa5twbFQePMwZV9ijwqwW3YWZQ1xaXufFEBA9TPsQJLgIvLcWGlwAl8yLo/POf/wzY8FKrynzazPIgHFHzKMBIOJODXgsIZswj9BJMqS2+/fbbVRohlC6veFCDyYVY1BQPGDAgAI5Lly5VYMdFc/R+wDZ+9KMfKc0pwZbeCo4cOaLGQ+2xjIOa5l/96ld45ZVXVLsET2pyucCOsE6IJ+glJSUpeP7Nb36DN954A1988YWaE+fNxXocz+TJkwPjEa0ux8wyvGZMOXABGc0YqP2cMmWKAs1Zs2YpTw0iT5GNEQCZR/hnX926dUNubq6yA+ZCvsceewyxsbEYNGiQglqWZZ+EUM5JwJLaV2qxmUYNNw8uyiMAjxgxArt371Zpb775Js4//3xlpkAw3r59u7J3pmadsqA2m/eCMmJf7HvkyJEBUF+3bp2SI7W+1NwTtNkP5cY6rEt5GP82ws1Z0moHXilhxjUkEAq7vNaAtyoC4NUXYwWg7wyC70nA2xupORJSkJGruSUbt/B6jJ8zBI+/uAj7qr7BoaoNePqvCzD1wWGYOvdGpN7bB7fNH4gJiwYgPbsfxv2sD0ZmdQZdkakd3LK7Ykx2F31L4muRnt0Z3JaYu7FpwEsThuC8BWw1s47mumiNwCsaJi0OLF7jAjOrDXTQTy0gA8FIIFhpcJ0OVDr5eTcYjKBLIBS4CAcepyMtVAt51oHXqsmEkK8BrwXldHtFmTkdCv7lJSA01l4MWEYL3ACCQW2IwE0R8qXrxAAAIABJREFUHA71olFO2dvtakc0j4P+jLWXEO6Qxl3x1M54TpvyL1yqx6Kh5zXzKx12NR7aZvOeqgWE9EVrs6LcAL285wyR3JvmBrwyN85TydbhUgsoSyw2lFgcqHBEKcjl5iEeZxyqHNHK40ilTQNdj8OtQLkq4LvY+OIY6bn8/dVXT8ppNvea7b3+N0uttdWGYrsFh6Pt+CrRiS0TbgbyNgAVxlXrVK3xE7O+OcJZVgoZH+o8bynAywes2HASXuhujMBJmKSNLoGUAEaYJUzdcMMNCrSo4RUbXoIPAYxaSHpfIKjyYHv81J+SkqIA79FHHw3ApHhp4AIwQjQPLkAjCA4dOlRdE/AIlmyXWkkBZrolo3aW6RwDoZNgSGijWQA1ucx79913laaSjREMqSmmBnT16tVqbEznveT4CYmEYObxYFs8RAPKcnLQ9pdj5JgYqBklQNILhGiBWZZ1pJ4xZjmOj6YWdGVGOfHgHK6//nqV99VXX0l3oNaZ90PmSLnQVIML8Qj2NA956KGHlGb9rbfeUsDKsnxh4YsG08TkhOOgZpfjZbvffvut6ofpHBPhnXPnmPbt26fSCMFsTw45l/GwLuvIHFnOeC71TOAVSTQwbnbAGwrVAp05GoASSCfk9MX4+QOQOn0gsp+dhZ2F61CJw8g/ugYb8j/BN3s+w5aDX+L9Da/ilS+fw9SlozBqTi+MXdhTbTxBoE3L1jW6uRr0puV0Rmp2F4OGNwi7Qc22aHdbDvAqO1va2oYBXgHfGtDrsIJbB0sgcBpDSwZezo27k1EugRcDS1BTqhb+6RpzAgnLGWOeh75gSD7LUs6EZ6WB1YFVpdOtlpUadB2y7domCmojBR2ABYQZM11gm+Okz2Xpl+2IhlfGxzEIFDYkbq7Ay/unXuL0Fwua45RZuQObCwGotUcpuK2wOkDApXkDX1AY896zjYbIqPYyArL1tSPlTOBtSovWBEwEeAkp9NLAz+2EH0IWIZEASRBloBaQeQzUuhK+CIzUGjKN+dTe9u3bVwXCriyCmzp1qtLQEpQIiizfu3dvBVcEJgIvQZqf4AlbbPfJJ59U5WiD+8knnyjwoocAMWno168fNm/erAiC4//Pf/6jtJocNxfEsS+aHPBzPMfPsVBTyf4EAo2QyoYI4GyL9QT+CLlMk4VuHAttY6nxpM0y53nppZcq21qBP8aUDw+OQ8CW2miaY1CbzoPzlLHQNpdy4TzEhnfs2LFq7CzLNqQsNa28V7w/lA89V3CMPGiOwBeUSy65BIsXL1YeJeg/mQvu+IJAEKZJipirsA77FY0xx83FgLwn0dHR+OlPf6r8HUvfzGfgeDhPmbPqvJZ/TOCtRTC1JTd74BUAVsCbjPTsbkhflIz0+f1x24Lh+OU/srG7fBOKUIDjKEAlClGMwyhCIQpxCAf8u/Hh1tcx96k7MGHhDWrDCmpu1QYWbEvfuY0xF8ZpGtzkgHux8LDbsoA3FHoJQAJigViZLVD7q8GuEXLlXGC38VBQOwycSw2vmp/+YhB4QSAA6755jZpUyTfGPBcNO88liLwZ018s2+M521Py17cANpYjrAokK02zbpKigM4ehG3pg/1yG2ExZZD6bFMbV+0yD4W35gy8XOCo/YZtAVkQaKttDmXywcWWHotND9p94O9bybHRsEsZy2+gPnlLORN4mxrw0lbTCGNiw0s4JFRS47lmzRqsXLkS/MRN0GMeIYvAxINgRFMAfmKnBpXgJOYGPCcwMSZ4cZEXQalHjx4KbunLlrawHAcXcLEcoZRmCnRZduGFFwZATGCTWlHazLIsP7eLlpLgyM0wxI8wwU5gdsKECao8tbHUOvMgpFGDy755LoBJeUi+QKpKAJQ5BdtkHSlDCPzyyy9VvwkJCUpeBEnCJ8syZj8cHw9qeCkTLh6T9tkn5UK7ac6LcMq6TJ84caJKYzscJ9thTLlxUR/lzjlzMw8e7Jsw3L17d2WzTLilr2AGQjnL0paZQExNscxFTCRkTGyLLxPsn/bFfEkhfPN3wP4FeuX3w7S6DhN465JOmLzmB7xGLarxnGYU3TFuSTIyspMxdn4K0ub1w4xfTcJTr2Vj8e+mY96T92B67jRk/ToTr3/6Br7auRqHqg+iBEfwdcF7uGfZWGQs6Ku8PIzN7I6xi7spTW9qbjekLemB1KU9gptQiF9dHbjVYjm18YaMSTPrEFOHsVJe10iLHTDHyjAum6YS3XHrkp64ZVkyRuZ2xeTMznho+lV4f85g5A++Crvjtc/RoYBxeq/lQVozFjAyxgJDAloKwGRL4pDFQmcSco3zbwrAqwGiAKtVgaS8NNQVa7BzMugYXxTkXKBSFpdRBkwz3h+ey70RgBVYlnJBwNKAV8pLHCx38rhqyN1gJy1jIwjKuAiSCgytFpTaLDiY4ABuHYBdcRYcirYqrTPHQtdnRvdnxj7OxjnHLuMUWRs14CIPvhwoeRvmfXrGJ393dcs7eN9M4G1qwEsQI+AI5BAYCTeELnpYoM2sHAQbwh1hlxpKMWkgxHIhE+sQggnNXN3PT+fUphKadu3apRaHCYCyLsvTpIEL4QhLtGElTBPM2rZtq/K5gI3eAUSLy/q0KebnekIbNZL0hcuDQEhzCtanpph2uYQ5gtmQIUNUe/yUTz+8hGemy7wZi10q2+I8OSYGtiFQyP65yI0Hx8F8gifr0/yDY6JWmgfzKN9Q4GU+y9FLBMtQrgw8FixYoF4WaI/MekxPS0tTcuE5++G4WY8vHwL3lBs9OTCPB71ZcKEZNeAfffSR8kKxdetWFdPLAwNtelme/fDg/SAE8+A8CdbUplMWNC2hPTaBniYuYhbC8XBcHI+0oxoI848JvGGEUldSywFegmY33Jp5tVpolrG0D0Yt6I4xC1IwIXcAUuf2waTMoZi8YCSmzBuPBx6eidk/m4vVu1ZjT9lOFGM35jwxBZMWDcC4rD7IWNxL8/aQzS2Mu2FMbjeMWtINo5ckqxAA2FYMvPLwrxGHAd2zBbsEjnMNvEEQEXCJNK4bdAQIOU8GdW3TQFGbe83+atwbg9a4tnGyPGE3tF59MBeAQx28BRxZj+fNBXiN86hvzqH5MDW8dT1qTsrjw5wPdXmwtyQbXoIewYVzJDCKSQPdfImmkjHnzs/9BCMCFheCEQQJRoRVpnPnNG7EwIPl5aD2lEHAjuYQNDug+QOhi2BIe1SCIF1fsV2CNBeeEbiMGlKOgX542R8XyBntXWkKQI0m82iTy4Pzoo0r09g+NZSSznkTzOXgmAUCjeMnALKsgDDbFAhmOssSePkiQJDleNmOaHV5zrmzHk0aOHdxgcb6LM826LGB46RGXQCXWmyOW8bGfgnZdKNGW2i6duNGGLxvdHnGPvkScdtttymzE6YZ58K+5JC+WYdjJ1wzTV5CWI7j4AsC69G9GV94qIHmwfmwbcb1HSbw1iehkPzmA7yiOZW4mwJcQq6EsQTSpV0w5pEuSPt5MkYvuxZjHr4Go5ddjfSHr0XGku4YObcnJmaOwLC7bsD4mWl45bOXcNCbjyLk4T9f/h535NyIybk3KDtg8a1LF2h0STYyp7vyxTtqaYqKCb1i0tByNLx1A1foQz6S67OhuTv3wFu7/Ixji0RuoWXDQq+VWxrX3ndoG3VdR/KCIoAoMdsl4DZX4K1LLuHyKPPTJXetfXlhqe9eSjlTw9vUNLx8xBKgBNoIvGJGMHfu3Bo2nixD0CKsEcrEpIFt0ByBUMZ02n2uX79eaQm5wIzeAvh5nRpJOajhJTTTHZf4+KW9KaGL2lmBXwIdoZZaUdr1EhwJvLJojYDIBWcELkIbgZdaT47xv//9r9JQsk+aD3BsnBvPWZ5QKQfHxnyaPvAQ2BTYk3I0k8jJyVEaa+YRyBlzvjRTIAzSftbYBoFQ5MtzAirnSEBesWJFoCwX5fGFgeYeNNuQg4vGaCLCubMdzpPaa/Yni8yomeecaatMMOX8aFtMeS5btkzVYd8Ed+bR6wI9a7BNXlOTzz5omiKmI1JeAJnzpJ9fanllYaLMjW3Ud5jAW5+EQvJbGvCOWtYFNy+5CrcsvRIjcr+LWx6+HDflXoy0x76PkTnfVV4cJuQMwu2LxiBtxi148sVHsad8M47jW3xz4F3cvnAgJizuh/H0yZvVC2nZPZUZQ+rSnhiztCcIuwK83IhCgFfz3qCZJdC0QtKbn0lDfQ/a2vPDPfgFziQOBw2nM804BkIYP0+frY0n6gLF2sBI0iWuTxYsp2SptmYWMwYtrb66zK/tPtSWXl+bAroSs3xzBl7KgXOgnCWW83AaXLlvUqY+edWfLyBb+9+Z1oaUM4G3KQEvIUUAiCDFa5ojUENK+CHYEViZRzDiQeAlGBLsvve976k0ghA1guJWjHm0ByXUcWczghnhlm3zYHsEO7bDxV7UHrMNbrBAQGM6tzcWsGZMAKbdLmGPpgR0S8Y26S2BsMk2eRg1vEYbXgIi/QuzbY6Lc2QbPDh2QiH7po0qzTGMB2XE8THQPIC2rJw73ZARnvliwMV6HA99EfMQjTjPOTYGypeAyHlSLpwj7ZW5mI/uyeQlgOYMnCdlzb5pSsBxsy4DNeVc2EfTE3FBxpjzodzp55cHzUhofiByplcIumQjFFMG7JMwK7bLHD9NTHgw/U9/+pNynUY7YWqj+ZLBFwaaPdBnMsfC3wXHKvJXlWv5xwTeWgRTW3JzA15lS5vbPbCzmuywprS8S7vj1pwuGLWsG27JvQZpj3VFxi+uxbgnrsaI7AsxYsElmLgsBWPn98YdWaOQNn0YfvXXJdhVtBbF+BYfrP8z7swZhrFzkwPAO3ZxD6Qt03ZYoznDyCUa9BJ2TeCt76F89vON4NGUgLd+0Dk7sjKCaWPGJO2ExmyzOQNvUCYEykjuSaTla2tbQLa2fEmXcibwNiXgFQhjzE/1PKgVpd0s4YfAS0gjvDHwoJaVdrCEKEIXIZAH4Ydt0BUYPS8QvBjoB5a7onFRF+GIAMeDWky2QQ2twCQ/yxM66RGA/RGkaDsqNqzUKNMrAkGQdr2sz618aYvKg/OgOQHNJJhHrTPbkDxCGwGTmmgCJ4FOtJf87M867IsH5832OC8eLCdgx8Vh3K2NC+BYhyBN8wLCsMxPXhDYBufH9iSP/oQJ8dSS0p2beKxITk5Wi+5YlxAqbRDC+QLCdjiGRYsWKWClaQQPuQeUMUGcmmPZAY4vLI8//rhaAMh5U750Lce+qcmVuuyLY6KtMw9q/Sl7+goWF3K874RzaoZ5UCasx7nJYTyXNIlN4BVJNDBubsCbqrSntQBvbndMfKw/aGowKluzu7110ZVIXXItJjzcDRk53XBbTn9MWjAQY+4ZgIz7huO9r1/G0eqtKMdW/PqFWbg9cwAyFvZW3hrG5fRRGl5qa2nSoDS8uUHgNU0a5OHbdGITeOu+FwKokcHcyW1KO6Fx8wdeAUmJT557TdlJOYnrK19ffkPbkXIm8DYl4OVjVyCMYEfIIcAIBDJfFmjxXNx18ZzlBJakPK+lPZYh7MkhwMyyLGfMI5zR3pZaR2qHxdRA2uU2vARLem0gUIc7+EmefRO4BHIZc15sh/3LeKW+wKyUl3TKQeyGCZ5sl+NlOwJ0nD8P1mU6r1mGMQPbZp6cS9uMufEE58rtkHmwL6PcZJzUZLNN49hDx8p6BFemh8qVY2V9mSf74jnHxLI8Zxm5VoPRxyP9GMcl+RyPyJntiEwYy7mUNcYm8Bql0YDz5gO8spmF2PCGi5MxJjMZYxf2QmpmX4xbfAOmPjICU5cNx5TFgzBx7g3IeGAgfpo5DtPmTMKcpfdi2/6v4cVeHCr/Cvdn3Yz0B3tjfFb/APBm5PZSG1HQNEGAl5pdwq4JvPU9vM9+/rkG3lAAPF3XNSErKNfQ9kPLhebXdx1aP/Ra6te26I3lm7eGlyBJiBSg1GKZdyA2LADUFvhpvoxD5RX5tfQbvMfh25ByJvA2JeAlrBB2BFIEgvgoFhgKfSyzvPFgHR4CSGyLkMXAg/kMcs00OSdAy8HFUDR9IAjSVIALyQjb1ERSS8x0bspA8wP2Jf2wvvFc2pMyAnvsU86NECfnjEUOUk7aMqbLfI15tcnKWIb9U3aMqZnlXOkTVw6mE7Tl4DUPY38yJ+ZJupTjGGXcbEfGbLxfMle2K/UkjXUlyBjkJUXKGO+xtCF98pp9Sr/ShjE2gdcojQacNzfgpccEhlRd0ysxF5bRd+7YzGRMXjoYdyy7Gcv+NhMbjn+EDUc/wvPvPoFlv5+NB5dNxaycO/G3//wBu45uxjFvPg6UbcCbn/0GP80ahlEzkzFxyQ1IW6TZ72bk9sToLLol02x5x+T0DMCuCbz1PZTPfn5zB17aHIe6xSJkhYee4M51AmKh5SS9oXFo/dBraScUCIPXLQF4BSYlPlnOoV4sfBYTeBvwuKlRRICCD3ietwQvDQI9nJMAlECPEWQEdIzwRK0my0gbFJbUkZhljAfTjf0wj1Ari6QItjSDoDkFP73zMz4/s9NFGT/3ExA5Fo6DbUk/Alq8No6R7XN8RpBkmkCZjEXSpD1e87wuCGZdaYfljQfrMt/YnuSzzi9/+UtlUkF7Y5bh/KnllXkYxyvyNY5F2uJcmR+uH5YJnZ/UYyxjNJYzzkcg15jPOlIm3Hhk/MZ+jOcm8Bql0YDz5ge8dBPWI7AoLLA4LLunMj8Yl9ML6Zl9cNP9PXD3wxlYUfAuirAbJ7ATJdiNKhxEFTee8O9FoXc3vtn3Kf7xwdMY/0B/TJzXH6PnpmDisuuRujgFqVnJIPCOXaztsDYmu2uwb9nhTTa+CAHwwLiWJGu+e0Uj3OT98NYOV0b4EfAJjY1lzsX5uQbexsyZshTgNUJvQ9oMvQ9y3ZC6DSkj7UkcBFyBQolbEvDW/rcgcgjEusa3IbKsu0xQjg0rZ2p4m5KGt75HrsANywn0Mi0UpAQyBZKMZaQNYx2WZzo1vAJ39HbATRCysrKU3SgXR9GWuEuXLsqWOC8vT4Eh67ItaU/aD50L0wUWjXkyDyMkGsGcdaRtYz05Z7tSV2LmGdONZSVP5slruvSiRwou4pPxcF7GMtIGY5mHxBxfuHkb5cJzGR9j45x4nyRP7pm0HdpvaD/h5il1mBdaXvIYm8BrlEYDzpsb8ApIBmIBTz0mDKdn98aErEGYumQMcp+bhb+9/yxWbn8L2498gS37PsHOwpXYtH85/vnBb3H/w7fh9sybMGnRQKUZTs/uhwlLr1NtcHMIAq/aZji7mwJfapG1TSNCtxYONbForhtP1P6QNz6AAw/6EL+7xjLn4ry5Ay/lKtDL84bK8Ezfj9D2Wzbw1i/3UHmIxreh96v2cibwZtzzBFLSfo2hM97D4NlfYuBDKzFg9nIMfXA5hs/8FCOmf4Fh01di2AOrMHTml2hqwEvgIewwCAQxZiC8yGftUJDhtYAizwV65TEubVFzKYf0JdeMWU7qCswyPRyAEQglXWLph2MwwqMR8FjWCHnG/qVvpgn8ybnMmX1IP8xjuuTJNfMljefSLsdkrMt0GSfHyD7lmjED22E5Yxvsx3hwTiIDtiPnLGMEZxkT0zkOYzmmybX0xfJGOUif7ENkKmUlr6GxCbwNlZRertkBr4At3YWp0Atjs3shNae30qSOWtIZt+Z0xpisXkhfeD1GzuiL8bMGYfLsQRh/Xx9MmtEXGff3wvjZ1+P2zBtxe84IpGcNwJjFfXDTfJpK9MXoxSlIy+2jzBg0wO2OtKyuKqRnBYGXWxALeI9d0h0MYmIRTG8ZGt6THvAhoGuEtLrK1v6grx8yGlK3pQCvyLAhcz6TZWQcoXHLBd4wv0O+eIQLEXlxCNNu2Pom8DZn4BXY4ePVCEa1AY0RpKSuxNKGQLBcSyztS0ywY1lCFD/ps21ZIMdzpgukMpZ6PGd5Wcgl6dIPYzkIeEbYZDrLG8coZQXmeC1zMtblOccseSzHsRivpX1p0yhHti/XrMMxSF3Oh54n5DD2yz7kYHnjCwTTZf7SppSVmGM2zk3S2b/UlTSWDT1YRsYj42cZSQstL9ehbTPdBF6RTgPj5ga8abmE2xQddgV6eyrgpa/cUUuvQeoj3TEqqwfG5V6HSbmD1aK1CXP7IHVmN0zJ7IvbFvbGuAW9MfKhZIxe0Bupuf1xy+KeuP3JGzE6OwXD5l6r+khfSrMGzbeumDVECrwEX+WLN8SkgenG7YWbztbC4R/MocBzqtdnEs7Y9pkGXs5b9RMC/CKPxs5P2pG4se01tr6MIzSuD3iNO6txEVuV1QKPTbOFbcpbC4eVl2jaQ+OwwBr+7ydsu2HrRw68tB/WFtppi+14r7xWO4ptVhyOseGrRCe2TLgZyNsAlBcHH8o+P+CrBvyamyg+C87mIQ9+Puh53pJseAlLRiji/IxpRrhhOgMPYzqvjW1ImdDyodAk5bhIjfUFlKSeQDHBj3UJw1JGYulXrlmX53LNfOlH0oy/HeOYWM44L2NdqWNsm2nSnzFfzqU9Y5sES14zj4HXHAPnyLaZF9qHsb4xj+kyf/ZpnAvLyWE8lzTG0q6xjVDwlTIsL/ORNoxjqSuNeSbwioQaGDc34BXN6Umx0rZ2x+jcazFmSWfQ3nbU4q4Ys6g70hYnq22CUxf1wG1L+2lbBi9Owfgl/ZC+pB9GLeqJWzOTkbqkTxCkczRbYZpIGAMh9aS+A3a8hFvR9IaUE820Dr6EYIHejOxkCPDSz+/IZckYmdsVkzO7Yvb0q/C/uYOxa8jVKEiwo8JpV5sHnPwAbeiDsrEP5KZd32fhjmNaqLZY4dEDz7V0G6osBAItZnq5w4a9cVYc7BQHDOmNg9FaGmwOeBVE21Bts6PabgVhQmR/MgQG86RM64u1bYkpX9mimHGl3QaPzQbKu8xuxb5YK3DrAOyKs2C/24JKl0PlneoGGC1FzmIaIXFt85J8wi7h1mdxQoGvOrfBY3GgxO7AwSgL1rWPwqb04UD+xgDwqoc1gbea0MuntB4a+Nw4HcU4Bj74GXjeEoD3dMilMW0QsiQI/Mk1Y8qa6TwnyDHwPBS6GjOGM1lX/W5DOuDYCZQEec6P85FrAi+vw9ULaaZZXprAG+FtazHAq6BT88+buqSL2phidFZnMGibU3RTEJyxrJcGsNk9kb6kN9KX9MWYrJSAGYMykwiBXCPw1gW7zAsFXtHiUiutNNMG4CX0Sv5JwJvTPSzwljo0LQ4fhNRmMlCDJho3SWu9sYCtVQGUEXgJWwK6Ep8EvINScCDKoiDYZ9cgjHDB8myLshbZ8pyaTAZjuuS3xpi/w4C86NhdB18CL0NdwEv5+m3Bnc4oPyPwtXR58jdE+RhDuDlLOe23zd+lDV6LU4Gv30oA5sucC6UOpwa8HVwa8O78RgGvD9TUUevmBahYFNjVFL0RPkFOvbgJvKcuu9pqCuTKi4TEki4yl2uJWa45QCHHGDpO45yM580J5Gu7n/Wlm8Bbn4RC8lsa8HLHtTRlS0v3ZfSqEOrCLDkAvARQ2urS/peB5+caeEepjS2SMToEePOHahpeAV55EAp0yUOSGsnWHES7y5gyqbJqISAfahsJqraghrfUaUNBnBX7L4wDBvXEvigLmCbAK+2wHmWrAYd2Tm2x0hi3crnLby4c8FLWAryENKOGNz/eouRd7naoFwrRmouMQ2PppyXHdf2egvKQrxc2VCnAdcDDrxC28MD7TcZwQAFvEbzww4dqVPu9AF2+EnSDX2pDnhBn7lLgRGDL1PA2XtaUaV1BeggtI+lm3LwkYAJvhPerZQFvMrj1sACv2m44xF0YtbVKS6vbAQvoauAbXIRWnya3tvzGanjp55dbGBuB9925gxEOePnwEw2jpu3RNJDBh2IQzlpLWkOB16jhFeBVJg2DeqpP7LUBL+UY+rIhGt7WIuO65mkEXtHuMhbg5bkReMWkoaHAW1ffLSOvpoa39jnVBF6P1RkAXu1lrqaGVwFvHjW8RfD4ffDAC6/PAz81vIRdE3hRzVeBEGAUDSnTeUh+hI9Zs3gTkIDcwyYwlNM2BBN4IxRlSwPeoJcEo8cEnhOGeyrtLmM5p0aXC+EYeF4byDY0vSHAq3Zq0/3zhpo01AW8uxPsCGh49c/ohC1tQZBmI2m0MTV+Dm4t56HAG3wREFMHTbOrPrvrn5DFhvfQBXHA4F7qMzDT+HlYAw7NFIKwFipf0UiGprcWeYfOUzTrRrmLhpzadsqdshUb3t2xFhzQbXipta3PpCG0v5Z3Lbb4Etdm1qGBsdjwVltdqKbNOU1vbDZ4rO6AScPaDi5sTB8G7NwIlJ1Ala8alX4PKn1cIU+N4LlhXgKIEShNDW+ED2+zeEACdb2ItETQlYmbwCuSaGDcEoE3/G5sGvAK+BJ4ea4gV+xrzzDwcmc2wq4ReDkGWbSWkdMd4YD3nXmDsfPGqxEKvISsUOA1tY1BDRmhS0waeE7wUtpGw6I1plObG1i0NriXsuGlFlJ9HtahWEGEAomaWnPKW0Lt2riadVp2OU3ORrlTxhU2LVD+Im/jojVqePkpXxYGioyMLxSS1rJjTXMrJji1z1VewuxKs+u1ueC11wTeMqcLh6KtCAe8VTrwerw+cO0a9Zean4AGPjhOQzETeE+DEEOaqAvu6soLaabJXtY1h7rymuyEGjkwE3gjFGDLA17dRpdbDeuaXNHOhrtW0KsvUpNyjYnr0vAagZdmC+KpoTbgvS2zK2bNuAoCvPlt7ChxaluY8rO6PAwVcOneCGhnWm5tvUHAisDFc4Irg6RX2G2gWyzGZVaLSj/htmFXghV7L6KXhhQUxFhwwmlFlcOu7H3ZVrneFmVLGRPOGJfZtNCaZS5zV3LRbaYpLwbKnXGJQws0bShy2bA73gqMHoAdCRYl7+Joh7oflXYLGCp4j/RgvJa+WnLM32Vt8xOZiFy8fZAIAAAgAElEQVTLbdSY21HhcKHSQTveoIZXgHedUcNbehyV1V6l3a2oroQJvFBmHTTvaAkmDXVBX115EWLDOSveEuZwOoVnAm+E0mxpwCumCoRW+tFVvnR1t2E8l3za8goAixcGXjcGdlk3HPCqNmkzbNDw1gW8o5YmY2ROdxB4H3jgKrw9fzB23Hg1CLzFDm2luwCvLN6hRkjTaAZBIfhwbE1pGmTVBbwKdA3ASwAj8O4j8A5KRkGUBrwepyMAvKyjQNmmm5DopiTldgsYWqesa86bpjWi2Q0HvHzxEOAtSLDVAN6SqCDwUpYCfEa5Ms143TLPgy8K2ktazTmLXFSsXiZ04LU7awBvpc2Nk4B3+wag5Dg81V54qqtQ5aXLJq7ON00aWgrwRvj4b3HFZQFkuIm1RFg2gTfcna4jraUBr7j/aji4nmzr2/C6Ib52dddotCMW21zGRuBVXhhytYVpRg1vRlY3pGd3A/NHL+uJ1Id7YWJmFzww82r8Z84AbB92NfLa2nGC3gMstO+radsni4WMn4Bb4zntbiXQJEE0Xsr+1qK5FlOfzplHN1hWO8pcdmXSoGx4B/ZUNryEM+ZR1gzUXrIt7ihnlKvsMBeabizTqs4NbskocwnGRWvKZjrGovzw7oy2oDDBpex6eV9alazCbV6iu8CT311t8uBvU/mGpt0u3edZNZMG/k4Jwwp43W618cT6jm58kzYM2LERKD0Br686ALzVHl/ASwOfBWfzIICYNrxnU+JmXy1NAibwRnhHTeA9O8BL7S5DfcA7MicZty7toaCXwDtz1jV4fd5AbBt+Nba1seKEW4M4ApZa4EO/pQw6nJ0MwqFg3LKvG7JojVpxAgVNQliecLs3xoJD58UAA1Nw2G1BqW4yorWn++C12c1Faye9aNX8PYn8gzaoNW16KftKh125IsPIgch3W1AYY0eJxQLYnWFe5Gq23/J/3/JCG9miNW48oTagiHIr+ZZanThutyt79A3nR2PrxFuA3VuBsiJt4wEfNxyo0rw0mG7JlMeKlmDSEOHj3yzezCVgAm+EN7DFAa9h17PGaWrDaW8bkqYBtGh4ZQxG4OVuaqEmDaLh5YI2Au/IJT0wLrMzHph9Df49bwC+GXYlNidYcNRt020jNVtHbteqgm7SUAOCBYZbUczd0SSoRWd2O7z2oNZX+dYluNodCnphtatdvg7E23HkwgTlpYHbsRKCCcXUnKsd1+x2VDsc6uUisBuYzaoWWXm5wI0a4VYk59rmKrJX8rU71IsY7wPtoXkflAlOtBv742zAqEHYFWPBkVg7igi8rihThrrmli+wKtTym6KcNRk7Ue1wwmdzw+Nwqt9tiZMLA6NQHBOFQ3F2rDnPjTWpQ4AtqzXg9fuUD97qat0tmQCvqeENuB2j9jlUA81Hq6RH+Jg1i5sSOCMSMIE3QrGawNsQiI2kTO3AS5hVWwfXAbxpS3tD2fAu6YGMhdfi/tlX498LB2HbyC7YeUE0DlEb5rShzGWtEbjlMAPtTFtzIKhK4EKp4w6LCiKTYrsFJ2wWtYiqiLaiNiuOR9mQ38aGvZcmAAO7IT/WgmNuvR3ddpd1iuyabGWhGtssYvtOC9gug/TTWmOx3ZVFarwXPOciQAaCbXGsU8kbI3pje5INtOc9bNfuSWuVm8xbfkcSS7rEZXYLGES+JXabsusvdjiUuVNRlB0nou04ERWFI24H8qMtWNXRiXUZw4B8+uHV3JLRSwPteGv44TV3WgsArYCt0eSCj1ZJj/AxaxY3JXBGJGACb4RiNYE3Epitv2x6Du13gza8Rg1vXcA7fnE3UMs7alF3tWBt7LIUTM7qjhkPXYt/zrkOa4b9AOvaWbAnxopClwVH3Fo4GmUBg0pjerwNhwkQrTRQo8VwOFaL9yfYsS/ehoOxWqBmkT5gqdFlzHJ7Eh3YeIEDW3/YFp4hnbExyYL9SS4UtnXjSJxDBbZzoI1DyZUyFjkfaGPDvrY2HEzQQmuVO+et5BJrVxpb3gPKWO4H5cdAeVO2my9wwju8GzZeGoVtndzY317T+rZm+XHuBxMcKhyOd0AF/e9YfnOH4q1QQZfvgXgHGPbHu1Ssfq+xdhyOduNQrAt7k5xYc2kc1kwYDmxbDZQWosxbhTJvBcq9FWrRmrnTGkyThgi5wSzeNCRgAm+E98EE3vohVqC1IXE44OXiNPHQUJuGl8DLMG5Jb7XL2q2LuyD1oR/i3plX4o3c4Th81xBU3nA1cGM/YHA/YEgfYKgx9AOG9gNu7N26g1EmN/YBJEj6sL7A0N6AxEwf0QcY3QeYMgSYNwUY3gO4qS8wzCBntsM6ofId3hvUVILxsFYue8pmaN/gb5MyE7nLfeDvlnIa2QtYdAeQ3g8Y0095bNDuVSuWIeXC35iEG8P83uT3R7kq+bIMQ39N9vz/w8BeQL/ewPUpwKAeODa0K/LunwTsopeGIyjyVKDIU4bSqjJ4PNXKS4O501rLcEsW4ePfLN7MJWACb4Q3sOUBb+giNLmuBWz1TSci9+4Qvr1IgZe2vuOykxXsEnjHLOyG0fTYsDQFU3KSMXNuF/xz3vVYddMP8HVHK3YlOZUGaH+iHfsS7dibZEdBOzsKkpxKm3MgyYFWHRJdOKAHahL3tdMC0w4xvZ0b+xKdKt7f1qnSdnVwYd1FTmy8sg2qRnTD+vNsyG/vxL421J7ZVRlpi7I9lKgFnu9rp4X9SQ4wtGbZUy5H27hwuG3wHiiZJwbvw8G2TuQn2rGuk0PB7uqLndjYyYndHd1K89ua5afmnuhScuDvTf2OQ35TB9s5oUKSW/sNJ7mxT4VoFcvve0+cG3vaulHQKQbrr+mIjdPGAgU0aShEsa8KRdXlKKkqQ5UOvDTfPcsmvCfZyJo7rUX48DaLt3oJmMAb4U/ABN4UBGCX8NvIRW9G4JW2jBpe8dIgLsmMwEuTBmp4WS99aTKmZPXA7Dld8K95N2Dt8B9iXQcb9tBkgR4FYi3q0+aBeCv2JzDY1UKgw3FWHIm1ojCmdcZccGYMYsrAtKPRNhyMtqqV68rkIdqq0ijTje2t2Hx5LHBLL3zbwaHkfDDKojw2HIt1aCYSsVxgpclW5Et5S2CeBMlvTTHnWhhlU0HuwZEoq9rt60CMVcme94C72m3p6ACmjMD68+3YkmRFHmUdY2vV8tN+O5pJCBfyaSH83zHzDsXYlE0/7foPxThxMNaOvW6LMt85HOdGQZwDW+IsWH6BCyvGDwV2rgaqilGOapT6PMq0wePxKNdghF3uuHY2j9BFYSbwnk3pm321BAmYwBvhXWx5wBte8yrweebjkzXKArziqYGxgtocbVth405rXLQ2ZmlPjMruggkLrsb06T/EW/MGKT+8O5McaoFVhcMCCeUOC4xB0tXuVA5916pWFMv8JS51WsAg1xVOLkazqAV+XFDFHde4aC2vrQ0Fl8YDw/tgd7wN3H2tyunQNjrQ5a3q6efhZB7ooxXJ2/g747nsikZZUUaUCTfmKHFo96GSi9fcFuQlWIExA7A51oK9CXa1+xrlzXwVeI9aoRz5e1TB8DceTg4sQ3/G3GWtZqCHBovakfGIy6rs11e0c+KbiTcD+RuA0uOo8nrUDmvV1dXw+7xqqzFTw2uaNESIDmbxJiABE3gjvAkm8J5uQK4JvAp2dRteBbq6CYW4LSPsCvBSO8yFbdx4YmRuV0zKvBaz7r8S/50zCHlDrkJBgh2lznCbTrQ2X6W1z1cc9dNfK89lYwhJZ8ytmNWmFBbN9RhlSi3vgYvilc3pgRgNOpTrJ93vLOvRb2+4dlu+b9ja5R1u7rLltfIVrfszpszFdzRhmFpejBoIbjxBbSUBji7iAi7f6vH3G67flpEW3v9u6NwCG1NYuDmKBG2jD7opVDva2S1KA/xVohNbJtwM5G0Ayk7A561W3hmUhwafB4BXPTXOsoLXNGmI8FltFjclECoBE3hDJVLPtQm8TQt4lckDtxbO7YrbFnXG7OlX4d25gxXw7oqzmsBbDwgJ2BIQeG4Cb2SwGgpWp3JdF/DyngSAd/Qg5Ol+eAm8yuex1aqg91T6bRl1zjDwVhRrtgt0Qab87xJ2TeA1txauBxTM7CYpARN4I7wtJvA2XeCdvLgLHpxxtQm89UCuEXQiAV61la2Fn4BNDa9Rho09bwjw0lUcDMBL/70m8PLl5AwDb2WJtjpNbBj8JvBS020Cb4TgYBZvEhIwgTfC22ACb9MF3lANL21LTZOGujWWDQVer9UGAi+3wDWBt26ZRgrApwq83JbYNGloHPDS1IFfNTw2m9oohQvaapg0hGp4dZMG4d8IHx+NKm4uWmuU+MzKpgRgAm+EPwITeJsW8Iaz4RWTBhN46wezhgAvQdcE3vplGSnoSnkTeBsj29MLvIejQ4C3vAj+ap+2w5oya9BseAm81RE+Oxpb3ATexkrQrN/aJWACb4S/ABN4mxbw1mXDawJv/SBxqsBbEGc1F61FYDoicBsurg946b0hnEmDqeHl7/vsAK9PbHgNGl4TeL2BrYNlC2Fza+EIgcIsflYlYAJvhOI2gbf5AK+5aM0E3nCA2dTS6gJev82q3JWZwFvbb/kMA69u0iD/34ff1PCaNrwRQoNZvMlIwATeCG+F/I/PGFf7fahEFbxcvUtNAF01+v3wwwsPyuFBMSpxBM+9+nPcnnUjUhfrmzc0ctOGM+8j93TDbbj2GueWzNTw1gYCDUsnbFHLSwhkrOwZrdq5aH/DmTRQw7v/wjhgSG/U5pYstF22LWlNDTrP5XjqAl7Kq9RmwYE2DmDkAOyKs6iNKUwvDfL7PjPAu3W87paMJg38nz13VlOGu0Hg1Xw1RPgAaURx06ShEcIzq5oSAEwb3kh/BUbQlXMTeMOBbEPTTOA917AlEMrYBF4BqbMXm8DbGFmfPuAttlsgNrwNBV4NhSN9ipxaeRN4T01uZi1TAiIBU8MrkmhgLJBrjE3gbSjchitnAq8JvI0BnuZf1wTextzDxgEvbYDVi57VhkiAl/a71PCawOuvYcdr2vA2ECTMYudEAibwRih2I+jKuQm84UC2oWkm8JrA2xjgaf51TeBtzD00gTfjnieQkvZrDJ3xHgbP/hIDH1qJAbOXY+iDyzF85qcYMf0LDJu+EsMeWIWhM7/EkNmfY+iszzFk1scY8uDHGMx4FtO/wuBZqzB45hcYMeNj3DLzEwy4521cO+pxzMz9M/L3HFeEb9rwRggNZvEmIwETeCO8FQK5xtgE3obCbbhyJvCawNsY4Gn+dU3gbcw9PP3A+3VbJ+ozaTA1vB5Uw/TSECE+mMXPsQRM4I3wBhhBV85N4A0Hsg1NM4HXBN7GAE/zr2sCb2Pu4ekF3qPRdpjAa2p4I8QCs3gzkYAJvBHeKIFcY2wCb0PhNlw5E3hN4G0M8DT/uibwNuYemsBrmjRE+BA3i7daCZjAG+GtN4Iuz31+wOP3wQPtE4+4JWMG3ZJVK7dkRajAoYBbsrRFvZCek4KW4VYsHMSevjTKKT0nWQ/dkZGdjIyc7iqk53THmJyeGJvbG2OykjF5YTfMeOAavD1/MHYMvQq747kNbjhXWHxINuYh23LqErZq89JAGTEvUrdkrCNB2qD3B9MtWfjfjQm84eXSsL/R0wu89NJQQ8NbEd4tmWnSYJo0RIgOZvEmIAETeCO8Ccq/Ln3s+v3wKaTVsJZoW+33wuf1A95qFfy+KlR6jqOs+jAqcBC/eXkZbs+8EROyb8D4nH7IyCX4Eua6t9IgIHtyLGCbntUD43K1kJ7dFWnZ3ZCeq4W0rK4YPb+HkidlOnFuT9wzuzv+nTkUBbd2QUFbC0rcFnjtOoBxRbbFroK2Q1NjHrQto67PAP6EVKNbsgCscmthmwa+1Ra+RNhQEGfD/gvjgcG9cTDaBvqFhc0Bwls4uDUCsLTLtIZBTcsu1xDgPZjgAG41/fCe/HtpGPAGd2Szw2+RoNVVv/sQLw1bJuh+eGsBXnpnoLLjbB6mW7KzKW2zr5YoARN4I7yrJwMvTferFfyqvGqfDrweoLoKHs8JePyFCnh/+/IjmLLwZoyfPwgZi65HRtZ1GJ/VH+Oy+7bSuD/GZ12vB573x8RA6IuJWX0xcVEfTMruh0nZfTEuq7cKE3L6YkIOz/tgctZgTMkegYkLh+C2h/rjvjl98c/5w/Ht0B9gT53A27Ih6mQwCD/fMwm8BF8Zhwm8QVmITCQ2gbd22YiMao/PMvDqWwvzsWECr7loLUJ8MIufYwmYwBvhDTACr3rjRrWm56WvFoYA8HoV8AKVAErhxXG89tHzWPK72cj8zX2Y/+w0zHn2Tsx9+k489Azjaa0wnqbmzblLmPf0nTCG+U9Nxfxn7sT8Z+7AvKd/rIVn78C8Z+/A3Gem4sFf3oG5v75Lhdzf3o1nn7sbHz72I2y54YfgbmDKpCEAXvJwZNyYh2zLqWsC77m/lybwNuYeyN90fW1IOdHuMtbSItLwmsCrHnNen2nSECE6mMWbgARM4D2FmxCE3mr4CbnKBbkvDPB64auuQJWnGH6UYvfhLdh2YA3yijZiZ/F6FfKKN0MLW1pprM0/v3gzGHYXMXxTMxRvwO7iDcgrXou8ovUqFvntKNqAncUbsa1oLfaUrMHRwk9x9M0nkT84Gfvi3Ch3GB+E8tCzwM/P6eYndZjAa/x9nJtzE3gbI3fD33SdL7FSzgRe0w/vKTz0zSotQgIm8DbqNvr4YSsIvD5qeL2Al+YMPu0cPng95fD5K1HhKYIX5ahCKcpwHOUoRjnK9FDRSmOZvxZXogyVKKkRKnACWjiOcv9xlPgLUeI/pkKRvxAn/IU4Un1ImY2geidK3nkO2/p1x96oKJQ7+IDjA1UeePrD1QReJRcTeBsDW6enrgm8jZFjyN91rdAr5UzgNYG3UQ99s3IzloAJvI2+eTr0+n2ADrx+H80ZeO1XWt9qbxWqqirg9VZpnhv8Vaj0VWiOu/XtKbn+ofUGLv/TgvYCEXyRoAbd569SoRqVqPZXweMrh4eac38lqvj64C9HsZ9mI8WAZw9K3voztvTsjv0xcaiwO03grRUCLKaGtw7ZnC2zFxN4TeBtyGNImdD5fOD2vTzn+ugVG/bCdEvWEOmZZUwJACbwNvZXYKRUuiLzeeHzeVVMWKuspA0vUFVF6K2Czwd4PNWo9hCIgepqvxmqq1GtB5+3GgyapwuPin1VlfB6KuGrZvDA4y2Hx1OpXiAqvB6UeipR5ufLRDlQtRflb76ArSk9cDAuQQde0e5IbJo0CMyZGt7GwNbpqWsCb2PkaPibrvPlRcqZGl5Tw9vYh75Zv7lKwAText45A/Bqb+Be5Z5MuShDNUrLS1QPhN3KSo+mwGQdmvxWaWa//mofzKDJQDMF4ZuAHrw++D1e+D1V6iWCKyYIvXyx4AsFi1V4q1EFH6UNX1kBit94Htt6JeNwQluUKw2v9rDzGc0aTJMG06ShTkBqDIRFVtcE3sjkJS9rYU2Var2nJvAOe2AVhs78EibwNvahb9ZvrhIwgfc03jmNfQleDHRWpoXg53rdboFf7LnWjTErqRP5jG/GNeTh9ynApVmIMhXxe+CHFliOwMuNPyr9XDVcqjS8ZW+/oIB3b1SMAl7lEstig9cqfnj58GvMQ7bl1DU1vOf+XprA25h7ICBbXxtSztTwmsB7Gh/6ZlPNSgIm8Db2dtFM12B/S44l8Hr1/4zQq0BOCmsFlZZSAZyf7s08reuac6bGlq7d1GYeWqz83uj2zxQuNbrVHt1UJFCe8vKB2zor4KV9L214qwpQ+s7z2NyrG/bGRKPUYdc2QrBqwOu12jXwNT00mBreJvLSYwJvfbBaV76AbF1lmCflTOA1gbexD32zfnOVgAm8jbxzwq+MdYYNAV5qHqmR9KoQ0F4S6Hx+tQCB5g8Et9YXB4FVWTBQBjRtVgsztJjbN2v2vX4lIy5tI+AGl7nxNcGLSnARYBHg2YWSd/6Ezb27YG9MlNoVTHb+8ths8NgIvNxtzRbYUrc1a3tNDW99oHTm803gbYyMBWTra0PKmcBrAm8jH/pm9WYrARN4G3nrQoE3CL5BswaBXcb02UtEoxaT2k1l9xvwUSDGD60o5k7MumM3xvp7AAi6ErjQTwGxHnsMdRQAG4G3Kl+5Jdvc+1odeINb3ZrAezIUmMB7skzO9guQCbyNuQcCsvW1IeVM4DWBt5EPfbN6s5WACbyn8dYRdnloEBzUQQa0urqtrga6GvDqVVplZHxZkPNQQYRCr5deLXSPb8rqgYpy9QrBZWtFQFU+it/+Awi83HiCO62Jhpc2vJpJg6bhPdtg0xT7M4G3PlA68/km8DZGxgKy9bUh5UzgNYE39CljXrcWCZjAe6butKJfumJgB1yIFoyDwOvXbVcZU6PZGuOgNlubv1EmfuW9gpJTmnCfTzNvUOYfmvkD5UbwpVmDjza8lXkofvM5bO11LfbHajut+WwWZb4gwOuz8KHHB2B9D8mWn28C77m/xybwNuYeCMjW14aUM4HXBF7taWz+2/okYAJvo+95iFcFUVVKzGw5V/Crwxs0sFNOxOlii67JWllMf8XGoOYvPoz1WO1cp2vG1eI1bxWqqz16Pc2XMff8oA20318KlO9C8Zt/wtZeXbA/NlptLSzAq+x2TditAfom8NYHSmc+3wTexshYQLa+NqScCbwm8Db6oW820EwlYAJvo29cLcAroCuxQK/SVmqf4TWNZrU4422VsdJ26zbN8Gu2zTVibtWsrHzpnqwSdE+mNqBQsBwEXpo1aMBbgNI3gsBb4dA2mVCuyZRnBnnw1feAjCw/2H7NekxXJhVWLpjTFs1x4ZzH6kSlzancppU4nShyOlHitCuvEuUOGyRU2G2oZHm12I5u1Th+rY9A2zatDxkDYylTX9z0gFdbTBgwQ9G188a5UVNfabehwm5X8qOv5XK7G6UON4qcDE6UOpwqX5O5yF6LNfd0mteOYDtulDhiUOSIQYnDbahrgcfGEKwr9XkvZJwyvvrkzfzQ+9NcgZfzoCwoG5Gjdk/cKLXH6YEvnfz91pRVQ+TUsDIN/XuWcibwmsDb6Ie+2UAzlYAJvGfqxunaXM2k4Ux10lLa5VsBjzCxMvXQF/fpkKt2slPmH//P3nnA11GdeXtur2q25UpPwkI6CS0B3HsndEJIyMcmodkqNpBGIL1syobdhLBJSNmELAkQeo+7Ldu4995lybKsfvu9z/d7z7lHupLlhqus0Y/DmTt37pR3xjPP+c9btJHFrUEyXEATRHco4F135UfZGw7SOfAePRAezUNXPfgNdLamP9JwmrAcJH1eoj4vdS4X9d4A9a4ATa58moPF7PAXsqNnH1YHg2wuymNnfpCdIS87Qi7qiwvYF/BR4/HRFAjT5PKCO0DaciCQlHBZtDgtWlwOFBi7nCRdDlIux0FQ1XYc5sGv+9MNvHIsJmUUDhcZh5uky6XOW8RtEfdaRBwWabeljqvFsoh4PTSHg+x2u6nO70FlXjE1eX3Z5uvBtj79WRXKY2deAbvdHlpCQVp8ejCRCAfZa1nE/AHSvjDNlpsDXic1eWH2hPqx1tWb7b0vZlthf2oCIRq8LhoDbhr8LjUtAxOBN4G6uMtL3OUm4nQQcWkoFvjtCLPG7jLfNLOcWfZMBl4ccn70vst+qn12OsDlJO3x0OJyUuOwaAyHqfb5qQoE2V/Yn92hS9gVvJSdwX7UFBRR73MR8Vu0uPX5zDgtcJ6If4fmej7SusxyNvDawJt93Nhdt7OADbzd7pR3vQM2vr06XVlbLXmZL3+i7qakbJ348Crg/R3rrvi4Bl6Xk0wObLTz3T0GJdSAS2e9QECr0pcFXlH/RNWKB30ccFkc8Dg44PVS6/ZTF+5JZaAHG4v6sOfaQTTdfgctd9wJ994LX7ob7v4cjRNHsLRfDzbm57E3WECtN58GR5BMIJ+4qGqOLPC6LJrcDqUIC/QePfDKg1+gXAONgRo5Dlm3gTF1bJY+vrhlkbIcKtXbzrCTygF5MOxqqgKitjrB6VYgbmwhvbGXrMes02xLfzbgLYGEWskWtVAr3JaCybjbQdrtJO6wlALenJ/PjqCP1UV5LD5/ADuHDSZ562dJ33U3lJZQ/+9fpOn2m6i+7lMsCfuoLi6iJs9Pjc9NczhMgzdEgzuf5p79WBJ0s2RAH2qGTIT/Nx2+Mp31n/wU6woK2FcQ4EDITX3AoYBNAa9LlGMPcZefmNurBhstLouYq01lN8ec25vjz7WNsceZDLzmGMz+5/YyMIl4AzSEC9idn8+KkJ/l/Xuz+RNXkLptGnz+m3Dz3azv1ZudPgcNQYumkIN42K3sFTUAfVy+9AZk2641s8/te7OcDbw28Ha9Z6C9xyfGAjbwnhg72ms5iRY4k4E3FwA0wGSVXadTgeH+sJs9Xov9botmv59Gb5CacAEb+hRTe+N4+P3jsGYZ7KiCbVVQuRd2bYEXnqZ5+hQ2fOAS9ub3oc5RSMTKIxkuosnlVOpu0qWBMFfhldfvAlV6XzqDgNwH/5kAvHpfzatxAWeB3VbF2uEEl0e5cgjoN3v87AuHWOJ1sPbqD7P70S/B3L/Azndh9yqo2QI122DzuzT8/mds+exEZvcvYkNhgCq/l1ioiIi/mEpPMWv7nM8bH76ILaV3w0uvwLbdsG4Nex+cwowBBawOWuwLy3mzlOKsXsubTB+WT0Gv2P5YgPfg60UGEbqZAYJ8lkGHOY/NTouqfDdMGMz2sEV1QCv6MmARBdaosO0Br7Nz/97nddxvtY8yuHPnq8HY+h5FVFw8gIpxV8GfH4d3V8COGpgzl5WXXsyWfD/7A06q/BZNBV6lmovtzDG/93031/ORjs0sZwOvDbwn8WFlr/qMtoANvGf06bF3TizQFYC37YGtgVdUygavU8FubaGPOq+DZq+XZn+YHcUHDDUAACAASURBVEE/2z90Ebz6NCx+B2a+Ca/MoukPL9I0axHUN8CGlTD3LQ7cfjub+1zAfncPos4imoL5NAQ8GsBcFlGnpV6ry/aMn6SBk7Z9yoWB3Ae/7Gvbd/I7AZBTqfAKrKntOpxEnQKPbWq1HI+oxuLqkBQodPnUgGGrz8PCgJfEV26DuU/Dxtdh6asceOkvVL78dyLLKiC+H9YvhPmv89anPszSfoVUFuRRbXmpDRazue/72TthEvz6J7DoLVi/Bvbugd0bWfP9h3ju3DArCi1qQhYxr7aLtqfYTHywfcScvmNWeM25Mb2ssysCr+y/+JVHvAXszOvJnoFXknzkfnjhv2BnBVTtgJ27YPUK3r7wXDbkB6kL+akLemgMepWftbhDyECn8+u07bo8/Pfmej7S8mY5G3ht4LWfq93VAjbwdtcz34WO+0wG3oMfxgZ4LZUDuNZr0ZLvpcFl0eByUOPzsjXPT/Xll8LMF+Hx7zN74kSWXzOJmZdN4IVxX6DmL8/Dzs1QtRWe/BUVH7iEXXn9aPb3osrrpy7fT4PPIuKRV+kCutmgIFEfOwR5dbZ/xmdWuzS0gYICz1MMvOLLabZr4N0o1gK84kIh+ynAm3YHafSF2R4MsrSoAB64G/72G/Y/eC/rJl3PrE8N47lrxvL8bV+iZW4FHKiBvZtY/dBXmHl+MRsLQuxyetneozc7xoyEWa/B6gUw8xX2vvYK7N8H9XtZ9qNv8nS/PFYUeDgQdJB0tynmylXF4SHqDKhAOXEnEYVXfMVFXZdjOdjmh593JgOvUY/luEzLZH14I14fW/1BGocNht//B6x5HaoWsOrpn0H1HtixGyoW886F72dzfiEtgTzi3iBRh5eUK0DS0sr9sdqr/fIGZA9v47Zr3gZeG3i70MPP3tUTagEbeE+oOe2VnQwLdAXgNTCgIvdVFgENQTGfRbPLQl5Lt/jc7PO62FMYZPv5veGz17Pkg+9jRnFfVuVfwLKeH+KZfh/kzdu+QGbJfP1qfuZbzP7kFWwsGkBjqJgqt4eGsI9Gn0uBls7eoAtpCBy27kcHn9k2SDCAoPvTrfAK8AqkK2XZKYq1QweCOR1qWvw8U263KhjS4nBR5/GzNy+fVaEQDddcTdO40Sw8/wJWFJ3HiqJLeLPwQ/zpnKtY/71fw8btULWTyN9+y0v9e7G+KJ99oXy29+pJ9Y0T4O0X4D+/x4w7buaVhx+CvXth91bmfn0af+/dgw0FedR7PUqF1Oc162Ps7J7AK2Au15cBXnHR2VRQCJ+7Bf72K2JPPMrSh+/isUEfY++8ubCrGpauZMFHL2NbUTHNnhBRy0/C8oIzTMbynIB82OZ6toF3xNS3GFZewZDpCxlcPpsR02YzunQOY6bMZ9SUhYwqWcSI0gps4D0ZTyl7nV3BAjbwdoWz1M338UwHXoEAA2yiuEozEJeSTArZjApxn0sFP9WGg+zwutnu9bBNMjfkF1Lr9FEZKmBBcTFvXHslTW/8HWq2wqyZVAwaxar8vjQEe6i0W6KAJrxeFbwmr9dNIQ0BXgW9EoyWo8jlTrcpXWcG8Bo7te6jJcFpGnpFPW3yWjR7XbR4XdQ5LGodLuqDeez0+tkeyGdHYTHbAkXUBIqpz7uQrf73M7/o42y64yFYvBlq6+DlZ3llQD+29+5FTSBIdSjI9g9cQOOoway65EL+0bc3M+74PGzfA+s2sGz6Q7zesz87QsUqtVbSCpBwBIg6JV2ZTiWnFF5HW9Da2arwmoFS6/kxAymndtHZ4POz+5JLaRgxjMUfvoQX+/fjt5dcyr6X3oDdtTBvPi9f9D425BfRHMgnIsquJ0jK4SfemqHjSLB6uO9t4L353p9y5Y2/xAbebv6gtA//iBawgfeIJrIXON0W6CrAK5H6Otiq7fVvXFwEPDqjQrNl0eJxK19eyfUa8YRIuUJkHF4VlNWUH2BxzxBvXfNRmt94Ghr2wEuvs2b49awI9KLOX0jM76PJssDjI2W5VRqvTLaYhk5X5lDr6ggo5vOZBLxmkCC97J/ZN0nlJum+6jwWjSEX+7wWB3wW9R4HjZKtIZhHIlRIjeWh1h2kJdyDRmeIWLg/BwIXsLbXx1g5/m5YVwV7q4k/9XveOvccNhcWsM8rPqR+NjksdhUVsjHoZ3G/fqy4/QuweiOs38z6r36L1/L6Uxk+h2ZnIUkrSMwRVLAmOX8FeuW1fNThbufDeza6NBwKeEXtleDJ3eE8VvtDrA4XsLaoL6t7X8CfCvtT/9zrsK8eVq/ixYsuZGNBD5qDedRJxg+Pn5T4ZrtshfdY7q1yHzSZamQ6mYEFK3djA++xWNFetjtbwAbe7nz2u8ixdwXglZRZJkOCAJy89hXfT/G1rZdcspK2SqLSXdmiEy4/cXeYpLuAZstLyhuiKj+PN3uH+dfkIWQWvgF1++A3f2Hd5aPYVnAujYECleZMpcCS7AVun1J3ReFNWeLvqv2H5fW7AVzTG3Bp60+/wmuAV3x1ZZ8N8Eogk/jF1vks9oUsqkIWNQH9WSup2oaSC7fF46fR76PS46C2Zw/2FJ3Lm/nnsOq+abBtJ+zZS90Pvs/bvXuzKT/E/jw/jQEPNZaLek+QfV4fK3oWs+Lmz8LWXbBtByu/8S1eLhzAzlB/mlxZ4M2qu20uJC7lZhGTvLwexxHTkrXZ/WC18kz24TX7ba6j3F7yEe/1eNkZDLE7XMS2/F6sLerPM33Opfb5l6GqGlYu5aWLzmVrfj4JX4CYuN1IdgmXXK8H28Js7+h7c90caV1mOduH13Zp6CIPPns3T7gFbOA94Sa1V3iiLdAVgFfgzSiVAgUy3ezRwFYdtGhUAWaWetgnLDcRh5eIM0jMnQ+F51BrhdnVewDPve8cVn71Pti6AtatovKLU1nW/0Ns9/ekzuXT+WkdFjFRyqSohWQwcLhISN7fLgm8OruEdssQGNGR+wZ4awV2wxYHQjo9mAwilA+pwwXiS+tw0uT3sNtjsSEcYO25F/HixZfCm6/Bnp2wbRtvDxzC0t592VdYwP6Ai5agj4gjQIsVVLmN1xT2YfmNd8COPSot3PJvf5t/FvZle15vmlxhFVwloJvrqiKQLv7GMYFdj6N1sCPn/uhhTS/bVYFX7FHvdrPfH6A6GGJruIBVPYr5W5/+NL74EuzbB0sX8eqFA9gaDpHw+/R1K4q+FBUxPsHHBb4GZI9kd7OcDbw28J7oJ5S9vq5iARt4u8qZ6sb7eaYDrwCOUb4UsEmqMLdFnd9iT77F7jyL/X49T+A0ZrlodHlp8OlStgfcBdQGz2Vx0fksuOVmMq88B0vnw9/+wruXXcXKvN7sl/yx3gBxj4ek10PKrZuUJ5YmOWqliToqrW1/Dg8CpzNoTSu8OstEG/AKwDuVIt7otWj0WzQGJODPIu6R1GRWNvesVGVzkfF6OWBZ1AXC7Ox/Ln/v3ZfGX/wUNq9WCnnj937Im/3OZ2deEfFAmJTfT4vTTdTXi3qrkCZLgtPOZ8lNd2mf09oDLPn+d/l7j15syy9Cik2YAYxUthMXFVHrZQDT6HYodVcKY5jBzqFg93Dno6sCrzl/UhK7MuRjbUGQij5F/LF/b5L/fBl2VsKC+bxz3gC2hwNE/B4aJXe0R59P9dYjpzjJoWx3+PkGZA9/nZu3B9r9x0Cv/q06vw6n2rd9AReLizysu20cbF0J0QaVFlFuv6rOTTqRLXUOaVNN8xTdm+U+aLs0nCJj25s5Ky1gA+9ZeVrProPqCsBrCgBIzlhR/yTYqsZvsTucBV5Ref0CSG6a3T7qAiFq8/LZ4/GyK9yTpaF+LLtqGLz4T9izFV56lhXXT2RR7z5sC4SU/6NU9hJFU2BXwFl6E0QVEcVRKrA5uzbwCvga4JUytJLhQsBIQNNApbwSF8VVHbvXRySviD3+It72BFn/71+Ele+q7AyV//1LXr30Yywr7EtjuEC5f8i6CRawzwrQ6OlDi9WLLfkXsfSGf4ddB+BAHe9+7zs8U9iDHQWFqrKbqtQnvtiSRUJgN3tu9/s08IqryuGAtv2A6GAw64rAKyq7NNl38bfeF/Co4h7z+xfyP/17kX7xVdi8UwWtzT7vXHaGgyr4sMFt0SSZS2QA6NHn9PBAe7C92i9vA6/tw3t2Pe/sozl5FrCB9+TZ1l7zCbJAVwJegTF51SvAKwqv+J5WByxqAxYHAi5q/R72BXxUhcPsLSpiS2EeKwb05c2PfoSq738H1r+rlKV9Xy3nteKeyvexweMhkYXcpMrK4EH6pNNLs9ujVEgJIJJE/uq1e1bhbQ8G7cHBANrpVHiVsiYgK/ubdcdQ7hlS0EDSk2UzXGj3DV3WWCnkTg9N7gC1viDV/nyVpWFlYT82DxtN4o//A+tXwPqNvHbZ1VQUnkNdz3PZK4F+oYB6jd5gWcTCvTngLCRm9WRH+AJWC/DurIW9+1j66KM8n9+T3Xk92gOvO6vc+yz2Bh2qpLJkj0hlgfeo7G2yHOS8xu+qwCvHLUVKIg4ZCHjY7fezuG8RTw7oRfqtt2FHJVQsZO6FF6ngtojbhQJe8WkX2M3Jb3w42x3+Oxt4beA9QQ8aezVnvQVs4D3rT3HXP8AzGXjJ+mzmwpvy7/Q4aPY5dNCa36LeJwDspMbnptLvU4rX5uIezCkM868PXkzTz74Pm1dBxQzWfuthZn/8MpYX9KAumEfao7M44PaSdvrAHSDl8CJBWwLDkiu23uNUUfOSJUIB5BF8Sc8U4G3dDwHBrHIbc0qOYZ2pwcCuBmIPSaefFneIA74Cdod6sjpUxMrz38e2kWPghZdgy3r42/+yetwNLCp+P5X5AzjgCChXBuWOICnivB7qPF4aA3lEXSG25fVlzc136fLO23ex4huP8Wphf/aEi2lxBxXQqtf32QIT4s5QnT2XErSm1M4cgO0M0NodZ4dz09WAt1XddTmIu3W1PykiUe32sbhvT35zTm+YPRf21EDFIuZ84GL25BUqV5JGSWfm0ap9yq1dbzqz19HPs4HXBt6u/4yzj+DUWMAG3lNjZ3srx2GBMx14BWakSpg0gd2U00Xa7STpdSlfXnnAC4iKwtXodlPt8bI1P8jy/n1450MfpvYbj8DSCti5FX7xK57t/36W9riAykBflQc2bflICFC5PTRLQQafZCdwq8ptdT6PUo3rvE6Vsku2I/sg+3Q4aGgFsBxQk3kK7HJUSDVPAuScOshI8vw2e5zsDDupHJAHw65WSqdsV8oAC7zJsqaZfTDbO9RnmS/LqMGCKORZxVrcNASmMpaftDNEwpVPk6cHNb5ebCroy5sFxcy57lpiv/oprF4Em9ezZvINLC1+H1tcPam2/KR8+aRDAZXOLaXOhUVTwKI+YNEYtFjdI8Dy22+FXbtgyxZWPfId3u79fnYG+tPoLiLuDBJ3uVWGDRlMiDtFi9OrAE6ybhwt8IpNOtpBjrvLAq/bqQL2ZHAiA5FqX4hFA3rz6/69YcZ82LwX5i1k5sWXsCu/l7JZRPynxa1HAv7c8pbi8NepuV4O3dvAawPvcTxc7J92KwvYwNutTnfXPNgzHXjloS2v3+U1vBRNkGAqlWPUK3las8FqLpeCJsm/uzsYYnm/nrx1yYXUfkfcGNbD4gW8+/Wv8fcPX8VLRRewqPA8thScQ3WgJ82BQlXGVgKu6i2LaMBLJOTjgM9Bnc+lmlJ4JYjqKIDXQJfqTzPwpgUCs9AjKq4ApaS7kny70mSQILCpchU7Q0TchdT4e7It1If5xQNYNHYMvP0iVK8j9eYzzLzxet78wKUsKzqXrcHeVOb3VBkEagN+mgNe5fIhAYU1IYudBRa7e3iY0yeP+VItrHIHbN3M8u/+kFfO+wir8s5jX6APLZ58om5R1KXim+yji6TlU03yyR6tS8PZCLwSuFfrcSm1fGthL2a+7zz+45y+8M4sWLNFAe8rl3yUVb0GsNdXQIM3RIvHS5NygzjywOzQoGtAuZsD76pdrXl4R03pvNLaqKknq9JamkwmpYL6Ot6j5bP8mfld88lj7/XZZgEbeM+2M3oWHo/cO6Wl023NzJPDTZMhlYkDjRDdQfNLv2PdFR9nbzioAFAFHbWqlvKAzD4sj6CCti5nlu+sl/U6LZWHVfvP6spnolQKWJk8vFIsQtStA+FCVhQV8sol51H388dg+1rYspa1T/6G/7jq0/z2g5/g2Q9+gjc/8kn+9f6LWdD/HNb37kOlL0zUn6fWEXO7qfc5W4sxSEUy5QIgwV1ZlVZgtrP9bwe7yo2gbTn5TqBM/DLN79W8k6TwyrZibl2UQ29PfHl11ol6AXm/kwavk5jXqXxvpWBBvS+PPT36sqBnb1aOG0XV7/8T9qyA7Yt44eEv8+vLPszfLnofsy++lJnnnsdbF/Zj9oCerMrL15kurBAt/nw2FgSo6BlkyYUX8uyFF/HKFz4He3bA3j28/eij/KTPBcw6/4NsKOzLvkCeGlSIUq9TkEkOWa8qkStqr5xr5Uaicglre4pvdEf/aGPTjuelKyi8HfdZgjTjbql6F6SqRw829+pDxXnn89dL/42yc/vTNH8WbFgPi5bzxw9fzhvn/xvL+53HRl8ekcIeNIp9JMVcZ/+mjmledwPeJOl0UoGm5IuYv2oXN973U6646Zd0BryjSucgwDty6kJGlC5ieFlnpYVnZ+cvZljZIoaVzmfM1JmML53F4Htf5SOTf0zpt3/Ptl11IPfiFCTTKVJy51XAK9CrW24WCbk3Hwy8aXXHlru2bmfhA8s+pDPWAjbwnrGnxt4xYwEDt2cy8EoEv3IlsDwqG4CAkACvvDKX1+cS2BNx+agOFrBmwDls+/yNsGUJ1GykZdNS1vzhSZZ+9zE2f/sx9v3we2z9Rjn7H51G7OEHSHzuZrafO4BdTi+NTh3AJsqaZH1ocltKRW51qchC66FAQqCrXcuBC5l/OoBXoFfDoKSL8pBweHQJZr+T5qCLmE9DOOE8VVZ4lc/PwgvOhR89BtWb4MBmKhe+xqxffJt1j3yN/Y98k9i3HyXy2Ndo+tHXiX71ARqHjaayxwW0eM+lNtCfZX36MPfD/8ZzH/4gT3zi47z4wJegajtEaln31z/yi2s/zV8uvZSVH7xUpduqDFg0h500OiykYl7C628tNSw+xwZ6jd27A/DKwKvGG2JNXiFzzzuP/7vkffzn5R/jF9ePJbp8IdTXwPot/HH4eJ656lrevPTDLOpZzM5wWLn2IP7POdffe5u2gbcj8A6dtpCh5bMZMW02ArwjSw4G3lGl8xheNpMR5bMYVnYo4J3RAXgP2MBrHkp23yUtYANvlzxt3Wunz3jgzYKiVvk08Mprb6kKJgpvJCCFCZyk/EH2eYJs6NOXxL1fhJ2rYP96iG6HLYth4yJY+jZsmAvr34GqxbDoOfjf/2DnwE+otE+N4bDK2CDQJRXXVCYDCaYyPsRHAF4DFa3QmwMcpwN4BbCldQReye0qtqu2LOJeS/kwt1gWVU43W/Ly2HPlJ+HPT8GGdbBrK+zeCts3waaNsGgBLJgFqypg0xKY/y/43i9YV/gB9rovojL/YtZ89Ap451WYL4Fu82GbpDJbD1UbYM8aWDkDFr4CzzxB1dBPsDpg0RB0Eve5iHndNIQDVDp1ed3cdHDGvh17Y++O8+Vzl1V4XV6aw/1gyFj4029hyUx4d5bOgVy9G/bXwK49sHCxPh+z3oAffJeN/fpT5Q0i2TLEjaUzmxz9PBt4DwW8I08p8GaUmntkhdc8u2yF11jC7k+dBWzgPXW2trf0Hi1wRgNvNthKgZtKr6WT2uuMAxaxgJOo36FUwbjHxwFPAPF1jN0wCf74KxLP/o6W1/9K8sU/0/yXXxH963+T+MevyLz4BPzzcfjfH8OPp7HpkxezLt9HrU+qVWXLs/p9CnQFersi8CoIzPHh1cUBdOW4iNupovkzIQ/4HSTFzn4fLXl57C4sYNvF74OSqfDkUyT/+iyJ5/4JL7wAzz5H+i9/Jv23P8Nr/4AXnoanfgNTHmZD/vvZ53sflfkfYPG5F8P//pbEn35G4sUnybzwFPFnfkf0/35H7JnfEf/7r+Fvv4SfTGfDJ9/Hao9Fc8hL3OtU/qd1QY+q2iZ+xgK84rstAx4BNZOTORfazlbgPeAuInbFdfD9b8H/PUnDn38NL/6Dpt/9gdjv/gR//hu89DJJAeLfPQ4l97M8v4BafwFJd1i9Dcm107FPd2/gXdDBh1dcFozCeyjgHVE2D1F4R5aeSIXXBt73+Hizf3YKLWAD7yk0tr2p92aBrgi8puKaqFhSQEFcGgiFSYQK2WS5WFJQwIwB/fjHgL78ubgnz/Xtx7M9e/LauQP4e898ninO568FXl4o9LKwdwHLXRa1oTCZUAG4fUiBC0nZJaDb1YG3DXIEXtp8oEXBFt9nPBZIAJsovJbF3sJ8VhcVMK+oB68Ge/Nij/N4pngAfy3uqyqtPduvH38t7sEfiwL8scjPa70KWBLIozrUlzpHETWeItbmF/Ka38VzPfP5c2GAZ3oU8MqAAbxxwUX8o3cxT/cI80KPMPN65bMmP8CBPF0aV/ZBWsLjJub10OJwHBJ4BXzNsZ2NwJt0uVVA5Y78XryZF+Dp/ABPBfw826sfrxSfzxu9LuKdPh/gb+48/tmrD3MG9FewuyeYT5PTT8pyq1LSxkbvrbeB12RpEB/eUw+8GnSNr26bwpvS0RVZ3973due3f2Vb4MRawAbeE2tPe20nwQJdD3i10ieqr7yOFx9UcUEQ6E16QxzwhNjdo5g1+UUsz+vJ6oLebOl1DjuLz2FLYR/WhopYW9CDLcV92NqjJ3sKCqj2BYgH81RZ4ozDDVJOuAPwSsCaBJzJdo8EDwbAOgZWyW9PVdCa7EP7/WwDXpUNwWmBBOJl3S4IB2hUeYw9bC4Is7GgB7uKzqGq+P1s7X0Ba4v7syxcyOoePVnbqxfre/diXe8iNhSE2CpBhIU92W85aQqEiV4wgHVBD5uLerG97wBl+/WhHsr2G4p6sbFnb9bl5bElFKa+qAfRUAERy0XC0unXEpZDga+ou91V4ZWUbM3+IAcKClnn87CxRw+29u7L+oJitvU8h7W+Iqp6nMcWXxHbevZmS0EBmy3xhQ4TPyGwK9dP9wXeZAZyFd7RD7QBr/jlGoU3N2htRGkFJ1bhtYH3JDzy7FWeJAvYwHuSDGuv9sRZoGsBb9sDWOAxJnlfvZby5xXoTbl8NDt81AcK2OsNU+kKUhcsosYdptoZVCVvG4I9qPXnqdaUX8QBf4h6b4Bmj1/7PXq8Khdsyu1udWUQ2D0bgFdKC+umU1Yh0CsKr1TncljssywqPQ4qC4LsDAXY5wlzwFekKq7V5hex1xukJhCixuOn1u1DfJ73eZwcCLhVawh7qSvwsdVtsSfopjZUoGxf4/TT4A+r9FrN+UXUhvLUIKPGG6DWLengPEQcXpIer3JfaPWhlrzIOSWd2wN8G9CbAUZn33dVH14pqdzgEt9mN5IHuiHsY3/Ixx6fm30FIfaH5Xq2SIYLqHZYVHst6kNSKMUi7XEh57YzexzbvLZ/b4f/nVlOuxzJmwQDy3JuZIDV6LLYF3CxuMjDutvGqYqHRBuUb6rczeQ+RFpyIyTVzS2tM2+duBvdEdYkKqpkaDBZGo4GeEd3yNJw8oG3bf9U/pxWhdf22T3C6bW/PgUWsIH3FBjZ3sTxWaCrAq88SAXSxKUh6XciQVcZt5eY5SbmChFzh2m23CTcAWJOHy2WW/k1JlwhWgSwFBx7qLOcNAu8OV3qFX/a51e9BMIJ5IpbQy7wHg6uDBSYZc4UhVdXUsvmuM0pjSy+uxmXVp0lCDASlkIbTqoCbqp8usSwVEuTV+SStq3B4VYqesqlq9GpnLleD1G/RwXA7fdb7JNguIDFfp+DFo+fFkv8oj3g9Sv/aAFYKYzQ5NHrb3T4iDj8xF1+pUzKORV3C0lRJrmXO8vSYOxsemNv8zm376rAK/mHJYivQQIPXQ5lE4HGpqBLpcyTDBspn1sVYZFe7NXidSgf6IxHBjY28B7LndEG3mOxlr2sbYGDLWAD78E2seecYRboCsCr05KZqHNRk3TmAQEjBWpuDUkCYJJrNpHN46qALKsQSh5fgSfddD7aqMOtcu+2OKWIhXl9LnlgHaqZ7YqaLM2Alelzwaqz6VzokN+cDpcGtd2sDQTipZn9l/0TIJSywDJwkMwNkp9XoFeqzDW6pOKZpAgLEHMEVB91SjCfTw0ipEqb2Ft6AVmxoeQtVk3s6faqQhcqw0Y2ZZu4dOjsFw5VTS1q+UlIxTbLq/xOxZ1BBQkKgHdThVdd35ZTlbqWMtdSgENS8TW7XSp3crPXRdTjUrl6xfVBqg+qIh1SqMPhVYVEOCFuDUa5PRI8m+W6psLb5iOrFVRReUXhnb9yJ7fc9zOuuulxRt3/JsNKFzBs+iKVakxSjo0sma3y8I4qWaTy8BqFd2TJXBW0Jm4Pw8vntObhHV6+WOXhHVsyi/GlB6cly2SFWhG3k5KJNy25ePUDoyOQi8Jr1OjcvLv6WLKK+Rn2rLF35+y2gA28Z/f5PSuO7mwAXoFeXZhCHrwCvVKG16kBUxRMaSqXr87nKwCmixq4FKhFnBrWpI86HQh0GT9X+d3ZArxiIwFeA+8CuwKgApgCvE1eDb0CvlKUQko1ixKecOgmSrkCXpceMAjsmib2lMGEVKMz25HiCdJSrqwNs+dCKbiqkIdTDU407ArwymcNvALF3Rl4xSUAy0/GCqLKXzvkrYRHQa9UyzMDOLG7uKlIjuWM5SNtBUg4pMm0Hhx2Nhg7unkGZM9u4JUbeUegVIUncoB35ANtwKvy8GaBV/LwGuCVoDbx4TXAK1B8JOD98KQfqcIT23fX0RF4zb25I5BLIYrDAe9Z8WCyD6LLWcAG3i53yrrfDpub6plYd4UuewAAIABJREFUeKL1VXwnCqtSSyU/riiJWchSSqIoY5ZWaY0CrIG4A7hml5OUVwJWUk3NVFWTafmNgd1WRbRDYYkjQcPpVnjNfhtbGeg3AC+wK+4aWnG1VKoyqXjW1qSiXRaS26nEOr2ZBPiJuihQK4pji1IeHeoVfMbpQJp6NS8DEvG3zjYZgCjoVudASglLkKBbDT5kwKEUXsmQ0Y0VXgFeAd20FVQAqxV2UdblDYZbBfjJtDQZdAjsZgR2rSBNbmkBNag70jV6+O+7L/CKJ7EErRmFtyPwDj8O4NWV1rTCe7TAqzM0aAVa+zknSWfiZBA0t314u9+T+8w7Yht4z7xzYu9RBwt0BeA14Jbba4jTaqJRFA3wikuCUi7ltbhHNwWwRmnMArSonfJbA7wCui1ZNwmB5bMBeA3cduzl2IyPslJTs8dtBgfSSzU7abn2Vcq3ch3RyqLYsMnjpD7g4kDApSrUic9pG+w61DlQEC3BcR6tuCvglQGEKPEOqabmUuq68ZkWX14Fv1nQlu0eCs7MddHZ913Rh1eOQw/2xF1E1PWA8nGOiY+605N1/dAuOqL2trmW+JTricBuk9un1PzObHL087o38Epp4Zvu/xlX3vw4w6e8ydCyBQx9cBGDy8VVQbs0iMKrywq3lRYeXjpXVVqTZXIV3valhWcw6J5XOBzwmlu1Vp9zXRgEx5NZ2JVpG3iNrez+9FnABt7TZ3t7y0dpga4CvOYhbeBGAZzDBGJpxbHVZ9e8FndrtdKAm0CcqMGiWuriC1KAQdwZ2oLTBJQNHJptHao3+3So/nQrvBpOc3x2swq1zBdQFdA3TUCzVXnNDgha1XNV3lfbWGd50Kqi5HqNON3s93uoDLvYE3axTyqmiRtDtvyzuEm0tmypZgFs2Z6AsbG19AZOlauFnMNsWjLlhtHdgFfS4EngpNuF5OQ1Kroo6ea8yrxY1tZib5mWksRxlxvx7ZXr9lDX5tHN73rAa17/S38sfxoq27IgCEYK8N78wM87Bd5h02YxvHS2Ki18OOA9XGlhA7xl33kKcWlAdjkt/4luK3ug/9qOSbsyGIVXq7vtgfdYj9tsw+5tCxyvBWzgPV4L2r8/6RY4U4FXQaZS/9orewY+9UNfq4zGj1QrhdoPV6mWWYWyFdyy7g+5sCvAKyAsaqeAVi4ImG0dqs9dtrPpMwV4c/dNjkVsp5Vt7bssYCmuHeY4MybATFK/Zf1+Bb6M33PCoQPV5NW6Al6fZHZwsjeoA97E3rJ8k9ui3uOgUQLZXDl+0tntGQXdQK9sX85BOhs02N2BN+52qOwL4haiMjV49GcDvApwPdptRJaRTA3SxGea44Zd+bfQtYC3DQx1/tpjuXl2BF7lwysK7wM/5/Kswju4bAGDHlzIoGmzMcA7vHQ+w8skkG0Rw8orGFY+j2FlbQrv4YB38L2v8pHJP0aAd8ee+nbAG0+nVMCa3J8PKjhBQiFxZy4N2gbHcuT2srYFTowFbOA9MXa013ISLdCVgbd9sI4n+2pcwEq7JgisGTAWGJblzevz3AAroy7mAmpHSDQw2Fmfu2zudO765HeyHVGQZVqWU/Mkl6q4F6iMCQ6lOO8MO6kckAfDrlYgKQCJROBnlzWAaLZl9qnjZwNGZn7rNrPAK1kVTHYKCRYz+yXAa6DXrFvgVPZDBgjiwiD+zhqC2wYYopQb9TbXD1cFAzrE79Srmh6gaAVSjiUmbhPiM93Bt7c7A6+yi0f7Psu0ulbUGw3x7dXXsbgzyDkx14MKznRZpCUHrw28rcBo7nEdgTj3s0BlKpVQTbIfCPDOXX1o4B1aPlNlazgU8A4rm6ncHo4GeMu/+4dW4JV4tFQmTSyVJJXKZFuKVEoAWDcB3VQ6RiIZUb0JYjuJjwl71bYFjmgBG3iPaCJ7gdNtAfMwOFTQmjwUUpk40ATRHTS/9BTrrvg4e0Mh5RZggEjDUns1Nhe03su0CVrr+Fu1TaU+mTRIErQjr9nFF9SpoEvAS4BP1tGWCF8nxNfA0BaMpfe9vbqbu83cY+xsOnfZ3On3DryOEwK8ndlP9l/sInZqBcpskF/rvmeBNxeaBKpECRdf3EaPpdRbgeBc2wmQS1PBcFm3iNYBRzajg84mIOdLzo3+vYDawcArqrNWntvOY+fnyJyT1v3PUerNPsn+y/dm/8y+NzstqvLdMGEw28MWVQGHgkiVJcHhILeEcWfrP1nz5Jjiyn9a20jZSgYllpRU1te1SrGXBV51XrM2PzOAN/tvLeuacVSFJzKCmfpV/nspPJELsGo6lSaTbUYl1b2opjp1l77/ZdRy6VSCdCpGJh1vBd4bp/ycK2/5KSOnvKqU26HTFjK4fC5Dy+aqFGPDjMKr1F1ReBeo79T30+aoacneoFKSlVcwtGy+Smc2pnwGQ+57iY9f/wOmfScXeNMk0wniyRjJZJJEIqGa7FsmnVQtlYyTSMSIxSL6OzkY+8+2wGm2gA28p/kEdPfNi0tYZy3XLvJgIK1v+ObhIJ9Vy+h0PamMPIQOD7waSkRFPbHQe3igMK9c2/p2D/5WlcvsU1tvIEn6w2/jvX8vcGXWb/aro8Iryq7AmFZ4NVDuzLOoHBCG4Ve1AphWeNvSpRmAk303x2KOw3w22zbzTd/6fRacjuacyW9km9KMK8IR159Vs9vAu+08mX2RvnV/ctRvNV/2r8O83N8dzXRnwCvuKwZ4BcT2Fjhh0kC25VlUBTXYy3YFdk8X8Oba5VDHqeyaY5/jtdXB2zHn60j/BsxyZgCqe9k/M7gywPtuoYf1t7avtKbuUep/ArzS9O0n9z51NNO5wIuSSmOQikMiigCj/Mlmkqms94CoqUnUoqRTkIyRijeqJgrrzJXbuaXsF1xz+48YN/VZRk99W/nsDi9ZyMjypQwrXahz80p+3nLdBGgluE3cH4ZOW8yQcuPqoGF4UNk8Bcwjps9g+APP8rGJ32Tqt37D7qomtVOZZES5K2iRIUks3qwGAclos9rZVCxKMhpTy8q+ywHpY9JqsLZTWoHx0djMXsa2wImygA28J8qS9nrekwXUM6QT6M1d2bECb+PLWuGtDLcpvAqElJp6qoH3SA/i0/v94YDXwMmhgTd4VMBr1mMAOPezTB8MMd1r3nsBXglyFNudbuA9/efOgOyRrhmzXBvwmrcoucC7P+CiFXi3rISILi0s9ykJ1EIpvCcQeGV9mQTpREurchyNx4hE48TjEqAGyXiKRCxOOh7TwJuMkEw1I9g5f8M+PnP/T7ls8iOMu++vjC95kzFT5zLs/gqGPrCY4VOlkIRA73yGl89juEBuK/AuZNj0pQp6R5RXIE2geHD5fAaVz2f49FkMvudpLr/hMR76wR/ZWdVMMpkmGmskTYJYOkKCCC2xOhLJJjUQUNAryXrTIPyeiEMyASlRq1tv6qL2ihocy2ZvaP3CnrAtcFItYAPvSTWvvfIjWaAj8Ha2vFFF5FXfQQpv9kGUQPI9aoW3M+A1kGUUJ/Ogbp1vlL7u1ue8WhdbKHW0gw2OBLzV2Vfsh1J4jY0PBbzm++7Yy3XYGfCKym4UXnFpMAqvcmkI6lRsYi+TS7g72k4d/zEGrQnk5jaB3Y7Au7TAw0ZReE8y8Mp9TbsvSPquNLFEVLkJCEy2ZjlIx5ULg7gxiAIsrgTRVIzmdIJ64F+ra5h8/+NceeP3uaHsRSaW/YuR989i1JTFjJ2+SgHv8JIFqDRkUlGtfF4r8A4VEM66MMj3UpBCwHhIqSi88xkhgW/3PsvHJz7K1G/9gU27m4imIJpOEidNfbKJFpqI0kRjopZIqp6WWIOG2Ay0NMUU7AqnJ0Slbr25i+wbBWzgbTWJPXFKLGAD7ykxs72RQ1ngxAJvo/LhbXz5d6y56qNUhoMH+fC2A94OYNctoeEwwCswJjY5FPBWDQjCsKs4FPBqINHKm7Ft7kBDANjM76794YDX2Kcj8FYH2lwabOA1yu2RFV71b78D8Mo8A71i51q/i1MFvKIYSxKvSCKtesl6kMikVcnehqYDqmhDMhVV5XllWcHEljQ0pKAuA/uBt1Y3cf2UJ/nYxO8x9r7nmFg2k/GlCxldulipvOLaYIB3RNkcDbXKt1eU3qwvb2m2BHFp9vvS+QqER02bx8j7X+KjE77LvY/8mQ1VIDp0UwYagZpMiv1E2Zusp5EYjekWkqRpicdIJLRPhlF4xQPNAK9OVSawawPvoZ6L9vyTYwEbeE+OXe21HqUFDPAebvFchdcEdrT68LZTeBshtoPDAa9OY5R9ONrAq4KyOkJoRx/eZAcfXnmdvluCpw4BvGZQcSTg7a6Qm3vcRwu8VflOmDhQBa0Z4FXnLVstLned3Wla/3sW6G0/sDrIBq2+4Dp7hMmIoiFYQ68B3mX5p0bhFQiMi1uuQG8SaluiRGQeUCd+sAK4iTSNcaiPQ10c9iWgOgWVSdiZgBfejfKFR15m0J2/Z/S9LzL8njcZfv8cxpYvyVF3dRCalBEW6JWUZMPEp7dUVNy5yPxRZbMYXT6b0WXzkawOI0orGFO+gIF3P88VN/2SqT94heW7YW8cdrbAriRsNy2eUWrz7kiC+iQ0p6C+OUokmqSpOUYylVGwK/d6yTKhgVdUbGl2MNvhnn32dyfWAjbwnlh72ms7RgscD/Aq397DAO9epfDqFEjmAWgDbwdVtYPCK3YS4M0FCAO80kskvg28HWx4HAMnsbPYNNeXWqblHOQqvDbwdm7zYwVeAV3TxLXB/F7At8WhFd5TBbwSiyuv+kXlbYymiWbgy2UPMvqmzzL+s19kxA13MP6Oexh9y70Muf4rDJp8P4NuLGfgzQ/z6Ru/yuWTvsZVN/2Efxv1Xa689SkmTpvB2LIFjJy6gFHl7zK0pEL77marqhnglWIUArsCviPL5zKqbKYGXqX0zmX4lLkMmyqQPBcpV/yxz/yKD41+lKGf+zHX3PQwn76pjCGf/yqDv/BVPnb9Awz9/DcZeGsp133m3xl1y5cYdf3tTLjpVv7rid9QW3eAWELczUSj1pXYNOSa6WN8YNiL2xY4DgvYwHscxrN/emos0JnCa+bJHsgLQe3D217hNcCbm6/VBt4O4HCUwCsgbANvB9sdB+i2DcCOF3jFj/fE75fZvzO9N8BqBmgd+9b9z1F4c314ze87Au+mW8bB5pMctJattiuxW6kUtKRg3a46vvzVH3DNZ+7msnFf5PJJ93P1Z6Zz9Y3f4KrPPMYV13+HKyb/gE9M+gEfn/hjLr/5V3zkM0/wydv+l8H3vMGwEvHDXahSjA0uEX/duToXb9lMRpRLm6U+K5W3TPx25zCydKZKQzaqdI6C3BFT56n1jCqdx8Avv8Ynb/sTl47/GVfc9Asu+8wP+ORN3+Oym7/DhyY9xiXjvqfmf2zcNxly62MMnHQfnx51C9/+yS9pjMVoiNQTT7UohwztlJF9ZkiSHSF9+8+2wCm0gA28p9DY9qbemwUM3Bp3BhW8ltGVikQhtoH3OICnA/CKqtiZS4OAgw28x2HnQ8Dx8Su8NvC2G8TmXM/KtsbuZyTwZiCZURKvZDIQV4YDKVi+J8Ldj/w3Az/3NS6/+RuM+PJ/c/ND/+Dmh19hUslLjH/gZSaXvMFND81i9JTXGFPyDqNKZjJ6egUjpi9iYMkClWpsSHmFzserClDMZPg0U2hCF6TQCu98BGzFlUF+P3baIkZNW6inH1rEiKmzGFUyg+H3vcrkh//FuLJXGDXledXGl7/K5GkzmVjyJmO/9DQDb/oRnxxxD1/70W9pSkHVgf1KikgTJUUs64UMksRBWqtT73t7LNi/si1wzBawgfeYTWb/4FRbQABXqvgY4DXTAsKStkeq/nRUeNde/TGMwitJ7o3S0+7hmPPqvqMy1B0/K6g1hQGMbZwOBboCwvKqXewi1crEh7f6nJAKWqvy6wpnJkuD8eE1y+eCh7Fr2/loc58w33WnXhdraMvUYGwl9pYmQWlNDou9eQ6YNIgdeRbGh1d+2zEHr8zrTvYzCu2Rj9kEt+WmJXOCw6VaR4W3Y5YG8beV+0xuWjIZbB/Ln75f6XuZqkomRRoizZBOEoton93aFOxOQMXuBDdMe5xLx09jyJefYPTUZxhT8grjy2cyZups5ac76avLGF0mLgnimzuP4dMWqhRjg8oWM7CsgoFl8xkybR5ScU23GQybNlOVHB5aPlupvyPEBaJsIdIPLZmn/Xenad9eqcAmgWvSxj0sy8zhui+/waSvLVSAPfhLrzFBFOIvv8zA237LsNt/xp0l/6UC6hqTEEklaIo1EE81INCrfXdzYPdYDXgsxraXtS3QiQVs4O3EKPasM8sCBnRNnwu8sqfiHSbAC82tQWsCvJ1laWgHvN0ODg4PQ0cCXqP8dgReydIQc7uypYWl0pYuxmAD7+HtreA2Jy2Z8Z0250HSkgnwqiwNArwTByrgNYUn5Pc28BqQPZKtzXJtwNta3VD59TqVnY8lS8Ox8poBXnMfU9UkVB7eGIl4iyrVWxOJqaC0GmDxXrj3J8/xkUnf5BM3/4Lh9/2TkVP/pdwNhpYuZvDUhQyRnLvitiD5c8sWMHDauwyavoRB0xczcHqFAt7BArcKetuAd/C0mRp4yxcysmwxw8sWMWTqvNb1DS2dxeCp73Dd/W8zfJouVDHwAcnju0gB8uiSCkbfP5NR97zEtZ/9H66+4cfcMuUJFby2pxGl8DbFozTHG0mkm0mrtJEShqerx7VGsZ1Zjxp7b85yC9jAe5af4DP+8OSpcYQnh3lAmAeGAK+oLfLZ/OnEPs3ZtGRPsfaqy9oBr1LM7MITB6l/uUqrAS2xlUwr1czpaE1LZoC3yW0hldb2isI74mpVaU0Br8tDymqrtGYD75EgTPx3HcpmYrdETkleKassTVRzqQBWme+CiYPZnu9orWwnAwsbeA3IHsnWZrn2wCs2lLRkcYdD2VkKT3RMSyb3GLnTyMA6V+EV1fdY/+SeZZqsUfLRplKS5CuiXvvHiLM/GWFvPK7gsWJrE7dN+yVX3Phthvz7Hxl0zwuM//pixj6ymsEPLlWpxYZPq0DKCYuie23ZIgY+qIH32nJReKWQhAFere4OmzYLBbzls3XKshIpTLGQIRLkVl6hAHfkdMnZK9A7g9EPL2LYQ4sZVKrLD1/7lRlMKl/IxJLXufLmH3H55OmU/eQZdsegKgYNSZ1lV9KsReMREpJajZQKWlO2zLlvH6v97OVtCxyPBWzgPR7r2b89fgvIQ+MIDw4DuqZvB7zZ3+rSwi0Q3UXzS0+x/srL2BuSSmsuBW8m16YOWJGH35EekGf/9wZ2Ddx2BF75nHIdG/AKtClb5/gCd7Ze88rebLu7ng8DvGI3aUodtzTsCvCmnK424J00RJcWDjhUfumMeh3vaHct2y4Nbf9uzbWle2e2pHhb4YmMw91adCLqdNDgsmhXaW3rSojK63idVkv3Jp3WCSgtrNYcJ5FuJKOAt5EEjURppDZap3Le7m3JsHpPnLsefpKPTvgGV97xJJ/+0rNcdd9rXDN1NqMeWsyI6QuVujuofAHXlS9UKu/AaYuyLg3ZXLtZ6BWIFeDViu9sVYZYsjqoamxlixg2fREjH6xgzEPzGP2gqMfi+zufwdMquOaB2Qx8YAYjprzD+Cmv84lJP+K6Wx/koZ/9li31GWok8E78kCMxWuIJ4smEytCQTAvstt3m5T6uYd9OSXb8D1B7DcdiARt4j8Va9rIn3gLmTmj6TrZgQFeUXlF25QaqbqJy48zeOyW/I5kWiOyi5eU/sf7KT1IZlMITucDrIml5VKUlA1jyMDSQ0S37bFCP2ENsIa/RTYUv+SzAG5M0WU6LeDZVVqPHUq/WK88NqdLCe4NtLg0K3JQ62Rb8JuvJBdx2n1uDiTSQdMtzkFP8QJReaaI4ShPgFRDbU+CGyUOUwrvXL6nhNAwL9ObaTH6b+7nbTJvgtJzevKlQfdbGMuCVtGQpp5uky61U9JjLSdTlpM5tsS/gYnGRh7W3joUtKyBSr7DU+PC25ZA9duDNvY/JvSyTERCUUg2StitCPF1Hc6SKWGI/GZpVwJfK0QtsroG7pj/Bdbf9kEF3/w+f+vc/MUaCyL66QLk0DJo6R0HvkOmLGTxtEYPKtT+vKLYmBZn45EqWBgW95TMUzI4RH+BScVWoYFjZIpXdQcoPS8YG8Q0eXqYD3K4rmcF1U95ieMlrTJz2EgNv/U8uH1vKt375B9ZVV6tCFNVNjcpvV5RduR83NDSQTOryyAff3rMpKjq539uzbAucLAvYwHuyLGuv9+gsYO6Epu/kVwp0s0FrArqJVFIBrwSsZVJSsz1NIhGDtLg07FbAu+EKAd5wDvBm829aHvXAE+hSrfVBqF9tCiCIQtld+o4geszAO+xKKqXyl0u/fs8FXgMcBnBzQVq+k89tynv3snsuiIoNjAtDLvCK6ijzBXgrCz1w/VAFvGLvZo8GXgHi3OtVfp/7ubtdx+Z6NteXDNj0NW1s7CIhNhPYdXsQG8u1KwOIeo+DfSG3At41t4yBTcsgUqfuNQlJqCAgJznEMhIv0KZYdnLL6nTWwcArmWll8B4jndGVxxKJA8RiUkOtmVisnng8SjSBKj6xuQr+30NPcu2tjzLwzp8y4v6nGf/gzLbqaeWSnaFCuR6oTA0lFcrnViqtjSzReXUFZBX0ls1geNkMxpVJFoaZKlhtePniLPBW6MwNkpe3VKczG1Y+gzEPv83ge/+Xj3/m23x68nR++MTzrNi6W9VLq4+IOp0klYkTT7SoG3MmncwGG2tbyS2+7c8G3jZb2FOnygI28J4qS9vb6dwCBnQ79tmlzUPCqLoGeAV6U6kMqWiaZCRFPCrJLJsU8EZe+TMCvHsCuS4N+kGXcGSBNxtYpWFDw7B6EDqyD8Ru0MuxG0AwUBpzWUgzAwKj8Mq8aBZS6705Cu+Qy9nts4hIxS+HS/n7anhrK5wg68oFaQMjbSDSXe2vYVVfgxrIBFhl0KBATPynHfpV+54iD9wwjG0FTkRRF+CV34lSmXvdJpQ7RHeypwHZtmvMXGtyzSblDYW6pp3KpjGni3gWduMej7puBXajHhf1PifVYTcLizysvGU0bFgCLQfUADuWSqtX9KlktBV4O7+hHX6u3M9MU0vKfS+VJpUUiBY917hMSJ9UpJiS4hQZaI7Dmm0J7vvabxhy04MMueMnDL/neaXGjnlwISPKK3TgWUmF8scd/EAFo0reZeTUhYySYhRT5zM6q9yOLJ3ByLJ3GFf6JqOnvsmwEgHhhYwsX8qI0kWMLlnA2JI5jJ4yg+ElbzGk5FUGPfB/fOLWH3DFjeV8/WdPsbWqQe1xfUMESaum/RbSZKSesMpooTPrmBdxCnjV/8yyh7eV/a1tgRNtARt4T7RF7fUdmwU6gq75nF2LPBzEZ9eoutKLb5g0Ad5ES4p4c4JIrIVUql4FrUVe+SMbxIfXH1AKr4E6AbG2KkttKq4Cv2yktv7eLNcN+pyUbWIngQMBho7AK/MEeOX7Oq8OWlNpyQR4PZaKcBc7xsX9QVTGnPRmBqZz12ugV9u+G9j5kNeXHnSYQYICXoe4jwjsals2uC32FnnhhqEKePeEtEuDBl4DzboX4O1e17M+XmM/A7u6d5J0yQDWiYBu1CG9h5jbS9zjI+4O0OL0E3VL89Lo9VITCrC4MMCKm8bBhqXQUqde00eSGSKJpHqTlElrhffYbnSdLC33umSqTf4UF4e0QG5CA7C8vsrmrE0kNPTKlldurGXat/6bwTc+xNW3Ps7w+19mwkO6gMTQ+2eqLApjpi9lRMm7rcArfrqi8or7gqi84qqggLfsHUaWaOBV5YZLljDk/gqG3jubEffMYNK0uYwteY0h9z7DR276IVfd9giP/s8/2dOUUmWQGxvFlSwLsGlIx+VzmlQsqsP8DOAbRVyWlT/pbRferDHs7lRZwAbeU2Vpezvv2QLiB5ZIJFQeXgFg+SxN3TDlQRDN0JJqoTm9j1RyM3Uv/ZrNV3yMGp9fpctKucQXtb3iaPJ3qlfL4t6QVXy7XW9cO7J+tgkB3hyFV9JitcKv20nE56TWa7Enz6K2Xwiu+TiSlizi9yC+kAK7RukVW7bzlc5uy/gLm/WqvpvaX+wrldIO10tWDJWH94YhbApb7M23iHr0edG/a0sF162u3yP6f7cFqInvfsrhJ+kMElctRMxZQLNVRIvVg7ijgKi3gP2ePJYX9GD55EmweR1EG2nMpGhIZWhMZRClV+IITCDbe76pmR8qADSv93N7s0AbT8Ykt21C68BLVm9l+nd/w1U3Pso1n/8NYx94lnElrzG+fDZjpGzw/XMZPW0Jw6ZWKOVW/Hil6tqIByUv72wGl85k1INzGTV9NoOnvK39fEsXcu2X5zN2+ipGijr8wExG3vsyY+77u8rGcN3t3+brjz/P+pqUJIBkf4s4MaByoWv3MiFeE1fRpmTLPdv+sy1wJljABt4z4SzY+3BYCxjANa8CVZYGqcUpzwepUBTL0JBppja1h3hiPfUvP8G2Kz9GrU+C1pwK4NopjupBKcFr0nTO2G7bZyFUKa5ZdVe9Cs4pfKAUWlFv3Q6a/G72+Swqw07q+oThmsuQwhONAY96LSwKo6TSkgGF9MauSnUU5Thrb5MP2Xzurr2kbsttkvpNmpkn50XSkhng3ZgnKcrEh1dXvrOBNxvsmDNwax1AqX/nGnoFeFVz+tsBb8zRG2kpZz5Jbz4HPCGW5xWxcuJ42LAGYg00E6cpnaIlrf14TbBZu1K5h72DHe7LXMiV6fZ/iWSaeCKFVOEVP+LmWFJNtyQyrNpWw5Tv/YWPT/om19z+C8be839MmPoGE8vncu3dbzGitILRD76rClJcVzKH60pmMWTaHAW8Q8okW8McBk79F6MeEneIhQyeMp8RJUsZXbpEuTQEPwY+AAAgAElEQVSMuv9Nxj/wT6666ScMvOVRHvvVK6ypTFCfgdoEtGTQ+5ZKtUsRae7TciS50+2PzP5kW+DUW8AG3lNvc3uLx2gBAVyBXnPzNEFsxjcsQgZJ4lOd3EVzbA21L/+arVd+kipvkGZ3Vnk02QckStvyELX8qsUtt1Ille+j8p/UKmX3+exQgCWv0CMu7Yvb4mrzIdWvip3qdbAMHhr8bgW4e8IuavvkwXWXs8drccDrpsnjokV8SEXZdQjsutS0rFteJ4tfalwCtFQmAqfKRtB97Hyo66rN/uLCoG2kXRkEfOVzncdit+ThvWEo68IWuwscNHmcKnuGCVozrjhiz1yXhrPevtkBghkoHNRn/93L2wedjcGl3JwiLg8Rl4+EK0zKFSLt8ZL0u5Uf7+oCH2smjYQN70KshmSmKRtYFoe0vLIXh1UJMpP+YEg9ttvb4YFXtFHTRDwVt4poMqOgV5wGFm9q5Cvf+iNXT/o6o774Gybc/zwTSt9hwoMVSL7cUQ+9qwLZBpfPV2WGFeiWzWbktNkML5/D4JLZDHtwIUNK5zN4iqjCixn8lXcYXzqDUfc+x9W3/IyBt32fx554nWU7okrZlTy7kn6sJZGmJRJTb9/k3mz+Ok7nfjbL2L1tgdNhARt4T4fV7W0ekwXaAFe/JpPPqmX0I0duvvtpYm98B80ta6h76TdsufpKqnx5NLm8CrIEujRoCeB62wGvhjBJPq9hrbv0GoZ00n0BUgk8a3E5EeCNODXkSkCUNPlevqv3eqjyOagMeThQnA/XXkmluDl4XDR5PDRbksZMVDcPCeXPq9ej16+njX1TWTg2n7tlr+wrgVcuBWTia6pcQ7Lzoy4XdV4Hu/PdcP0w1oY0/Db5PeqcmPMjvdjPNDPffD5SL9eCLNPVehkkxLMp81Qv0zlNfHlbg9ZU+rFc4NXQK/67ca9FJGixP2ixqqeftZ8ZARuXQHQfmUwjGcnOoGBX+9hq6FUv9I/pXnbwwocH3pT48ApWy70umSYpcQtZZbUpllHguWpblPu/8TuunvA1ht/5a0bf85wG1tJ5iF/uwJJ5Kr+u5OwdUjKboVNnMqZ8nioVPHR6BaL+SlozqdgmqchGPvA6Yx94nqtv+YWC3Ud//RrrqqEJ2NeSVHl2RXFubG4iGo22upuZY8sFXJnO/WyWsXvbAqfDAjbwng6r29s8JgsYwDU3T9PL46Y5A3WkqKGZ6tgOYi1raX75f9h2zaeoDhSpoBR5iJt0TzrRv3zW4Kt7d/Zz9+rl2PXxS7CZW0FuxKn7qMOt5mnfR2/2ezdNbh81XhfVAR91Rflw+SfY7/fR4PMT8QaIOmRZDzgCJJXC60bWJU3WbdabyG67O9tfbCBZQxScZnsVVCWwm/O53uNUAwwmDGWduJPkuWnx+dQyKYdXv6rPvrKXdba+vleDDrnOj9y66nmQ61bBvOmz0N8K7tkBm9hT29ZH1Klbs9tHncev3k7UBixqQhY7ww6W9Amz6qYxsHk5RPaTTktAbEKlQFTSqpFcj+ku9t4WltgF82dcu5QPsQz6FQRrn94VG/ZT9tgf+PSkR7j2tscZ+ZXnmfzVCoaXzmVwyTzl1iAV2YZMmcXwKTMZVzqXEVPnqEptg0rnq+IS4s87auprjLr3GT59288ZeNt3+fFT/2LFzoiCXfFhjoj7gmQOTrQQizaRTMbV2zd5Cyf36Vy4Nffp3HnmWOzetsDpsIANvKfD6vY2j8kCcsPsDHoFeOXFotRCqiPCgUQlmeYNNL34W9Z/+iq2h4rY7w3Q5HIrX96o20K1rNIj6pndtA2kQEezW1Ra3WRa5ol9Ym43LU63smODz0uNz01V0MP+ohB84AKq/W5q/R4V5d7i8SqwTXr8GnI7Wa+s27a7toFW1EVd1wp6k8uJNDNfpms9DnaHnDBhMKv8FjvzXTQEPGqZqMeNaq6282XO25H6s+EciGuCuC2Je4J2U5DpQzUfArnSZOBW7w2w3x9Qg7fqsItd+Rab8x0s6JfPspvH6Ty8zTVkkhGVNUGywkhwloCmKJzS2l7kH9Mt7T0tbO6BknNcBe1mgxgkV6/cC9dvb2H6957mU9c/prI3jLzvBcZNn6WUXFFvh5bMY8TUeSqobXzZAkZOmcOQqXMZMX0BI6fNYsy0Nxh495+47Prv8umbH+FHv3+L1Xuk7hvKfzmaassXnEk26/RsGQ26NvC+p1Nq/+gUW8AG3lNscHtzx2YBoxKYm71REWS+ed0XS0sleiljuR9atlP34lMsuvZqlhYWsjU/zJ6wRwVZVYUt9gXbt/0BC1F3unsTO0gz9tkfcFLrd3LA56A+4FLTMq8m7GZPnpOd+U4qe/mo6ZvH9qAOYqsKOKgNulp/I7+VdcjvZL3VIb1+s63ubnM5fnP91fn1NWhsY3oVIBi02NbDAZ8ZxLKwxeaeDqrynSp4sDYoNndwIOhoXZf8Vj6r73K2YbZl+q5uf7mupBSwVEfb7/eoafmc22qDblpbwKuXk2X9HvYFfFQHQ1SGg8plZGuhk7WFLuYMyGfJLQZ4q8kkGkgnWpAcvFIkIkFKDbRlsC3Qezx/Rizu2GvfYF2NTVRUgdy0pCxTkCtuFZLOLA7JeuTuF0vEiaRB3BvKf/AMV05+jE/d9l+MvO95xpa+zZiy2bqYRFkFY8oWMaZ0IaPLKlTRitHlsxhd8jpDvvxXPnXbfzDs8z/gO0+8wsbqFI0paJKc5wqpY0SjdSjYFQuIL7OkUjOpxzr0Yhfz3fHYyP6tbYETZQEbeE+UJe31nBQLyA3TwG6uiqBupHLPj0FMcvEmJXiiHuJV7Hvtr7wxchCvn9+f2QN6sqB/mIX9Ayzq52NJXw9L+3lb24o+flZ247a6tx/T2tmhd5DVvYPKn3FNcZBVxQFWFAdY2TfE0r5BFvULsOTcfBYPCFPR28+yfiGWyLr6htWy8pvVvQJqHSt7B1nRR5q/tbXbVje2v9hB7L+mWLeOdlnWy8OSvj4WnheEL4zhzf5OFlwYYsU5IZYXe1nVN6Bax991i8+t11WIFX10W9k7xJGaXMcrioOsKA6zsrgnK3oXsbRviEUDgsztH+LV9xcz987JsG01NO/TwBtrVNCbTomaGieuCgJLcrLj++sIuuazAd42yM3CbyKSDZqTwjuSHKyJZLxWBdBFUyABZSu2xSn57v9x5cRHuOb2xxlz/3NMnvYvlbJstPjzPjCHIQ/MZ+y0RYwpm8X4sjcYN+U5rrr5P7jm5sf4zhOvsbtJMBok729MKqcRI5OR7cVIJeUbDd1yb879M/dr6eVP3adzAtpyl7WnbQucagvYwHuqLW5v75gtIDdVgV3T5HWeTCt5RUKVWyAVi0OqhXjDbnbMfIFljz3EwrtuZcVdN7D6rkms/OJ4Vn5xLKvuGtXa1tw1lnV3TmDd5yZ2y7b+cxPZ9LlJ7drGOyay/vbxrLttAutuGc/6Wyew4baJqsm8NbdNYO0dk1h952RWfU73Kz47gdXyuzuvZ8PnrmfDZyex6bOT2XrnDWy8YzKynUO17mr71uO+YwIbPjuBzbdPYMtn9fnYcOekVntt+fz1LLtlFMvvmsj2h77IyvtvZtndk1l9x3j1O/mtnLP30sw5ad2X0/DvYL0c62HaYfftzgms/5y0cYdsm+4cT27bcMdY1t0+kjW3jmT1TSNZe8M4Nt06mbW3jmHDnePZeNdkFn7+etb9/DHYJmnJ6kgkGknHmiEehaQEr+Wqrcd8O2v3AwO4HXsDvG29qLvZppRVUVfjpNPi0NVIRlKnRSM0JaAhAcs2tfC1Hz/HNdc/ypXX/5DJU//JpGlvMfyBt5Vf75iHlnDtPW8zdsrrXD/1ea679edcPv5hvv2r19hRr2G3rjlKNB4hmRLAlRutLqusIbzdYdgfbAt0CQvYwNslTlP33kmjGuQqva3AKxJECySjMRLJiHoAUL8L1i+FpXNgyzLY+i5sX9xJWwrbpC2Hrcu7Xy/HLIE5qq1s6zetgI3LYcOyg9vGFbBhuf5ellHLLtX+jmo9K2BTdv5mWedK2GLactiStbXpu6PdzfUmx67OwTIQu25clrWXnJMVsHmZtt36RSBty2JYPUdfz9tW6SwCm+Q30rK2Pda+K9tf7fsS2NqhbevwWb6Xf+fSb1kEmxfCxgWwrkLbXa7ntfNhk7RFsORfxJbPh9pKmlvqaIw1k4i0QCQqSb8hldAcaij1OG7PZhUd+zbQNVkcDgbejEqLFiOFJGZMEEtHiSZTiIuXuFtsrYJ7HnqSK8Y+xJgvPsmYr/xNVU0b99BsBk+d8f/Z+w44q6pr/ffy3vu/3ntNeS8veSnGaJQ6vdGbBQEV7AWYcqcA9oKKDdHE2EUsqFGxFwQGhhk6WBOTGKOJsSDCMDO3n3K//+9b+6w7Z64DAtJm7j7z27PP2X1/55y7v7P22mtjzOw2jJnxOIYedy0Gj70INy5sxa+3Q9QY+LPKNiWSnUL4xUoFaBrSMxtvBPtLiGnIHFUGleqqlPcLwGOzWgT2KwKW8O5XOG1hBwIBJbzqq6RX9qDvTMLvMrYgUxkHsUwcaeq1dWwFPnoP6KQ9na1A4hMgud24RDsQ5zbEnUCSahAdQCoPffY5sdNgkOgUI/vcWQo8pyNGdNF24yS8C4juBLragViHcSwj6xTXUDnEOOxYL6/p5yPu+rxJ34NnkFgSZ+Kt9yTJe9MFxNuBrk/hRrciuvMDwO8EuGiIGCruYXz35rwv459qB8RtD3y9bg89bzu649LEczuQ+BSIbwNin5pnnhgkPgISvzN+9EM40U8RTcfxUTqJHb4PhzP4KQ/gdmdusGqNXJSs8AscSnR3XYQSXtonow5XyMEH9zpL+mm4olnswif9TSVl8ivmAO987OPcWbejYMLF+MHYuTi+6VkMq3kKZbXPYWzTixg07mqUTLgY19+xHL/hzyOAjiQQDQQIsWg7nGQXfI/SXaooQNZOONyEIxNsrLYbwkshhRLgXffRxlgEDg4ClvAeHJxtLV8AAVVpUF/N8/jch97JwE9xSUUGCfhod2NoT3cinuhAtONTZNIi/kXGT4sURKQOoWXWHDIcpPPSURfRyxhHCVGuIzZ0lBw5HvHjlqpGXzruJJDy0+DKbQ66xJDp0hlKnMwiF5atZfiB1iPrDLt8xb673+b5I6HIOEl4fkowpJYo/4hhNN6BnYmd6PLj6PBj6EICyQzvSdLcP+pVIhVolap2abrXML0P9PU+dLdl/78HaaT4ZOzSfV7du88b7rN5VvkkGmeeQn02+UT2eBYzKVmA5vpM4SKFBNLoRCr9CRKJbWiPt+ODWCfed1PYTl3WDOCmHfhpSndJOs2Wul/gZ02y7jPhDTa84KI5yn7lXUzHRP3ApZ6x6yKe4l0G3t8OTK2+CYXHX4yCSdei8py7MazmEZScfhsKxl2AKxc8hV+872JHAtiZMJpitPzQ2bEdqVgn3GQM8ltLzu2HCG9gH1gJbW8+hRNKenOxkt/i3EB7bRE4gAhYwnsAwbVF7x8E+IOpP5r0lfCKpNfxEY0l0JFOyZ7321MxbE9G0ZVKIJ5MwHV9EYqQ44qjWSGOEjRvSamNn4afSeStMzIdynUCx5XffkoM7XMa0/OTSLkxpJ2Y6An6mZScp1MxWbWeTkVlEQvTMU3aiwdEKwnXi2VxzWQSoFOsOQ1Lp9f56BsMDMbgYqQ02UYSxNgJSCz1NCll6+j8FFFuc+tG0ZHciWSqSxYteX68T2OYQRLq9uUZ4DMqH7P8oOW77LviaKu221HyyQ0QaFHAg58xaamP66WScNJxOODzza0VOgDurJZJoj0dxYepGNr56UGC7EbhuFG4iAup5qcKPwIPyKFMWP3sD5iKVU2t3IRCzaNRpcvo2+rHK5B0gIQHbO0CzojcjIFj61A06RIUnTwXRwybjituXoL3KQTn8jdu0y67uaXF7jB/IP00P8Rc+Q1lV/nbSes42pzeSG44zBLeA/J02EL3EQFLePcROJvt4CGgP6BaY5gAczxI+1yyYX601adWW0rtlgX6ZhwYZHBgJp0ppMhC2C8ZsHXG1BC/BLp1BkVSSyIcLJpRgiFpvHRgnkgHWePvHksOrxZrYmCk6lxw6VCEGODOcM5Z6FeZwZhkRuWW5l70Mwz5jOW6z3tO+Jyqy77U+kvRi893X9OJuJI/Cpy3oJQ8hoyzE0h3iqQ96bvoCDRk5XnNxOEjJumM5Jn36SARXvnN0q/24AeNPQkIqOkp31k+EynRvaX0mr93nXEgmQF2pICJ51yCIWPPQelJtYjMvRMfRc02wTRpFqf0VrQ0+HngwOEiPTJbBga/l6JRIaoNAmRWXUF/o3N9FVQw3B4WgUONgCW8h/oO2Po/F4Hefiw1jD+j/D2W7TaDbTg1TH5i+U9ILX/EOalpKAMlPfIrLvGBLl6+n6vYRqEJfSiEoZEbFg7geQhCZtdoExMKkLQhkqAJ89Rnt8W4FUETpUiDnVzyiWVYgI3MaIRsv3pULPXN1q36PmTx3pOTg4G5PlN76vMjNew+Lx/f7eD9Vpy06711j3HESvCipJKqqVTLlc+5gDAKgTa/K/qZJztO8KOEFhJooksUIUh4v+DRWyP3MozPCNUt2DazuMxsEMHfOy5iY6/Yj4/a09ieAM5tuAy1l1wr2xLTzu77O6ISzzQdcVp9CD60yHLZloDsGp/Po3k6DWqhd/kLQmGzWwQONAKW8B5ohG35XwgBDkwqJdCCsgMWBykvkx3neK4XOp1pSK0ZzswgZXQXs9KzHj/mOcRMB549SfOZgSE0UGg56vdWHsO0jIPpsy4e2rYcn5dKYJV7ZNMGbVbOoVllmjUYKyVtbvmmRvNfMx2q/h/ierX7WVIR+sjgU5vFmjqkVM8JPu40XLiepttXn4UeKhwUANavjg+cnmvbwteaJ9vf0Jcuw3rCplCJH0R3J2K53KKXNDb7EWzK4/POppB/w/H4YwNw8wffMeoR0sgeJe79hfZlV37Qvh6dCKcN4wJftvtlb6jfTcIr7WfffEN6qbbwzgcfI+pm0OX5+Ki9U8gutwym9jc1nbm5Rtb+r9YVbof02/ymmhu19922OSwChwIBS3gPBeq2zoOMgI4KRsprpiGDQVJHBI5u/HHPjrShJjI89IMveoDgnvKcdqYLytfBQdNrnlBRchpOx3Meh8oPqt+dp03bXZpw3N6ml7yaKd98BY791r6HCJtGh/1QsnDwFzvXQg+Wz9ePuyWqxJrvCjkUAyi1Frm3IfiCi+zpSylmXHTJqRRDiaQ2d1edZ/yuXDiPppGwUKHZ02yC4F0PZz6U59qubENN+wx+/Nnqxjjp+4g5DkhuU5mMOEqAHZckNzA3Fug6f6ZLWr76+pv3mYQ2wCJw+CJgCe/he29syw40AvzxzootdaDoJrGGyJqBWdJx8OQgnTFWCcxqcGMSSBfEfGZ0lT7kEG2Gse7s4HGgO2rLtwgcXgjIzI18LhqeK5JqJbwiaaTtBF84sLRc1AyofxBFBnHdBkH48e56pq/Zrvzd5e0TcdqxnMZqMH95eM7viKTrIJZKIp5OIe6mxeeWxI7jyEY+vCfhI/c6HGfPLQJ9EQFLePviXbNt3n8IkPAG0gqVKnFajwOE/vwb4+qmSrFDmfHhZDgYUz/OGD7qQXjJnZlZC5BTJb1B03Pi91+HbEkWgcMfASW8RqdedQeCVzGX8PJd4TslOrRx0VPtIeHVdyn0vuUiEE4SPs9N1+eutTM5Dddg9XMJb8IxhDeZNjbMqTaWe1jCm4uIve7rCFjC29fvoG3/F0RAJbtGf82Q3W7dt2zhwchBjxxZ5cAkvWZZSDBgSILdE14mkSN7ogHWtwjkBwKG8Hpiv5nvkCG0AbEV/muku9kPT57QBebHVINU0OJ7pG4X8Gl0rr+L5H0nWDuU02INVp/QpX1PpLz0+cEuvuOImcfeCG9OkfbSItDnEbCEt8/fQtuBL4YAJa9GD1cluzqzKoMtdQ0NJxZfNSCYRkz8Z2iNk1tuBiu/dYU5E3K0CQ6eSnk9Bb8abX2LQF4h0E14uXlEsFUv37PgneG7KMbZZLGesVZmTCm4gEdbu9mk3WRX2V3ovcsrUEOdDUOh51zIa2amuvV69Xcr7IeKsacWgX6FgCW8/ep22s7sLQJGjaEn4SUxpRNJruuLaSgu4Kb5H8/LyKpt2v6lnU7qw1E3zvFckZTQVBQNtUuigPRmB5zPF0TtbfNteotAn0TAECzOjpjd0rJroPidmDGfoPyg5G6+yZSPVNyDn6I5BWMtIcMPS32xevP7JCr7r9G5kKhClRJe84vHVOYIE16e28Mi0B8RsIS3P95V26e9QECHAkNyw2SXv/tOKiCwrgeSWdnlzfeQ8lxZ+BFNp5Fw3CzhdR3uTMSNBGgQ32xBqoOPlk1fw/aioTapRaDfIKCEl1vGyLYxfCECCS/jVBpJc1rcIjeZ8LgxmjG4TfNg8n7thvRmGXQwPdNvkNu7jujvjP7K5fp7V5pNbRHo2whYwtu3759t/X5EIDs4iJTJiHi5tSZcB/DS8D2zmpmDMSW8NO8Td3wkPbPbm5j2cdPIOEnI6CxG6jngGoJrCe9+vFm2qD6NQDfhDTY56EF4IZZQ0hkqC0Her0SadojNzmK+EF5uhU0zZaGvx/C5JbzyfCgkJLp63sNXVaw+/TTZxlsE9gwBS3j3DCebqr8iwF9/PUh0KTQiMxV1BG7FRLKbgu/ExVBoIhGTXd1Icmm8vTOVQXvcRcLxkUq7yPgufC+FVKxTfCO22s24rHVb3yKQBwgYoms2k5ENZbiRA8mpsrBAIOt5DlJOXDZCiLuuvGPxNJDmx6Xjwff5PlLk64uaEWde9OCOdAzPbp4g5Fdj89NXeHP9/ETD9jpfEbCEN1/vvO23QYAjgM56hkcDUeCl3U8HXrITbsoQWOrqRlMuuPc8h9snXliJn737IdIZICVbMvnwXO5BL/Ov2cLDRYfP7W2wCOQbAkp66ZsPy89KaTmbkkxFEUtHZSPf1ZvewCu/eB8dKSDuUFrpwHGjIDEOH1q2JbxhVLq/J8zPnRpg5JU9LAL5g4AlvPlzr21Pe0PAjACBVDc88PoAt+dMR4Xsmu0604imPMQzwPYU0LzxbUw+uwFLW7cgTolv0hGpUrSrXfIyv7LpMMk1w4yybOsrRtbPn2fB2K3uJlxiCSX0kvCjMZ7owM7YDsQzLuZcfRMuveFu/OLDBNqpZSQLSx3R9ZVvUy8DbqJgiuDOYbTxkItnbz8A+RGmHwK5fn703vbSImAQsITXPgn5jQBHyGBcFFUGHXQl0EEytkMM3TNRZyKNmAfscIAX17yHs5tuwsCKk/BS22si7U3QikPGRTS209hREilvMGUb5tJSNsM1LndgttfZm6I3x/rdD2ofx0I3aTG++fmhsDdLevn4uymkU1EkPbOr2ux5t+I7hcdj7q1P4pdbXUQ5oxJsm6uvbLeeqiG8xpp27ruU5z93gc6uEt/8RsP2Pt8QsIQ33+647W8PBGhqTPhDQHy7Nxzi8OnA9RJw/RTaowl0ucC2BPDCug9xZtO9GDSyDj8oPwUtr7wrUl+ziNxH2onDp3SYhJcL1zIeZEAPTC5xoDfhDnoO/kxnncWg/zwD1KPt7X4y3DiaITNT7tQIEpVe+RakpZMk0m5CthFuvOYufH3oFAyfOheN1z2K93ZmhPQmXKoSdVtY4Q5itOFLw2aOlxRrvrToS/JrJvJNXSIK7vFLYC8sAhaB/o6AJbz9/Q7b/u0WgWQ8Ac/hSGsGXjFoT7NIniODLQfLjkRMBl0uk3my+dc454IHMXT8PPxgxIU4quw0NG96R6ZZExmzX72bSYp0ynW4DWraLGRjFeLMYhoTnoKfSQcDvxIA63eTIYtFf8eCC8xIT8WCScY376LDrda4GYVRS/i4K4XZ8xfjW2XTUTT5Wgw97mLUX3Mf3t4aR5pWHNwMoslUdiexWDoebCbj9iC8hvRawrvbH0QbaRHoxwhYwtuPb67t2ucjkEqlzIYRnmds6frBAJxxkfQSiDkJ7Eym0eEAKzb/DtMit6NgwjUoP2Uhhh6/AEeWnYmXN7yDrgyQCLbvFMkwrTpkSHYN4RWhLgW7ni9hXNRGCbAlvJbU9ndSq/1TSa9e06eJPyOPzYBm/WTGxfUB2rJ20xLGD81Z85/AN8oiKJt2OwafcB0GjqnHBdcvxLsfd9A4GWjJgfSYxLkz0YV0JhVsW0zTZkbCGya8qgbx+b8Q/SyFdlxmtPjPHhaB/EHAEt78udf509Pwj3r4XBDoqdNHM2J0jpNCIhVH2k3JlChpL3d6ak85iAFY+crvMHnmfBwz8iIUTrwNo85/FsPPfBjfLT4fL617FxyU4xynvSQ8N2HMmGWCzSd8s+1wVm+O7JfmzuisCkOvU95KjqzfF9Ubup93MT2Wozdq7inTmL6RiNJxVkV2UKNZMTeFTDqOJHcyBBC5ZjEGn3gVhp1zH8ZWP4zSU+dj0LgImq66E+99EhcdelpP4YYwVIOgSgN9kt1uZ9Qasj+Eecb3+PsjVjG4yk9dFgx7YhHo/whYwtv/73G/7qHy2e5OUjch5PSH3Te2OblQzM+k4GY47ZkEMi78FOmqg7QTE71dDy7akwnRy92RBlZs+QiTZvwIg8dficrTF2HYuU9jbKQVBac+hG8XRfDy+g+F8KbM0jX46Q6A2wtTF5FSXVETpgahkR7zWvSGpUmhwSfbVhuWHZAtJt3kpM9g8Xnr6+SFEMLLd5FOZ0K4wQvtXsNLIJOh/rwjhLfuintx1PB6jDhnIcbUPoGx9U/jyPHXYPC4S3HRDUvw3rYMutJGXaF95yfBQlMXLjw48JAWeS+XuVEe7ADptHkHswsAu39B9uxMP5z3LLVNZRGwCBx6BCzhPfT3wLbgCyCwe6mLCFcAACAASURBVMKrA29AIGVwM4TXE7ltComunUbSGgyESSeKHdGdQl07PEN2x591HQqOvxqjznsIhVMX47hZazDpklfxvfF34NvFs7B0/ccyKKdFqSEKpHfmEF6SXUqaKDM2C9WyhC60gE0Xslmfkm/r+iYGorfTO0nv1usxu7tkHPiZhDjpKz9KfRLRBOB3wvWpmuDIx2T9lffgu+XVqDrzXoyufQajG1eg5JwlOPb4WzB43BWovWwRPuwEuBUxmazrJGTjF7551OQl4XWRguNzvoazKz68hNm4ImC+e/krZAnvXgJmk1sEDjkClvAe8ltgG/BFEFDCS98MXMFApBE6LslAaNJQypOR5S6GfCaTlPC6cNwEOqMdIphtT0J0c6fU3oQfjL0AZaffgZHVS1A141mMa2xF5fRlKD7tEXy7tAkvbfgYUQBdXicy6ILvtgPcXtjh8nEl2F3w0Amf8STbHHj9GDKZLussBv3oGeAz3e18P4rPuk74vnEyyyIfm/qi8kMnDj/D97ATaaSF8M6a9yCOqGzAiPMexej65Rg1ay3GzdmAyunPYODEBRgy4SLUXn4XPmh3ZCti/U1Jp5PwMmmkMjG4SCLtRWVDi+zObtnfDc2xp762d0/T23QWAYvAoUbAEt5DfQds/V8IAcNrueyl24ULpHaDCI4CKwxGj5a6fCS9nOwEOuMxWRzDshJJF7EUsOGnH+G0yHwcPXYWys68DcNrnsDoxpdRVbcMIyMtGB1ZjcFT7se3yy/A8xs/QRfLyXArVEqQEmZuNbDK4FGyhBgckPTmEt5YQIIZbp3FoK8/A3yed+e0f/z4owqRm7UQJqo+GU9UHPgepRFDNMO3xujwfqtyNoadtwSVM1ageMYalFavw5hZ61By9uMoPe02DBjbiLrL78THHUabKJXWr1wXKbcLafkgpVoDtwHn74Uemk6v98S3hHdPULJpLAKHEwKW8B5Od8O2Za8RCBNdc264pgp4Xa5/EZcB7XxmCS8XqnlUQiAdBTri1Os1QtnNb2zF6TU3YeDYOSicegsqah5Dcc2zGHnhGpRHVqJ05jIcN3s9Bk66D18eXIPFK99FO4AdPqdfk7JIhnU5DkyZoJF8Q3tJsTnEazt0wY71zcIli0NfxyH3jcztDy0m0KVFv5YqCEkPSDqBczNIug6SmSRoDHC758m7dd6Vi/HNqkswbMYLqKpdj2ENP0Np7Wsoi2xGVWMryqcvwVHHzUPJlMtQP/defJow9nmpSs8FqXwDHa8TnrzxvtTJ992QXkt49/qH12awCPRBBCzh7YM3zTZZETBSFiMlUglvtxF6Sm9lwHN9OHRUL/B9szo8ILydrtER3B73kMwAP307hqnn3YiS4y7HgAnXovTchzFs1jKUzVqFooZVKK5rRmltM8pnLkfJGY/h++OuwQMrP8LHJLwA2n2gywdiLhB1IAvfqO6gjtsSJzjI+0CaA70P0H6vdRaDfvEM8HkOu96ebTMHIs98zAc6fSDqAl0OZHOXKMMA7Azeqa0Aam54AUdOuAmVM5ejdOYGFFe/hoHnbkZx5HVUzn4Vw5paUXLuoxh6ygIMPu5CRK5chPd3GElvNEarDbTYm0JnbAe6klFRW+KaUkt49bfU+haB/o+AJbz9/x734x7qtKLZolelumHf9Yx9T0p56HwatA8WRHEFeKfvYrubwdYE8KuPgbGnzMWgEZeg+KQfY1zdcxg9ayVK6pahdFYrCiLNGDRzKUZcuB7DZ63BUZMexNerrsZZc5/BdY+04YaHluKGRc/h5kUv45b7WnHzwrWYf3cLbrxnpbjr72nBjXevwY13r8P8u9bhprvWYP7drdZZDPLgGWjB/LtXGndPM+bf04wb712OGxauwA0LV+KmhS3i5i9cgfkLl+LGhc/iukXP4obFK3D9IxsxasZC/N/YBSidvgzDG14T6e6w2W+joOY1DJ25AeUN6zHukg2omPEEjhg7Fz8Y3YSmax7CxzuNpJfmzUh/qcaUcpKiypR0qdpkD4uARSBfELCEN1/udL/tZ0B6QyOXntKnYXsvYxakpdIxWZhm5D5cvZ3BtlQK2wG88X4awyZfguITrsPQE25D+ZlPYuIlWzC8oRVlDa0ojDSLhLeofiVKGFa/BsdMW4JvjLoFAyYuwICJl+H7Y+vwgzF1ogoxaOzlOHbkFRg07mpxA8ZdjQHjrsWAsTdiwNibMGjMjcaNvR6DrLMY9Otn4FoMGjvPuOB9GDRuLgaMvwLHjLsYx0y4RM55PWj85Rgw9kIcM7YBR42px9HjZ2HgSdfgiHE34usjb0Hx+csxfNarGHhOK4bNeUvc0JkbUVCzHoXVqzDmonUoPucRlJ5xO74/oh6Ncxdhe8y88bF4UmZ4UilKfLnEVX8p+u2Po+2YRcAiEELAEt4QGPa0DyLAMSvXsRvUz6PB+8DcGE0RubTtCUd0d6PRKOKOjw4f2PDOp6iccgEKJ83DkMl3YUxkFSpmtmF442aU1a5BSe0qFNWuQGn9SpTPbkNZ0xoMrW3DoPNWYMhpz6Bg6mIUnX43Ks6+CxVn3omSqXeh6OT7UHzqgyia9pC4wmkPo3DaIxg6bYm4wqmPg65o6k+ssxj0g2eAz3LY5T7Xj6Bo6iMomvZw9ztx2gMYOu1uDD7tbhRMuw8F0+5H4dT7UTJtEcrOuBelZ92F4jPuwsCTb0fxWY+h4MynUTJ9JYbP2ozi2jZUNG5ERf0mVES2oKh6PQpmMGwDRs5pE/WG7427StQbLrrxUXzUaUhvPGEss3gut7MIZoZCG2Nwq2M9HCrh28MiYBHoNwhYwttvbmWedkTJrmo30NcwLo2RzSTSSHvcaMJBwk0imuQSMqNnu+HnH2DsGRehdMpcFE+7A2XnPYeSmW046ar3cfTpzSipIeFdjbK6ZpTVr0B542qUNq5FQf0GFNStw6jGLRhVtxoj617CmIbnMTryEkbWvohRNaswpr4VIyMrMbx+JYZHWjAs0oZhkfWorKdrQ2XDagyPtFlnMej7z0DdWgwPu8/cUz7rdC3mfeA7Ub8CI2Ytw/DZzRjR2IqRDW0YXb8Wo+vbMK6pBeNnNWPcrOUYUfsixs9eg2F1LaisbcPwpg0oql6OssgqlNasRlX9Rgxveh0VDa+gOLIBJXWtqKh7GcNql2DwlPkYNOECNFz1AH7zKZBwYCw0+CS7brDbG38weh5h4tszxl5ZBCwCfRUBS3j76p2z7TYIhIkuWaxe0x6ZSHAcdMXbZb8lGqD/pDMqVhliHrB6y3uYdP5cFJwwB4UnL8DIumdQOL0Zoy7+JY48vQUjL/olSmrWobyuDVWRVaiqb5ZBtri+DUWNm1DW9Doq615DxYx1KJ+5AuXVL4tfOqMZZTNbURFZh4r6FpTXt6CsoUVUIUoa1oGuuLEFxU2rJJwqE9ZZDPr0M1C/RtR8qOrTw/V4ts17UNbA5964wsgKFNatQnHtGpHSFs/ciJIZ61Fe3Yby6lWoqG4Wvd2RDWtRUdOKkuoWeaeKq5eiqqkZw+pWobKaBHcjKutfEasNLKuioQ1jL1yNkvMW48jxV2PIcZdi9ryf4MPAZFmaNrLlx8LshMgfE9clCTaHsc2tPyb07WERsAj0dQQs4e3rdzDf20/hDMcjjl86hvFathc2G0xQstse6xCiyy0mtsaBtje34azGH2Pg2NkYdMJVOGHWC6iqXorKpk0YUr0RlbN/jqLaLSiPbMSwyFojoa1bhbI6LmJrRWHDRpQ2viaEt6rmFVTVrUNVA90GlEfWo6xuA8rq16Mk0oKSyEpQ97ewvgWFkTUYWt+KoQ3NGNq4HIUSHsRpGusbrCwOfQSHlqCd9FtzXDiOz3ngGppR2NCM0llrUDbLvEtlDW+iPPJTlEfeEDUFSm6pUkQp7oiGjSivWyvnfAeLa15ERcNyjGhqAT8wi89rQVntJlQ2vo6yyEYU165GaaQZwxqXomLmYxg46SYMnnAZLrjucdmGmDYbki5VngDPMxLedDoNlez6IgG2hDffhxfb//6FgCW8/et+5l1vOEDR+Ry8sqSXWwl7yPhpeH4KKT+NnWkH7a4xdbTs1W2Y1nQXjh1zCYpPuRWjqpfItGlFbTOGz3kFQ6vXoah2M4rqNgp5NYS3TaZUy+tWo7SuFcX1a830afUmlFWbwbiiYa2kL63bgOKadSK1omTXSLNUwrsGxY2tKG5aHrhVKGmka7G+xaGPPgd8dgPX0BrMZOT4Gp99zs1zX1y/Wt6lkvrNKK6j9YU3jH3d2k0oj6xDVdN68L0aJvq66yWM0luSWaoYVdSvAt/Jirr1qIwYfV5+bJLw0oRgedNKjLqwBUPPehjHTroZAyZciosXPIW3tzmgSbSUCySTpL9G5z+ZNAvbSHg9z+j7ZjKerAfIux9X22GLQD9DwBLefnZD8607tK1Lx+nIbtLrmy19vRRSThwJj/ucGduez234AJNq78Cg467GkEm3Yvj0ZzG8dgVGNrTKwFlc3YqimjYUVa8VaW9ZHVUa1qKydi2q6taIq4isQVmkDaW1a0R/kJIoIbskvJTw1m9CacMmWUBTWr8KZfUrRRWCecoopWLehhUobVgmC+GYxjqLQd9+BlajtD7k+IyHXTgu9Lxz9oPpKJUti7yC0tpXUCYzK+tRXk/9+ebArZb3jbMnFfWMowpMi7w3VAWpqN+A8toNqKzZgMrIBnkfGU/rKsMuWoeqWS0YeuajKDv7XtkqvPG6h/CLrdwkhhtTeOACNX44U8rLcxJe100HOr6G8HIRrD0sAhaBvouAJbx9997lfcs5/LjcwSxDywuG9HoOia8n9nYp3WU89XbbPeDFDe/h1KY7ccz4K/GDCQswvn4ZRtavx3AOtrTGUN2CkmpKhDbJYEtCS0fSSx1BSpFEdYHEN9KCyrpVqIgYokxSy8Gb6g7FkXUojaxDWf06WZg2rH61UYuo24DhtVswrG6TLGDjorXK+rUygHMQt85i0OeeAerW0mWf3w1CPklAd+2C+ywzIqtEJ1cWckZIVimlNe9NZcNKIaokqyS4po5Noi5UUrcWBbWtKGxcg6KGNplN4VbDVTXrMLxuvbxf1J0vbWzDwJkrUDZ7PUZeuA6Dz3gAx0y6HkeNi+DSHz2Mt3/7iag0kOT2tMpg1BmMdNcS3rwfbCwA/QIBS3j7xW3Mz06I/p1Zay3SGddJgc5z04bwZnx0pFx0ZYDm1z7CSbW34PvjLkXRafei7KzHMX7WelQ1bpHV3aJ3W9uKkbM3oOj8pRjBwbh6tUh3jVRpkzGBxKnT2jZU1q2UhWzcaricC3Aam0V1gTZ6acWBZJcSKi5aIzmmdHgYt0St2YxhNRsDaXGwsI2L2+rWm0Vu1rc49KXngW1Vx2d3bxyJbd0qeT+oNkTSy7LKIquNqkLDcpRGXkY5dX3rVsr7xHextNaoDhVF1qCgoU0c9eVJlkl4K6e3ykI26tQPm70Zx57fjOKG9ahopIrEcpTNfBSDps7HEaOqcdWPHsb7W2NIeUAs6YEb1aRSKVA/yvdSORLe0O8sf3yswDcEiD21CBz+CFjCe/jfI9vC3SDg0lQmhTEODe+6QCYFNxVFOp1E3AO2JYEXNm3FqHNuwaDJC1B01oOoqnke4y5Yi7LaFgypacXg2rUoqaOawmoz+NatwojaFlTVrhZVBg6yXLzGaVc5D4is6A+GVpyrrm54tT2lTMa1BgO2IcKUWInUKrIGVJEwpMH6FgcSyL7yHKwLZjzUNyo/qvrT09c0PX3TV/MO8AORju+PsWzSbc0h+25FVst7yneV74/RAeYCtbUyC1NVuxlV1ZtQVb1BZlLEegMXvEX43rVi2Ow2DJvTjCHTH8MRE2/E0IlX4MLrH8cvPvBkO/DOmIuM5wMud2YzuzJSlcHnsgD+xMiC2JDpQ0t6d/PrbKMsAocXApbwHl73w7ZmbxHwheMCaR+g3l2sHenEThG+dKSB59p+hfHn3YShJ9+E4rMfQtF5T6O4ehlGzNqIgpktGBJZgyFcgFa/VgZS2vocERDeEbWtIcK7XqwuUHJLs0uU3ArhDUyOmQHaTL2aAduY2eIga66NuSYZ0IOBXQd46xuiY3HoezhkpbvZDzfOWvTmDKntTh+SDAsZ7e57+IPRLPo05szkHQsIryxUozpRfavoDpPQigoFVY9oZaVmE4bVbUHZjLUY1rAJw5s2iT1tbiJTOasNFU2rUVL9DI458QYcO3oWLlvwNN58NyaL2ERXN+PKhzNVGrgjm1jtDRNen783/PHZ2x8sm94iYBE4VAhYwnuokLf17h8EMkAi7sJJJEXC68R3IuMmkHYdLF29CVOmX4HSyZeg4JT5GFn3JEY2rpTFadxQYviFb6CgcSMKGtejpHG9ENOK2pWorFmJqtpVshkA1RdEOhTo5PYkvC1Gh5cEOGIG7F3768wKcw7uwRQw/R42S3NtmNpri89h/gzou9H9sWJIKIlo7y4gtqH3QElwtqwgLvea6biAVBeRynXo45E6vvJOUe++hhLf9SivXSc6+FSDoD4+20nVBlFvqF+OqnPuQ9GJV6B0QgMuu34R3v+oKyCxJLPGSgMlu0p4eS6HzCYxlCJfe1gELAJ9AQFLePvCXbJt3CUCHH9SaRfJeEIIr5eipV0XnV0xLFz8FMomnIXCExox4rzbMHzmoxh/YSuKZ67CmAvfwuDp61DQuBmFTRtQ2kS7uWtEzaG8pllIb2UtdXg5ddrTmD4H8m4plk4/95RY6SD+GT9HR5d1ljSY8q1vcehLz4P5WDOLM825UUfoKaHN2VAk+y6ZfGGVBiGwWbJL8trtVDeYBJausmYdqmqpt0u714G+PPXuKfGtaxHTgXxvqxo3oWhmKwpnUC94A4Y1bRbiW1LThhENzZjY9CTGn3sLji47CydOm4NX33wPrkfLDUZXSqW7vKIjxTWcVyiwJby7/GW2ERaBww8BS3gPv3tiW7SHCHDgoeldFxnR2c34LpKJGJxUWgatN99+H41X/BAjp12IISddhvENj6Ns+tMYNWczimZsQFnD6xga2SjbBHNRiyw0q2sT0ltR02LIrhDe7kFbpVZKeI2eYk+9RGPJQcNy9RoZzjCjqylkWnQWVfXB+r2rhFhcDjtchLzmEF4ltD12WGvtRVJvZjf0PTL+GkNehfQasmuso2xEVa1xJLpCdmvWZgmvSHIpuW1oMzsaRprFYooQ7/rAYkpkoyw6paS3eOZqeQePv6AVVWfdhaOG1WHC6Vfi3kdX4uMdLhyq8Mqua35WnaEn4aVU1xLePfyZtsksAocNApbwHja3wjZkbxEg4RWzZH4SaVlkQoFLBolECo5rhqSf/XYHai6/DSVTLkHFObdhyOkPoGTGyzjxil+hpHYzCurWiR5vkdjHVXWDbhUFXRyjUqtuwqs6icZXAksyS6lVT78VVXVGKpzr9xzww5Jje26xObyfAX6s9VA7CKkXSJyoNXTr5pqwkEqDpDeLOs0W3FSDCJfZbfWB0lyR6NbSJnYbqmrazIK5WpOG+SoaVoullKL65Siqb5YNMArr2lDetAVVTbTxu17MD46g5Ld6BUrPehhHDJuD4VMuxb1PbMS2OMAtKPgRzd+WsP6uJbx7++ts01sEDj8ELOE9/O6JbdEeIpCBDxcOEm4MHnXqeHA1tQvEommkPch2wpt+tQ3nXHoHik+di4Jpt6Nq5nMor16F4uo2lDSsA80bFdIOL/UDxbC9sSFKia8YxafUKpBY6aDdrarAAZzERH2ju0iyxkU2ZiDvzTf2R0mAjcQ3VxJsry0ufeEZCEzqqaqOmigTnx9/u1P14TtjdiIU036yK6GRBvckvmY2hO8Y1YxoykzMmfHdoVpDTRCfJbzNKGpYhZKmtWKOrIRbFNdtEN3fkQ1tGF2/HAWnL8YRo+di7Fnzsfj5N/Dep0CCm9MkPKT9jPyecKc1VWlQeS59o9KgUl6rw7uHP9c2mUXgkCNgCe8hvwW2AfuKAAmvBwcOUuB5NBpHIpY2pNcBaF2oK+nKLmu/+NTBOZffjSGT52HglFtx7MkPYeJlr2LEbBrON7umGfueZuqTJshIgGnuSPQTw4SXgzgXxFDHMCDD3T6n3tVag5Fe0aRSdmAPDfAMs4S3L5A628bePz6MHi3fBRJP8UVVx6jzkOyamY9d+fyQNBYYjG/UVvi+6Yel+iLtp4m/yCrQ9nVlZIXZ/IUS3xouZqM6g3n3ihtWoahhtSxELZ+9WXZOpBrD8EgLxtQvRcU5izFo8k04dvxFuPuJTdjpAfGMke4mXfOrwk1rRFkqsNKgZsmyi9ZksZolu/v6223zWQQOBQKW8B4K1G2d+wkBH46XhMsd1XxPdkzydWUJTfJ6kHDutMalbG994qBm3sM4etxFOPq4+ag6/3GMangZFXXLZWemYY0bUTijDeV1m2UzitK6DbJjGqW85Y3GcXtgLn6pbNiMEkqWGjdkN5roXnRmpFTc5YmDcEkjB/Vc34RxQN+9FGx3EjIbZ7E7xM9A8OGnH4Cf8YNFaLxPVH8wMyFG7Uc+EmXR5jqUNq4XV96wHnTGGso6DGvaJAvQhs1aJyoLRdXLZaOXqsZVqGxowXBu8FJNvftWsbzAd7Uo0orihnUoadwgW30XzlyJ8RdswOjISyg9/S4cOfZiDD7xQtz11CZsjQMdKSDB3chFemsktxmkkSHpzegua9YG73760bbFWAQOGQKW8B4y6G3FXxwBHy4JL53LLYUBIbyicJcRA/KOmwAtaSrp/dnHLmquXoySSVdh8EnzMWLGo6ic+QyOv3AtKqpXoaxmPQrOp8ToFZTXbwkI79oehJeS3cr6V1Bat0m2NKVaRGnDBrHlS/UI2vRlGH3uurY7V86V5eI40PPc+haHvvIcGHKqJHXXvj7jgR98PPI+cxalOLIB8nHJD8w6Wksxsyfc0ptqR0UzudlEm1hfoPpQZSMtMawUn5JdMVPWsAFVjRslL9/F0gbzbhZXt2J00xqMjLyIinPvw9BJV6F40mz88NFV+CABRD0g6dGMdwau78D1EvD8ODIZWn3hbJFjdpwgD6YT5d6Q/8V/xGwJFgGLwEFCwBLegwS0reZAIODL9p8+de1cEtxgszX6ng9abXDSMcQTHfDgosvx0JkBfvGJj7q59+PYMQ0YetI8jKt9DCNmPo3K6Usxsn4jiqevQ2X9ayit2Swru8uo2ysDLXdjaxO7nuX1r6Is8gqKRcq7EZWNW0SaVEpJVoORLHHg5nVPZ9QkqCpBQlzU0CbTr9a3OHAavm89B7xne+LYLzqqGnS74vo2+agsj7xmPjD5kVm3WXY1lB3SIhswvHGzbBrBjSO49TAlvrS3O/R8s9Uwtw8uqW4Fia1RMdoUbBW+EUUzVmLs7HUYUfcCys+8R2Z2SifNwZ1PrsZ2H4hmjKkx6v9ThYFk13Wj8LxYN+HlFzT1GCj+pcslvgfiZ82WaRGwCBwQBCzhPSCw2kIPCgKUtvhe4EJSF7OqJDAdlILvx4X0UjsvkQE6XeDnH8Qx57oHcczIWpSdeiOGn7sIE+pfhuyuVrMBZdWbZRCm6gJJLvVtKVniOaVSZRGGc3CmNCqYrlXD94yXBXAM13j1TVq1HVrS0IrixhZRe7C+xYHqL/3uOWhahZLGVShuakZJY3PWZ19FPahuCypqN6O8doPo41aKJQZjjYEWGcpmtohlBpXmctMY2tWtbNgoBLhwRktgZ3eTEOaS6u4FasOrn8PYyOMYeMLlKDqxCQvufwE7XLNALeE5og5F1QVRX0ASUJeV7vL3JSC8luwelJ91W4lF4EAhYAnvgULWlnvgEZDpRe6I1L3FZ3jG0RgmcxDt2gHXiYsUJ5FMiwVNTmW+/XES515wC4pOnI3Sk2/EiQ3PofjMZzBu9qsYelYLRja9IYbtS2vMQpmKxpUoi6wSyw2cMuXCtqqGNSifuQKl05dld2grm7E8dL1adm2rqlV/VXDNleYrUR5ZgbL6ZSivX2Z9i0Pffw7keV4RPNcrevanYSnK65eirOFFlNfTLYXsbFjdKupEFdXNqJy5HFXVy7JuGG3qTn8RwyOrMaJhrZBf7qImG1BE1ssmEvy45IYSw5peAcluycw2jGxYi+PnrELl2Ytw1JiLUXnKxbhp0fOyQC3mGWEtxbUZP1BbyNrVVXsMPswWwzkf0tmP6QP/82ZrsAhYBPYvApbw7l88bWkHE4Ew4Q3GJc46csjSBSjU4U2nokbXwXeQSMRk2+GUD3Q6wC8+iuLMxpswaNwclEz5EcrPfALl56/E1HnvY+CZK8RuZ0nNCpTVcbGMWTBT1rQGZY0bUVTTitGzWjGy7iWMqH0RYxqWieP5iNqlGNOwAiPrXs5xy4Nr9V/GiMhLpgzrWxz4LPWR54DP/Wdd7vMepIm8KHq0IyPPY2Tk2cA9j1GRpRgVWSb+6IalGFP/IsY2vmBc04sY3fAiqmqew+jGFRjZ2GK2/K5fB0qBS2vaUMytvxvWy45qND1GHfwxTVswmh+mZz6IASdehWPH1GLuHY+LtZYdcVp00R8L/VhW0S19NTsW+iHTr+ggSC+lnFAye2oRsAgc3ghYwnt43x/but0hICMPBynutUYTZT7S4pT00oqmD89NAq4TLLP2EY9H4WV8xB1fbG++txOYOP0GFE26DmWnPYzRkVYUnLUCo5uoU0id3WYjqWpYirL6FSifTfu9azDo3Bdw1KS7MWjKnRh66j0Ycoo55/Xgk+/GkFMYluNOXmjC6J+8EAVTFqFgygMonPyA9S0Ofeo54DNbOGVRT8ewsMvGL0ThFLp7UHjy3eIKTrkTg065AwNOvQODTrkNg6f+GIOn/giDp/4w5H6EIaffhUFT78Wxp9yPihkvyiK0ypoWUH+3ojGwoz1zDYqr12J4ZDPG0uLDOUvwvbHzUHLKXNxw/1Ls1XLGtwAAIABJREFUEFUmXxanRTu7ACekpsDfEXJf2vAOgiUoE5DfHGbLS6PSy98Xe1gELAJ9BQFLePvKnbLt3AUCRjqjhJfSXe6W1C3lZTYfXiqJWMdOuA7ta3K6ksvYMtgeS4jJMpLek2tvxZDjb8CRY29B2ZlP46RLXxHbnZUNXBG+AqWRl1HasAKVs1pRWLcKR5/2OL41+iYMnrQAJVNvRsGkeRh04hUoOOkqIc8FE69DyUkk0r24k+ajZOIClJ54s7gy61scTrwZfek5YFuNWxD4ep3rM55uvnETb0DZxBtQetJ1KJp8LQqnXIPCKVeJGzLlCgyZcjkGT74MgyZdisGTr0TZ6Qtw1Phr8I3hV6PsvKcwumktCs5bhaqm11A15xWxs0u93pGNND22GhVnP4Ghk36IgeMuwpW3P4udgLzjXamUbCSRZbWhRWgkuzyUzArxDTSlwqSW5yaN+cAOx5kS7H+LgEXgcEXAEt7D9c7Ydu01AjoYhX3HcUBHSw602uBn0uJkVbbvIJ52sK0zKYvZtkaBU2fegOLjLsGgCfMw/LzFqKp+FqNmt6CsfiUqZ9GubguGzFiKMZesxzGnPIRvVlyPi3/8ChYt/TXuf+lV3P/cevxk6Wt4+Jk38MDjb2Lxkp/hoSffxENPvdbtnnzDhGnck0z3poRZ3+LA56UvPAdso3FvBL5e5/qM78U9/ToefGYLHnh2M+5/ZjMWPfMKFj67Gfc9twWLnt+CB57fgodeeA2PL/81Js24DQPGzcPw6c+iaDotPryNwbVvo6DmNbGQwh3YxjU0o+KMRRgw+lIUjW/EDXc+hy7uushFam4GSdcx5gv9EJPthbH2EvSZ3yL9jflMhA2wCFgEDlsELOE9bG+Nbdj+QID2eelkm9AehNdsWMGBi4Ke9mhajM9v7QRqLr4NZRMvwpCJ12DYjJ+gfMZzGHXBepTUrkZJ7SoxgF9W8zIGn7YY3xt2HR5b2Ylt3JYUQEcmI9sZuxluegF4svkFwGuRPNMP4hgv6XhtncWgjz4D2eeaMyvh53wPrpmXcy60j8DNYWKB4znDOtLGfNjF1y/B4HFXYNTM52RHt4KGX+HYmrdRWPs6Cs5fiePmtKHq7MUonngtJpx+Na68cbF8xPJdo/YCN6ZxPP0t0M0k9oTa7o9fIVuGRcAicDggYAnv4XAXbBsOGAKe58H3ffi+K2oMGTiBlJe7s/EciCWp3kApkDFX9M62JGZcdgcGH3chhp58KwrPeFRUG7hLVEl1C8rqVmLkrNUYcMq9+OrgWjzZ+oFMm3YhjS50IoUkUjRmz9XgHuD4HHSpX+zK4CuDMMMk3AzIHJStsxj0xWeApJXt3lufeVKeLxs/xDKG2HbRPq4H0JJC1M2gI+kLEa6fex++WVyNyvMek+24i+peQ2nDz2Sx2pjZbSg6/UF8b9RcjJx2PS5f8CTiPrAj5gWmczOgrV2+7/zw1Y9fqjXZwyJgEcgfBCzhzZ97nZc9pWkh4yjV8UDCa0gv7fNSxQGIpz0kXTM47kym0Z4GfrXdQ928h3HkqItQcsZ9KD//WZTPXI4xc9ajKsLV4itRMO1+fLusDi9v/gA7qA/s70AcO+AiKvY9lWx7mTQ8JOAgJr6HFCRMwlMS5mesT4wsDn3rOeCz7IC7Ge69zzwZ0aR3wT9dcMo3VAlqynNF0tt03QP4zvBGDJ+xBGUzlqFw+ipZUDqqvhmjap7EkMk3o3TyPFxz5zJ0kUhTcuyLtj58ONn3jSpNhuiqZYa8/Fm0nbYI5CUClvDm5W3Px07r4jZDeIX0UupLdYaOnWLNIeUkkXDSMlhySnXj252YfvliDDrhKlScdT8qzn8KFdNfwIQ56zGybjUGTV6IbxbOxIotH6BLpmKjcNEJH12yAxwL970UPD8JLxOFm4nKtqVyzTBxcYnj5hhMY32LQ196DrgNrz7Xe+vrFr4+YnARF+fwY9BPAk4CmTRtZyeNhPfaRfjOyDmoOv9xjGlYiRPmrMEJs5ej8rwH8J1RF6Nw0lxct6gFHyeBnS5EwmvWpPF9T2cdyS9JtphlkG3T8vG30PbZIpCfCFjCm5/3PQ97/VnCS9IbS0SN6TKPi9tSiCXiMg3aEQycr/42ifMuu08sL1CKVDj1foytX4mJF23Bscffje8UVmPF5t/JNqVJpES6m3J2IpWMm3leGVQ5wIrMiZO/gQ0JhtHxmnHWtzjk23PAbXvZ5yR8RMVlKM9lWDpmHFJCeGuuWYT/Ka9HwbQHMbpmKY6rfUbsZg+dOBflp16JmxevwQeJQA/YB1IZY6SQzxTLNI4yZEcMGBrCm4c/g7bLFoE8RsAS3jy++fnVdWOKTNQaRHePBNgQTkqAkokYfNcTm0OJuIukY3QSKel97f0O1Fx1D0qmXIbh59yD8jMWY8zMlSg75RF8v6QBKzd+JJtYJEViHEyV0uOupC6dl9Ud9LwM6BiWdZ5jzq1vceBz0Y+eg4zn77o/fN+oU+STnJL0xoGMA7gukE4DaUN+EwDqbngU3xx1CSrOWYKxtS9iwrn3o3jCZSgYH8FdT67H1jSwNWb0f7tSCTgel7yR7MaF7JrySXhZl6o52UVr+TUG2N7mOwKW8Ob7E5AH/e+hw5slu4bwOq6ZNqXEJ9YVR4YcmFHBIrKdKTOl+saH2zF7/kMYNOFCDJxwI8ZNfwlVUx/GEUNnomXjxzKFKovRZCW4KA/CpfBKDNp3D7CyC3LAic1gb+J4LluZWt/iEOyA0O+fB+ocUMjrUmeXeu4p+eCR1Z784kwm4XtJsdxQc+Oj+M64y0SloeL0B1F50jyUTajHjx9ajk9doMMLLD2kVWWBevKUGsfgZxLBljSU7mZEjSkPfvZsFy0CFoEcBCzhzQHEXvYXBLKsMqSv99mwdComO7EZaWu3toHnuEg7VG9IYWvXJ7Ij21sfd6J+3kMoPulqlHEntfE34vvF52DF+ncknjKqlJ8WqxAeNRS4U1PYFFrGhUdTZeTDGu67skCH11yosyufkune4smoxdpEIF1murRrFuQ5nlmgI1It382G85rqG9ysg/mTacrQfJF+MT/L4zXLCadjuOsaKZmsdg+1N5VKSHmM19XwLF/6GayVl4VIbhrhdrE+bY/Wy3wMp051bj1aHn2m3xVeGs5+yKYktMMc9Jf1a3y4fwxnP9ge5gvj3Vs7tD8sl/3mNdvNcrReXjM+XA/LZ3nhdmh79sXX9up9VJ/tyW03r/X+7Al++9KeXNx6q0faRRMmhIqCXmrZZhx4NF3CD0WuOEvzfTEqDTOuux/fGNWEAZNvwYAJ16Ly+Fm486GlYras0wWi5MeuwZ4kOZPhM22ku5zBIUbyJAS7qcmHpxXw9pcfe9sPi8AeIWAJ7x7BZBP1PQQ+S245rOY6N23UCUQSS55DCS99j2QhhkRqBxy/C0kuR/OADW+346IFz6L4xGswYNSFGFh1Jpo3/gI70y7iIqXyA6JG1QXPbHLBhThZywwkUn53OPN8jjN6h5ROda821zxpl0TTlSncRCqKZDqWXfnO9AxjvOsbyxA817w81/QsJ5bozKbVcjVfNN4h9fOa+cJt0jLphx3r1vQsj9PMrFvz85qO1+qnnHg2D8O1rbk+y9XycuPC1yxPy6bPdmsbtS6GhdMxnriwzeGyeK79DodrmGLGeui0/0yrYUxDp+0Il7Mv52wr2673Wculz3r0/mkbWYeG7Ut9+zMPzQVyRoXkk69d2vf42hl19jSJbxppLy4S3nOvuhX/XXk2ys68CSWTr8DdjzSjPQ6xnS3c2M+IrV0uEiXhhZ8wpFeeNyPZFe0JS3j73k+5bbFFYD8hYAnvfgLSFnO4IPBZUtuT5PZspwgzjUAvSEaxTzDq+gl4XpesFE+7jgijqNO76VdduOimJ1B6Qj0GVE7Cqi1vycIa2tmlHCmepPl82uCldI8kiURLrPBmKSGvjXNI9/basTV0aTFn5iLuxOSaZdGqcCwdRcpPZuOTXkLOmYfh9JmGaek6Ex3ZtAk3DjqGdyU7xQ+Xy7y83hlrh5bLtGwLy9S8DNO0TKfXrF/rjqa6JJx5mVbT0/+0Y5uUyTiNZ4l741gnndbPfvKceLFMxoXP2Xb2mXVoe7VNvdXLNAxnGubTdjKcjuWxr1qXhmtcb2XuTRjLZnqWx36xf2wL62PdvbWdYb2F7029mlaJ9K58EuTe4gxxdqm+GxBeLiGlhDz44OSHaIqWG5KynG3WLXfhayUnYOz512DB4mbQbm8qk5FvU2YR9Q9K0/20WfTGDzNZEMd3mbJd8x0rafly8sQeFgGLQF4hYAlvXt3ufOjsXhBeHQU1iwyCKmJKARkufHGQjnfBdX2kXMjiNJLeLe+24/Jb7sex5eOxbM0rQoZjTgopJ42uGM2LGcLL/Nx7jcO5mbzXgZfSUCNtNOFm0nVPzg2V8oQgJFzKnj10JrpkSpjTwrE0iQLtCtOoP0loXHyqW/CcxELjPu3YDpbBa4Zu79whahkaz/zRFMm0ycPyu5Ik0yRNJozXTMcrLYfXDGcY87COuJOQ+nnNvCyD6TVOr7VNTKd56DNe62C+PXFsh6ZjeXS8Vr89ulPawnJZh/aJ14oVw3ZVr5bJeJ6zDPaH94PtpVN8WS/TaT+1XV/UZ/nsJ9vLsuhr/bltZ/3qvmi9zE91jd05vUvhNBomOanew53QgnbJK0hVIJoHTEYRTe9EF1KYftmVKD/5bNz3/Fr8tpOfkdw9je9PcMgJFeb1/aVaTk+yy+9aJsvm0bzWtwhYBPICAUt48+I251Mnlb3uyg9hwZFPk3GQ5SYVYh6Mg6UDUDqVTsBLpYWlcuo1lja7se1wgLc+3IaaOVdi9aafytDalUgilfbhpDNwuYJNDg7rJLf0WUf3gJsNCw3EkmYPrqkH7HoZxOJUCQhIg5dB2vHQFaXuIhCNUd2BM8PG3jB9hqkeMX2tj3HMyzI1L695nkims2WQnDAfy2FapkmmDPFwXL9HezQf0wipCcrWOulr2zSMZWk+1sP+sT+sj2kZplPTmufzfLaLabbvoL1lIJ5IZdvJa9ap/WZarYd1MZz+rupgWxindXR0RiWPXrMveh+0jl2VtS/hLLOzKyb9YVt4rvWxbsVrX8rekzyht2mvTuU9oHqBvBl8zsw7ooV4bhqJZJdIzVPwcdWtP8LtP1mCD6IOOlzz0Whe3iAHGyt6SdSRINE1ZJehjBItpdB7JbkkT5DfehYBi0C/R8AS3n5/i/Otg8pgd+Xn4MFkwcCnkicOvjJg0qIDGYPD/YHNeOrQqL1DA0piMRRb3vo13v3wUxlQSeyUGSVjHHhZlw6+QZQQa20DK89m0ax75EuzXOoCm/wkWErMlCQq2SEp0jA2iY7XzMM2f/TxJ0IyGU5yyTjNS3LIcJbB9Ds7uuRaw5lfCSqJopK6R3/yOEaOGoOCwmIsefLpbJ+U0DIPy+O1+gyrizSgpLQc55x7voSzTNZFx3Tafu23Xu/KD+dhnxQj1ss8LJ9pNE7jGcdzjdtV+RpOHHhOwqth2z7dgZ/+7Of4ZNt2CdM6Gb+n7deyduW37+zErNkX4PQzzhJCzzq0LfTZh/1VV29t0Cd5b31DePnJ1012eaUH1YGS6ZRsyc079dZv3sP723egM+2hM26eyTRNl7FRPLKNYxk9yS6jwoS3+yu3u76gFOtZBCwC/RgBS3j78c3Nz65xENNBTwc+DetlgBMzZSYd6RTlTBwceUgUR0tKSFOOSHpTKU6Rm8XlXY4nU6sd8RSSrln97qZJkoFkVwoOJcPClLnsnHWEBuZsG3tpk0nZ47/oKIZCSAgchyzcqE7o+fbt27Fo0SL84Ac/QFlZGZYuXQoSA8ZzkVAikcAPf/hDDBw4EN/4xjfw+OOPSxlMs3PnTjl/6aWXMGrUKHzzm99Ec3Oz6CKznMLCQhQUFODpp5+WdCzLpc3U4GD5Ws9ll12G3/u93xP3xBNPSN3Ejnl4dHR0iM9r9oXl0D/22GMlz//93/9JPMtk2+hyMZAEe/BPsaF///3348wzz8T3vvc9wej0009Ha2ur1K9tp6/t1LBdVRPuP9OwD2zztm3bcM899+DrX/86FixYIOFaRvjeadi++jt27MDEiRPx3e9+Fx9++KH0g1ixXUma9eLCMFGM3dcaPidflmiGnu3PCyMDF/N7tDzCRYzd74AQ4eDDRGxhe0BXnIoZQNoxX3fU+03QLAOzsS4eWqcEBiL5IFjJLn1TF0vrVnkISrCeRcAi0M8RsIS3n9/g/OseR8HduRxEaOg+kAhxzNTBUcZPD3CTKYo3kUmlRK+QZTOOWxAnKIVyPcRSxiQV45LxhBl8ZTBmaSz/8wjv7tpr4mjeKtwvmnVKp5PGTJhvzJkxfvv2bVi48B4hjX/7t3+Nyy+/VNIwjiamPvroA5x44vES/0d/9AeYPv08bN36kZTFNDSfde65Z4Nxv//7v4c333wdXV0dmD//hmyZ9fV1UhbTJ5NxY2LLMSbMotFOMc115ZWX40tfMoT38cd/ImmY/pFHFmPIkEH42te+giVLSLZ9xONRtLdvRyIRwzHHHC31Mp59Zj9pKkzrYXqGsy+5mDAu1zE/w2g2bM6cWfjqV78s9c+YcT4mTz4Jf/M3f4WvfOW/cNttt2bzsh3MQyzotAwtu7d6WT77znYxXUdHO6644jLB8ZprrpKwcDmaTsvcV//DD3+HM844Dd/97rfxzjtvSz3Ei+1hmXuK077Wv0/5+CwH91DaJ/fNvJfUfafqECXT/HYkAU6mfLHewLhELGk+RCm55q3ly8hDXlie6DPQHWyIriZhvCW8Bh373yKQXwhYwptf99v29jMIfHaA1DGUSWm2jDZ5uWOUkUplRGrmeLRrS7NfSVmoRkkgpWoqqZT0UpeWTz/36C0sN03Pa9Ezpq5x4CjBo4vHuZSOtvqTePPNN4WcUsJKSS3bpkdbW5tIAxn3+7//+zj66KPx85//XCSQlAzyYB7G/8u//Aui0aj0afHixfibv/kb/MVf/IVIkJmOkkqVaFJ6S6dlNDY2Svksh1JklTJSovonf/In+H//7/9JuEpSiRvPi4uLpe7//u//ln6xHpXuaj/YXx5aJtsQvtY2SCA/OYL+RyIRXHPNNXjnnXfQ1dUFSkcphf27v/s7kVyz/Tz0Huo91fy50tJwvYxjmTxYDu9De3s7fvnLX8q9YZnabi2PabUPPNfy9Jxx4TDtl5bDeEp1KeGlxP7999+XOjQP02td4TwsnwfbdCgOtltdbv0MNws+jQoNdyVMpymtNlJ+7Uduvt1dh99nk2537+TuSrJxFgGLQF9GwBLevnz3bNsPOAJKfjjQ6iDNc5IKxikp4nXY7cvAvCed0Taor4RU28L2fPDBB/ja174mxPGYY45BLBYTAsbyf/KTn+Av//Iv8ad/+qf40pe+hD//8z/Hc889J1Wz/b/97W9BdQLGTZkyRfpIMk3yRHUJqiKwPPaPxI7tULUEFqIE66KLLsqSbqpAMB2P2bNnC9ll+U899ZSEsVxtP1UxSIj/8z//U+JYNvMq2dNymD58aHw4jGn0PrBdJKBMp3kZ9tOf/hR/+7d/i6FDhwpu7BPDiSsP1qf3WsvStjJey2IaxUPTM5559GOE1+HzcJtJkHkwL+vUuiQw9E/7pO0iaT/ttNNEFYT3Pbc+ZtU28pz5tJ2hYg/qKdsQdrmVM479IBa8F2wv8aHPvmjeXWGUW569tghYBCwCRMASXvscWAR2gwAHVTodZNXX8F3FMd2BOLR+9UkI9FBiQ3IwefJk/MEf/IHo6W7cuDHb/muvvVaIKEkeiS8lsLfeemu2j6tXr8Y//uM/inT2zjvvFKkl63jjjTdEuvtXf/VXePnll6VKkkKStpUrV2L48OH4h3/4B/z7v/87br/9dlx44YVS9h/+4R+KJJdpjzrqKGkT6yThpcSYUuaxY8dKOSSkYQkvJdWM++M//mP89V//tejeUs+YREjJ4vr164XsTZo0CR9//HE2PIwLGxsmlIzTeOb/53/+ZymD5JEH7ynJFZ3Ww3DqMbMdv/71r7Fu3TrpM68pCa+urhaJMdvGg+UTP5ZNHWbeL96Hr3zlK5gwYYLo+EpCQNpN3Wj2nR8cmv/555/HcccdB2LIjwBK3hnGQ+81pdUnnHACjjzySCHsSggZfsMNN+A73/mO3Ae28bzzqL6yVfJrH+XiEPzT51ffn96awDSMJ6bsF31Nr/l7y2fDLAIWAYtAbwhYwtsbKjbMIhAgoAPrrvxDAVS4LayfJICHkiCSNC6UIrH8t3/7NyE+jKck9eyzz5ZwLkDjwi0Szzlz5oj0k4SCJI0kjnmp/sCDZHHFihX4oz/6IyFet912m4TzH9MwLUkpfTol0iTcvKZKA8seMmSIXFMtgvWyPMaT1CkBpYRXy2HbmYbqDyyf/syZM9HZ2SkSWOLAtlBK/V//9V949dVXs+0KnyguDFMVCmJGaTVVHCjtnj59eg9yqwSZ6ZT0Pvvss/IxQHJNAsmPCmJH9QuWcfLJJ2dVQKjewLYRS0rV2VaGXX45dZu/JB8EbAtJ9ZVXXil9piSchJ7tJVlln4nl1VdfDUrM2U9+cDzzzDPSPZZJks92HHHEEfjoo48knMSd/SFJHj16tJRFdY4BAwbgrbfeEikzMTyUR/gZ5rk9LAIWAYvAgUbAEt4DjbAtv98goIP04dahsPSLbSNJW7NmjRAmEsZTTz1Vmvz6669nSef1118vU+Ekl8OGDROpJftH0kaiSrJJMqXStWXLlgkpI7G74447pDzqjFZWVooEkpJdkmWWMWvWLElLYkfp5MMPP5yVmFKlgVJd1quST5IvkkpKTktKSkTF4M/+7M8waNAg/OY3vxGSRh1V5iFJVyJOskiSS0sIp5xyipB2Va9gO/RDgH0goaaUmXkp5b7gggtEb5ftY59JRplHD6ZXokvyy7peeOEFIbrE9KGHHpKkWgdJLwnqqlWrJJz3hJJuSsuffPJJLRabN29GeXm5SGQpwd60aRP+53/+B1OnTsXPfvYzSUdpOskqJbdsFw/265NPPpHyaD2D7eFBaxBUafj2t7+dlQ6zTErTR4wYgV/96leSTi1wKPnX/BJ5CP4R6zDee9oEva/7kndP67DpLAIWgf6JgCW8/fO+2l4dRAR08D4cBmFtA6euSYxIEjlVzoPmxqh2QDK7fPlykW4ynmGcbicxJKFkGAkY9U0ZRuJHAs1wEtF7771XCCSJGa9JGkeOHJmVMJOEURKrElwSWz049U8yR+LM6XmWrcSSdXFqnvWQQFInlQcJ54033ijhlOTedddd2TzaXy0/7CsZ1TBeX3LJJVIOyTjbTpWDiy++WMi21qV6tmyXSp7pk+j//d//vUhkKR0mCaU+M8kj20QcaPKN/SCxpHk4SmS54I+H9pMSXUqrKW0/8cQTJV+YxBMjYkByTGkt+0+f+fnxQmJNssyDHwlctPatb30L7777roRRL5n3nOog/CjhwTax//y4oOoID7b/UB693TuG5bpwGxnHfmiacJw9twhYBCwCu0PAEt7doWPjLAJ7iMChGoDDpI5tINGiT0JGe7OUptLeLiV9JKq8rqqqEusBjz76qEgsSa4oheV095e//GVJ89hjjwnRUpJGlQZKfklUSep4UP+VeTnVTqkpD0pR2SZO9TOOeVgv28TjjDPOEAku45Q4k0ySkFGCSWkz40hseZDosTxO45OkUu+X5JKHSilJOrV8tpfniosSPV7zXAkscXr77bezusas75VXXsmSVU3H/igGxIiEd+HChVk7wmwHy6L0nO0mgdV6KOEl4V2yZIm0leEkryTUZ511lhBd5rnqqquyJJTlkexT7YMWLahyUV9fL+0kWaeKAjFgW9knflycdNJJWR1e5qdUmFJ2lkEd3vvuuy9rmYLxPBSv4PKAeburJxzH889zB6yRtmCLgEUgLxCwhDcvbrPt5IFGIDx4a129hWnc/vSV1IXLJAnkJgskidQ3vfnmm0UXlARr2rRpQtK48Ep1Zil1JJH7p3/6JyFuNFWmBITEipszMC+lsyTKDGtpaRFCS2JFgssw7XNTU1OW0DG9hjc0NGStNFBFgMSS+Sh15DnbQ8kwp/l5aD6qBbB+1vXjH/9Y4thHEnvmV4IaxoDh4YNEkOQ03E4SapJwSmdnzJghyfWjgYSa50qsqYtLck/9aLaL4ayXpFN1malGwIPEloSe6cNWMHiveHBjDt4b9unBBx8UkqwLyqhry/CvfvWrYjKNHyEk2pRGUxpP9YXf/e53Ug7NklGdg9Y4GKbknLjccsstQoSJJ9U+aIKNmLH/TKdtkYIO0D+9f7sqnvHq9L7oNX2GqeN1+Mi9DsfZc4uARcAikIuAJby5iNhri0AfQ0AJAputJIYkgdPeJFWcAicR4xQ3iZRKY0mKNOz4448XqwOcaucCKJIvlqFlUh2CxIllkfDxYPkkilQNuO666yRM06uVBtYXXmTFxVckzQx/4IEHsmSH+TjFTtUItplSadbPvpF0kvCybf/xH/+BRx55RNKSrCopZX4lqtqG3nyWSX1W+iSlzEMCzfbQDJvmYTzbQzyZhgdVMCjh5ocE28U4lsVzklpiQUsIPJiXklUuOqOEl+VRnYBpqedL0krVEn5gUI3jtddek3xsEy1esD2q0sE8PLQd6pO0UqWBhJ2qKLyfPEhq9WAafliQ8LJMSuqV7LJcdZr+YPvEZXdO26f+wW6frc8iYBHoPwhYwtt/7qXtiUUgS3hJELjgixJTXYRG+7r/+q//KuoLSoxJlkiEaKaMxJVp6+rq8OmnnwqalIoy7dq1ayUdSefdd98tcVSBYB4SVFpgIPlkvSRhJNgslyoUlPAqCWNeTvMzDyWbDGcdJD0kYrq18P/+7//2uJsq4aXVA0qimZ4HiSXoJFigAAAJRUlEQVSn96m3yrp5aJz2USW0GzZsyLaDdfIgaeZCOrbn/PPPlzD+Y1u0TUoQ2Q8uziMxpsULlss4HpSQ84OA6hbMxziVmId1mKm2QXUG9oNWGfhhQKLMRXTsC4/a2loJIz4kwOyHYsv8qmNMqTjbTzUKqi6wTYoBVTGUGNNn2/mxQl1oJfpMw/SaRyo/yP+0/j31D3LzbHUWAYtAP0LAEt5+dDNtV/IPASV32nNeK3mgxI/klcRTXVFRkejqqgoApZDUXyUxpWM6SiRJ5NQxLQkv46lSwKlxEjASLpI35qGkkuSNNnnHjRuXlQYzj1opIFGjlFTt/HIqnuVSN5btJgktKyuT8ijJ5aGkjZtUsB6qZ4QtJLz44osyzR+2YatY0FcyR5LHzThI/Ki7Szu1lKCybn4IcIEf2870bIdiyDKU1NK8Gj8M2CcSR+rt0rICVTtIhKl6oKbBSF6p0kB9W9apZZAEU8JN1Q7WQbJOyxTET1UfuICQi9Ao+SU+bCsl7lwkSNNolJIrmeeHCXH87ne/Kx8pel+44I35aNf3vffewxVXXCHSad5vJdHaR/qH69EX2ni4YmfbZRGwCPREwBLennjYK4tAn0Igl/Cy8QwjISJZpIUAShApVSVhJDmizqkSY06vU2eUkltKa0nGSLJ4kGwoMQ6bJeOUPokVy6fUlNJFSiwpQWQdJKtcXEU9XJJDkiw9SAhJiFmX2uulmS4eVA/gOctgmdo3toNqDAynGgBVITSO6ggkoSSs3MaXh0pKw0SO7eXmDsShtLRU9HW52IvXlCqzHO0riTnLIEmlr+SS5Jh9Y/tJRokbFwaSwFMFgxJbPVgGiTnLJ0ll2SSggwcPlrzUidaDKibEn4vUlDBTJ5pEnESY92zMmDHyMUDVBLabWBF/ElpeU5LPhYlsKzfIoE4vVSMojaa6Cj8UqC5Cqw3MR9JryaTeAetbBCwC+YCAJbz5cJdtH/MCASV4JIMka/RJskh6aNOVxIdkVaeyCQrPf/SjH4ku7/jx42V1PyW3LIskTSWTJME0f0VzZVygpeFMQ1u48+bNE0JGk1okXCRuJHIkajRppgSVBJK6v1y0xZ3Dzj33XCGzJGE8aJWAhJLSYrZBp+8psWRZXHCnZJHxVNuoqamRzRXC0/iKBdundZMMciEX7dZSkkxVAEpLuUCP9bNPVLFgHl6zDM3LcEqnKbGlBQsS/cbGRlHd4CYPVFvQtOwjHSW2VBnR/lOCTEk0JeTaPuahJJ5qFew3TYtRNYEH1TTYN/abjtL65uZmUWNgPNvEvNSpphoE1R1YHiXYvKckwvyAoJ1h3h8SYvaNmJLwhkmvVGj/WQQsAhaBfoyAJbz9+ObaruUHAkqe1GevKdEkwQuTNkWDZI6EjARP8zBMSR7TMZ7kiaSIB9MxvUo7Ga5pGK9kj+dMo4SYZfJgWraJJE3T0Nd8Wr+mZxzrZFlUMdB0DGfZumCNBI6H+uE+SUTwj+VqmxjE8hQf+pqfbdS6WC/jtE1Uq6AUm/Z1w31XUk5MmIcfDOGD5YXJuNbPfKxP62Y4y81tq9av+YiLfrSwfXqwLG2L3lfNE06j5WkZ4bSazvoWAYuARaC/IWAJb3+7o7Y/eYdAmPQoeSGJItFSkqdkiKRMCRbjlNzRp2M8pYY8Z7lhwkeixGvmYxkkr0xPx4NkTQmX1qFtY3lsG522ifl4rSRYfeZhOSyPB+tleXRKSEnWND3Tar9Zj+ZhmJI7hmlbGMY2aH+YX7FgHqajY/1aL/NTh5cSXkpPmV77wTTMx/6wfkpoGab52U6mZRiJOtNofayH4Tw0P8OYhtd0PLQu+qyLOLDccF7m48F6mU77yfS8ZjjTqI4y4xlHZw+LgEXAItDfEbCEt7/fYdu/fo8AiYseSvhIbEhkeK32XUmOSHyUJNFnPNPpeZhksVzGU1+Uh9ZDoqVlM5zki04PrYO+kiktg2lYB8NZDttJp0SW5JN10jEd4ximdZO0MS0Jr5bFephO+6XtCPvaP/pKIlkO87FMHrzWfmlZLJsH6+fiO9rD5Y5rvGYbFD+mUckuw2gfVw8tV+tjmYqNtp1t0rqYL4yXtk/byrZp+1gXy+XBeJ6zbcyjaRRHjWMerZ/91Xuk7bW+RcAiYBHojwhYwtsf76rtU94ioARGfZIgEiCSH5Ic+rxmONNoGK9JhNRpPvq7cizn8xxJltYRTst20CnZUvLL+pWQMr3m57m2g/kYruSX14zTsnJvvmLBsrWMcFt2dx7OQ4sKNKtGHWbFblflabj6rIPtJBZsO/vI9mv8rnzWz3x0PM+9Zj6N1zK0Lvoatis/Fyt7bRGwCFgE+isClvD21ztr+5WXCCi5C/th0kbiw2s9lBQxrLc8Gsb4fXG7K1/rZFtI/njNg6SQh5I0rZdt0XPG5ZYtmYJ/mlbDcvuRW7aWm+tr/rDPvCyPh5ar9TF/OCx8zjitl22ny60v95p1aB5tQ+61lqt1aRnhNmlYrq9lWt8iYBGwCPR3BCzh7e932PYv7xBQ4kNfDw3Ta/VJgHYVp2l68/c0z56mo5RSDxI6Hmzbvh67q1fj6H/eoeRbJc2aXqXQen0gfW2v1vF515rO+hYBi4BFwCLQjYAlvN1Y2DOLQL9DIJccHewO7mn9SnLZPuYJ++E2a1w47GCds25KZQ/2kYth7jXbcyhxOdh42PosAhYBi8C+IGAJ776gZvNYBPoIAr2Ro4PZdK1ffdat5/T1CJ/vLiwc11sejVe/t7o0bk/9sLqFlse8ueQ3HLenZe9Jutxyc6+1DIaHj9zrcJw9twhYBCwC+YaAJbz5dsdtfy0ChxECuyNlu4tjF3qLZ1g4XK/V35euM2+uBDpcx76Uuav272tZNp9FwCJgEbAI7B4BS3h3j4+NtQjkNQJfhCjuCXBh4rg/69KywuXvSXty04R1i8NlhQlwbp5DdR1uH9uQe32o2mXrtQhYBCwChwMClvAeDnfBtsEicBAQIAHaWxK0L3n2tSusa1cL1bQde9v+fW1LOJ9aNshtX7it4faF26jh4fL25nx3ZeWWnXsdbt/e1GnTWgQsAv+/3TokAgAAQCDWvzWOEH9zWIaBQFHA4S2uqhMBAgQIECBAgMAFHN5TCAQIECBAgAABAkUBh7e4qk4ECBAgQIAAAQIXGLErscmRA321AAAAAElFTkSuQmCC)\n", + "\n", + "The first key point on implementing a CNN is to accurately measure each dimension of an image, because it will become the basis of linear algebra operations for processing images.\n", + "Figure demonstrates how CNN works on RGB images. Each layer represents a RGB color , and numbers represent intensity (between 0 and 255).\n", + "\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "If we use the upper left pixel RGB (5, 1, 4) as seen on figure 4, our eyes will see the color lavender.\n", + "Now imagine a digital picture. For instance, a 7-megapixel camera can produce 3072 x 2304 pixel resolution of a photograph. How much calculations are require no analysis this image using a CNN ?\n", + "\n", + "The convolutional neural network will discover which of those pixels are valid signals by simplifying the image into a form that is easy to process and without loss of features, it does so to help on classification of the image more accurately. This is another important concept to keep in mind when designing a CNN architecture. The architecture is not only good at learning functions, but also scalable to massive image data sets.\n", + "\n", + "## 2. Convolutional Neural Network Layers\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "There are several main layers implemented in CNNs.\n", + "\n", + "### Conv2D layer\n", + "It is a two-dimensional convolutional layer, mainly used as a layer for extracting features from the original input image. The first layer is responsible for capturing low-level features, such as edges, colors, and gradient directions. By adding layers, the architecture will try to capture advanced features.\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "The CNN filter extends through the three channels (red, green and blue) in the image. The filter for each channel may also be different.\n", + "\n", + "The red area shows the area calculated by the current filter, which is called the receiving field. The numbers in the filter are called weights or parameters. The filter slides (or convolving) around the image, calculates element-wise multiplication and fills the input into the output, which is called the activation map (or feature map).\n", + "\n", + "We can have multiple convolutional layers to identify and capture advanced features, however this requires more computing power.\n", + "\n", + "### Pooling layer\n", + "The next layer after the convolutional layer is the pooling layer (or downsampling or subsampling layer). The activation map is fed to the downsampling layer, which will apply one patch at a time a pooling layer.\n", + "\n", + "The pooling layer gradually reduces the size of the space represented. Therefore, it reduces the number of parameters and the amount of calculations in the network.\n", + "\n", + "The convergence layer also aims to control overfitting.\n", + "\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "\n", + "There are two main types of pooling layers: maximum pooling returns the maximum value of a specific pool size (2×2), and average pooling returns the average of all values of a specific pool size, as shown in Figure .\n", + "\n", + "The maximum pool acts as a noise suppressor, and the average pool acts as a noise reduction mechanism to perform dimensionality reduction. Therefore, the performance of maximum pooling is much better than average pooling and is the most common pooling layer.\n", + "\n", + "After the maximum pooling layer above, the CNN model is capable to understand the image function. In the next part, we feed it to a fully connected neural network for classification tasks.\n", + "However, before the output of the pooling layer is provided to the fully connected layer, we need an intermediate layer to convert the data dimension used for classification tasks in the Flatten layer.\n", + "\n", + "### Flatten Layer\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "As mentioned earlier and the name suggests, this layer will flatten/convert the multidimensional array into a single long continuous linear vector. In more professional terms, it destroys the spatial structure of the data and converts a multi-dimensional tensor into a one-dimensional tensor, thus becoming a vector.\n", + "\n", + "### Dense / Fully connected Layer\n", + "Each neuron receives input from all neurons in the upper layer and is therefore tightly connected. This layer has a weight matrix 𝑊, a bias vector 𝛽 and the activation of the previous layer. An example can be seen from Figure 10.\n", + "\n", + "Dense implements the operation of:\n", + "output = activation(dot(input, kernel) + bias) \n", + "\n", + "### Dropout\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "In fact, dropout is a regularization method. During the training process, the number of outputs of some layers is randomly ignored (exit, switch). This implementation will have the effect of making each layer be treated as a different layer with a different number of nodes and connectivity to the previous layer.\n", + "\n", + "In convolutional networks, dropout is usually applied in fully connected layers rather than convolutional layers.\n", + "\n", + "## 3. Build a Model using Keras\n", + "After setting your python environment, you should be able to import the keras package:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "TeN7ETGxZl2K", + "outputId": "bd4423b0-8407-401d-f5e3-467b925560ab", + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.4.0'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow.keras\n", + "tensorflow.keras.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QzmqxXDfZl2R" + }, + "source": [ + "## 4. Preparing the Data\n", + "\n", + "Before we start, we normalise the image pixel values ​​from [0, 255] to [0, 1] to make our network easier to train (a smaller centering value usually gives better results). We will also adjust the shape of each image from (28, 28) to (28, 28, 1) because Keras requires three-dimensional dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 28, 28)\n" + ] + } + ], + "source": [ + "from tensorflow.keras.datasets import mnist\n", + "from tensorflow.keras.utils import to_categorical\n", + "\n", + "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n", + "\n", + "print (train_images.shape)\n", + "train_images = train_images.reshape((60000, 28, 28, 1))\n", + "train_images = train_images.astype('float32') / 255\n", + "\n", + "test_images = test_images.reshape((10000, 28, 28, 1))\n", + "test_images = test_images.astype('float32') / 255\n", + "\n", + "train_labels = to_categorical(train_labels)\n", + "test_labels = to_categorical(test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Building the Model\n", + "\n", + "Each Keras model is either built using Sequential classes that represent a linear layer stack, or built using customizable functional Model classes. We will use a simpler sequential model because our CNN will be a linear layer stack.\n", + "\n", + "We start by instantiating a Sequential model:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "aMwboeTFZl2R", + "outputId": "d3b7d5bf-aa8b-4d28-bd87-fbd50499a177" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d (Conv2D) (None, 24, 24, 32) 832 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 12, 12, 32) 0 \n", + "=================================================================\n", + "Total params: 832\n", + "Trainable params: 832\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from tensorflow.keras import layers\n", + "from tensorflow.keras import models\n", + "\n", + "model = models.Sequential()\n", + "model.add(layers.Conv2D(32, (5, 5), activation='relu', input_shape=(28, 28, 1)))\n", + "model.add(layers.MaxPooling2D((2, 2)))\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pX8vtUQfZl2Z" + }, + "source": [ + "The Sequential constructor takes an array of Keras Layers. We’ll use 3 types of layers for our CNN: Convolutional, Max Pooling, and Softmax." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Z8FfKHO2Zl2a", + "outputId": "f2215a6e-3e86-4bb8-f721-5d8e3d34d8c0", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d_1 (Conv2D) (None, 24, 24, 32) 832 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 8, 8, 64) 51264 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64) 0 \n", + "=================================================================\n", + "Total params: 52,096\n", + "Trainable params: 52,096\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "\n", + "model = models.Sequential()\n", + "model.add(layers.Conv2D(32, (5, 5), activation='relu', input_shape=(28, 28, 1)))\n", + "model.add(layers.MaxPooling2D((2, 2)))\n", + "model.add(layers.Conv2D(64, (5, 5), activation='relu'))\n", + "model.add(layers.MaxPooling2D((2, 2)))\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* num_filters, filter_size, and pool_size are self-explanatory variables that set the hyperparameters for our CNN.\n", + "* The first layer in any Sequential model must specify the input_shape, so we do so on Conv2D. Once this input shape is specified, Keras will automatically infer the shapes of inputs for later layers.\n", + "* The output Softmax layer has 10 nodes, one for each class." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KcbZwqatZl2d" + }, + "outputs": [], + "source": [ + "model.add(layers.Flatten())\n", + "model.add(layers.Dense(10, activation='softmax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see a model summary of what has been coded so far, one can include the next line of code and in turn is displayed a summary table with all the values coded." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ke3vcrIHZl2g", + "outputId": "2bbddc8c-e023-4357-a9be-e935e8a7309c", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d_1 (Conv2D) (None, 24, 24, 32) 832 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 8, 8, 64) 51264 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64) 0 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 10) 10250 \n", + "=================================================================\n", + "Total params: 62,346\n", + "Trainable params: 62,346\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oLThtCv4Zl2j", + "outputId": "8601dfbf-48fd-4020-f74f-f5c2d79398de" + }, + "source": [ + "## 6. Compiling and fitting the Model\n", + "\n", + "Before starting the training, we need to configure the training process. We identify 3 key factors in the compilation step:\n", + "\n", + "* The optimizer. We will keep a very good default value: an optimizer based on Adam gradient. Keras also provides many other optimizers.\n", + "* The loss function. Since we are using the Softmax output layer, we will use cross entropy loss. Keras distinguishes binary_crossentropy (2 classes) and categorical_crossentropy (> 2 classes), so we will use the latter. See all Keras losses.\n", + "* A list of metrics. Since this is a classification problem, we only need Keras to report accuracy metrics.\n", + "\n", + "Next is needed to specify some parameters before starting the fitting of the model. There are many possible parameters, but we will only provide these parameters:\n", + "* The training data (images and labels), commonly known as X and Y, respectively.\n", + "* The number of epochs (iterations over the entire dataset) to train for.\n", + "* The batach size (number of samples processed before the model is updated)\n", + "\n", + "Calling model.evaluate()evaluates the output of a given input, then calculates the indicator function specified in model.compile based on train_images and train_labels, and returns the calculated indicator value as output.\n", + "\n", + "We must pay attention to one thing: Keras expects the training target to be a 10-dimensional vector, because there are 10 nodes in our Softmax output layer.\n", + "\n", + "The compilation looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "X2OxJYvDZl2n", + "outputId": "1f347217-b935-4f5d-8af3-c5caa0399591" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "600/600 [==============================] - 49s 81ms/step - loss: 0.9142 - accuracy: 0.7685\n", + "Epoch 2/5\n", + "600/600 [==============================] - 47s 78ms/step - loss: 0.2650 - accuracy: 0.9224\n", + "Epoch 3/5\n", + "600/600 [==============================] - 47s 78ms/step - loss: 0.1885 - accuracy: 0.9444\n", + "Epoch 4/5\n", + "600/600 [==============================] - 46s 77ms/step - loss: 0.1497 - accuracy: 0.9563\n", + "Epoch 5/5\n", + "600/600 [==============================] - 48s 80ms/step - loss: 0.1258 - accuracy: 0.9628\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_size = 100\n", + "epochs = 5\n", + "\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer='sgd',\n", + " metrics=['accuracy'])\n", + "\n", + "model.fit(train_images, train_labels,\n", + " batch_size=batch_size,\n", + " epochs=epochs,\n", + " verbose=1\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pzSI4bwiZl2t" + }, + "source": [ + "## 7. Evaluating the Model\n", + "\n", + "Evaluating a model from Keras actually only involves calling the fucntionevaluate(). The model.evaluate function\n", + "\n", + "* The validation data (or test data), which is used during training to periodically measure the performance of the network based on data that has never been seen before\n", + "\n", + "The evaluation results are retrieved form the variables assigned to the function evaluate().\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1WoTYHYhZl2u", + "outputId": "33fec5fa-adb8-4c63-c1fc-afa59b68e79b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 3s 8ms/step - loss: 0.1027 - accuracy: 0.9692\n", + "Test loss: 0.10267603397369385\n", + "Test accuracy: 0.9692000150680542\n" + ] + } + ], + "source": [ + "test_loss, test_acc = model.evaluate(test_images, test_labels)\n", + "\n", + "print('Test loss:', test_loss)\n", + "print('Test accuracy:', test_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KQkQsuZhZl2x" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "Convolutional-neural-networks.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/Densely_connected_networks_ipy.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/Densely_connected_networks_ipy.ipynb new file mode 100644 index 00000000..db68d102 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/Densely_connected_networks_ipy.ipynb @@ -0,0 +1,1417 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + }, + "colab": { + "name": "Densely-connected-networks.ipy", + "provenance": [], + "collapsed_sections": [] + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "M2Fkx7fkzpge", + "colab_type": "text" + }, + "source": [ + "## 1. Densely Connected Networks\n", + "Following my previous article on Convolutional Neural Networks this time i’ll be writing about another form of image classification which is Densely Connected Networks or in short DenseNet.\n", + "\n", + "![image.png](data:image/png;base64,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)\n", + "\n", + "The most basic neural network architecture in deep learning is a dense neural network composed of dense layers (also known as fully connected layers). In this layer, all inputs and outputs are connected to all neurons in each layer. DenseNet is a short for “Densely Connected Convolutional Networks”. The latest version now supports a more efficient DenseNet-Bottleneck-Compressed network. By using DenseNet-BC-190–40 model one can get the most advanced performance on CIFAR-10 and CIFAR-100 datasets. Its architecture can be schematically depicted as seen on above image.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A1SoY6BrFp_R", + "colab_type": "text" + }, + "source": [ + "## 2. Architecture\n", + "DenseNet is an extension to Wide Residual Networks (or ResNet). DenseNet got its name because the dependency graph between variables has become very dense. The last layer of such a chain is tightly connected to all previous layers. Dense connection is shown in Figure below.\n", + "\n", + "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeUAAADCCAYAAAB37tpYAAAgAElEQVR4Ae2dCfxmU/3HR1GEvyyZsaYiS4zdWEayKzN2wlhHdgaRqGSyJo2QspYtSiVCyB5hiGw1hkEyyZbJVtSk83+9n/rezvP87nOfe+9zn3vP89zPeb1+c5+599yzfL7nns853/M93zPMKQgBISAEhIAQEAJBIDAsiFKoEEJACAgBISAEhIATKasRCAEhIASEgBAIBAGRciCCUDGEgBAQAkJACIiU1QaEgBAQAkJACASCgEg5EEGoGEJACAgBISAERMpqA0JACAgBISAEAkFApByIIFQMISAEhIAQEAIiZbUBISAEhIAQEAKBICBSDkQQKoYQEAJCQAgIAZGy2oAQEAJCQAgIgUAQECkHIggVQwgIASEgBISASFltQAgIASEgBIRAIAiIlAMRhIohBISAEBACQkCkrDYgBISAEBACQiAQBETKgQhCxRACQkAICAEhIFJWGxACQkAICAEhEAgCIuVABKFiCIFQEHj66afdzTff7M4991w3adIk96tf/cq9+uqroRRP5RACA42ASHmgxavKCYF0CPzud79zEydOdJ/4xCfcsGHDYv9WXHFFd/LJJ7t33nknXaKKJQSEQGYERMqZIdMLQmBwELjhhhvc9ttvP4SER48e7VZYYQW3+OKLu3nmmafp+SqrrOIuvfTSwQFBNRECASEgUg5IGCqKECgLgYcfftjtscceTWT76U9/2p199tnu7bffHlKMBx54wO2zzz5N8bfeemv3+OOPD4mrG0JACORHQKScHzu9KQT6DoEXX3zRHXXUUW722WdvEOwHPvABd/TRR7tp06alqksrOS+yyCLu4osvTvWuIgkBIdAZAZFyZ4wUQwgMBAKnn366+8hHPhLNdvfcc0/3yCOP5KrbLbfc4lZfffUorYMOOsi98cYbudLSS0JACPwPAZHy/7DQLyEwkAhcccUVbq211ooIFDX1L3/5y67r+vrrr7sDDzwwSneNNdZwkLWCEBAC+REQKefHTm8KgaARYCsT675mTb3yyiu7iy66qPAyk+bCCy/cyGeWWWZpWGgXnokSFAI1QUCkXBNBq5r1QeCJJ55w+++/f0TGI0aMcF//+td7upVpypQpbsstt4zyZDAwderU+oCumgqBghAQKRcEpJIRAlUjgDr5uOOOc/PNN19Ejoceeqh79tlnSyvaiSeeGOW96KKLuksuuaS0vJWREBgEBETKgyBF1aH2CJx33nluueWWiwhxxx13dPfee28luNx0001utdVWi8qCEdhbb71VSVmUqRDoNwREyv0mMZVXCHgIXHPNNW6DDTaICHC99dZzV111lRejmp+vvfaaO+CAA6JyjRo1yt16663VFEa5CoE+QkCk3EfCUlGFgCFw//33u3HjxkWkt/TSSzccf9jzUK4XXnihW2ihhRrlxAiMtW0FISAE2iMgUm6PjZ4IgeAQmD59ujv88MPde9/73gbR4QLz2GOPdTNmzAiurFYg/GpvscUW0QBim222kRGYgaOrEGhBQKTcAoj+KwRCRGDmzJnu1FNPdRhP2RYn3F5i9dwv4YQTTojKTj3kP7tfJKdylomASLlMtJWXEMiBAOTlG04x67zttttypFT9KzgtWXXVVSNyPvjgg93f//736gumEgiBQBAQKQciCBVDCLQigHesMWPGRASGx6zLL7+8NVrf/R9V+3777RfVa8011+zbQUbfga8CB4+ASDl4EamAdUOANdjPfe5zEWlxfOKkSZPcu+++O1BQfP/733fDhw9v1PM973mPjMAGSrqqTF4ERMp5kdN7QqBgBP7yl7+4Y445xs0999wNopptttncF77wBff8888XnFM4yT322GNu7Nix0QBk2223dXgkUxACdUVApFxXyaveQSHwne98xy211FIROe26667uwQcfDKqMvSwMnsjMgG2xxRaTEVgvwVbaQSMgUg5aPCrcoCNw5ZVXunXXXTcipI033thdd911g17t2PrdeOONjkMzjJwnTJjQU3/dsYXQTSFQMQIi5YoFoOzricDdd9/tdthhh4iAll9+eXfBBRfUEwyv1q+++qrbd999I1wwArv99tu9GPopBAYbAZHyYMtXtQsMgWeeecYxA7TZ4AILLODYv/vmm28GVtJqi8MAZcEFF2zghKOUU045pdoCKXchUBICIuWSgFY29UaAvbgnnXRSRDSQMgc1TJs2rd7AJNT+0UcfdZtvvnk0gMEI7Kmnnkp4Q4+EQP8jIFLufxmqBoEjwNafFVdcsYlc7rrrrsBLHU7xvva1r0XYsT3sBz/4QTiFU0mEQMEIiJQLBlTJCQFD4IYbbnCbbrppRCjrrLOO+8lPfmKPdc2AwPXXX+9WWmmlCEuWAHA9qiAEBg0BkfKgSVT1qRyBhx56yO2+++4RgXzsYx9zZ555ZuXl6vcCsI977733jnBda621ZATW70JV+YcgIFIeAoluCIF8CLzwwgvui1/8opt99tkbxDHnnHO6L33pS+7ll1/Ol6DeikXg/PPPdxjIsS6PEdg3vvGN2Hi6KQT6EQGRcj9KTWUODoHTTz/dLbHEEtEsbvz48Q5DJYXeIPDwww+7z3zmMxHe2223ncOyXUEI9DsCIuV+l6DKXykCP/rRjxx7aW2LE0TBSUgK5SDAWdKGPUZgl112WTkZKxch0CMERMo9AlbJDjYCd9xxh9tqq60iQsAT1cUXXzzYlQ60dr/4xS/cyJEjI1kccsghA3d4R6DQq1g9QECk3ANQleTgIjB16lS3//77RwTwwQ9+sLGXlnVNvHTJCUg1smfd3j9Za+2113a/+tWvqimMchUCXSAgUu4CPL1aHwRef/11d9hhh7k55pgjImRTm/pXTnaCENibrFA+Aueee66bf/75GzKaddZZZQRWvgiUY5cIiJS7BFCvDz4CnGA077zzDiHj+eabr7GevOyyy7qFF17YYW3tE/SnPvUp7UuuoHmwJW2zzTaLZLH99tu7Z599toKSKEshkB0BkXJ2zPRGTRD44Q9/6D7ykY9EnTuEO9dcc7lx48a5q666KhYFnFxAAj45b7311nIPGYtWb29+9atfjeTw4Q9/WEZgvYVbqReEgEi5ICCVzOAgcP/997s11lgj6tAh2OHDhzvOPP7rX/+aqqJ33nmn23PPPaM0PvrRj7qf/vSnqd5VpOIQuPbaa90KK6wQyeHQQw8tLnGlJAR6gIBIuQegKsn+ROC5555zW265pZtlllmiTvz973+/O/LIIx1HCuYJN910U5N7yGOOOSZPMnqnCwReeuklx75x016w5v/rX/+6ixT1qhDoHQIi5d5hq5T7BIF//vOfjRObWo24PvvZz7opU6Z0XYtXXnmlada8xRZbuCeeeKLrdJVANgTOOecchx0A5IxB3qmnnpotAcUWAiUgIFIuAWRlES4Cp512WtNxinTYHBxx++23F17oM844I3LByRrnFVdcUXgeSjAZgQcffLDpkBDW/6dPn578kp4KgRIRECmXCLayCgeB6667zi2zzDKRShMyXmqppRzGXb0MrDX7HsDwja1QPgIsI5g6G/eovZZ7+TVUjv2KgEi5XyWncudC4LHHHnOf/OQnow6ZjhmV5re+9a1c6eV56Y033nD77bdfVIbNN99c6uw8QHb5zjXXXOOWX375SA7sQ1cQAlUjIFKuWgLKvxQEWNfdcccdG6cK2QwJ5xIHH3ywe/HFF0spQ2smOLqYZ555GqSw6KKLussvv7w1iv7/XwSQEUsKrX/dAsTJXr6VPEsX99xzT7fJ6n0hkBsBkXJu6PRivyCA9fTcc88dzYggZYytHnnkkcqrcN999zmcjNhAgaMfFf6DwJNPPtnwyLXuuutG+BhOdsVxC2v13ToHOfvssx0uU0kXI7BvfvObEoMQqAQBkXIlsCvTMhA477zzHDNQ68C5rr766sGd4oT1N/tnrZx4o6qzdTbbyMaOHRvhYbgwePH/llxyyaY4aEJuvvnm3E3rgQcecJtsskmU5g477OCef/753OnpRSGQBwGRch7U9E7QCHAQwYorrhh1rnTqWDtfcsklQZf7oosuajgpobwLLbRQ7dTZOG3ZbbfdIrkxc91mm20aTls4cCIu4Bxko402it4BOwY4qKXzhi9/+ctRenh0+/GPf5w3Kb0nBDIjIFLODJleCBWBp59+ummmQwf9f//3f+7EE090zEb7IaBS33TTTSNSOOKII/qh2F2VETX1hAkTIqct+BBHjf+nP/0pdbqt5Mws+oILLkj9fmvEn//852655ZaL5CAjsFaE9P9eISBS7hWySrc0BP72t781jHXe9773RZ3oe97zHrfXXnv17R7Uo446KqrLxhtvPJDqbIzv8E9tDj0YRO27777ud7/7Xe62gxX9iBEjIuw483ry5Mm50vvzn//s9thjjyit0aNHO2wAFIRALxEQKfcSXaXdcwQ4wcnv1OnYmWn+5je/6Xnevc7gRz/6kVt88cUbpLDgggsOjDp75syZjS1o+ANHXvzhPe2uu+4qBNJp06Y1BmSWNoZbX/nKV9zf//73XOnj89ys5Bn4TZo0KVc6ekkIpEFApJwGJcUJDgG2D/mdOh3wyJEjHU5BBilMnTq14Y/bCObwww/v6+pdeOGFbpVVVonIGMMqVM+9CJzkNWrUqCivlVdeObcXNQZ5/to1g4hu1q17UV+lORgIiJQHQ461qcVvf/vbISc4cZbx9773vYHG4Nhjj43IZYMNNug7dTYEueGGG0Z14BSuSy+9tOcyw5bg+OOPbzrretddd3W///3vc+WNBzYbIGEEppO/csGolxIQECkngKNH4SDA+h57i1krtk4RgyA6SdaU6xCuvvpq9/GPf7xR//nnn78v1Nk4+9h2220jmVH+M888s3RxMZhji5O1nXnnndedcsopucrBAIP90ZbW5z//+Vzp6CUhEIeASDkOFd0LCoGDDjrIfeADH4g6QTrDcePGOayt6xZwkuGTS6jq7IcffrhpXZc1cdb/055H3Su5si3Ot6rG5eovfvGLzNmxf9nfvoUR2CDYMWQGQi8UjoBIuXBIlWBRCGBQM3z48CYyxnmE3CA6d/LJJ0e4gEkozkYYNDBQ4BxqBk8YWTGTfOaZZ4pqFl2nM2PGjMYZ2TbT5XrAAQfkstQ/66yzIm9x1LlMH+pdA6EEgkRApBykWOpdKAx/Wk9w4v+oDRX+h8ANN9zgVlhhhQb54WijSt/Zr732mjvhhBOaBlH4lEZtHGrAycxnPvOZaHCDpft3v/vdzMVlm5S/Xo5nsXbOTjInrhdqh4BIuXYiD7fCWBq3+jlG7clsRCEeAQ5q8NWoVaizkY8/iNp6663dbbfdFl/gAO+y5cm2njFrhqg5YjNr8PeWszPgZz/7WdYkFF8IOJGyGkHlCDDLYosJpzaZSnH22Wd3eFGqeg2ycnBSFuC0005rqIrBryx19g9+8IOms6HXX399d+WVV6YscVjR/vjHP7r9998/an/gyEEmb731VqaCQsRLL710lE4Vg6RMBVbk4BAQKQcnknoViI4PV5hGxlyx1mXWrJANASydOXADDDkVq1fqbAyjODTDZMb+30HZkkbdfG3NJz7xicw+06dPn+7YdmX4kN6DDz6YTZiKXVsERMq1FX21FT/nnHOGnOC05pprOtb5FPIjgGZh7733jggB1WxR4e67726cSW1ks8QSSzS8W/3jH/8oKotg0mG7FNumrK5ocrIe9cnWr7nmmquRBpqfKraCBQOoCpIaAZFyaqgUsQgE7rjjjobnLevsuH7sYx/L7WmpiDINYhoYLOFUZeLEiV1XD0cbvmoXo7JjjjnGvfTSS12nHXIC+OD2Z7zsi8cRSZaA323U+tbeMQL7y1/+kiUJxa0ZAiLlmgm8quqyZsfBCtY5ccVndV4HDlXVo0754rDl6KOPjrb8IDP2jIey/aosWeCDfKWVVoraLhqdLDsB3n333capV9b2GYRyCpWCEIhDQKQch4ruFYYAqk22xti+VTomDLr2228/bRspDOViE+LgBgZLiy22WEREu+yyS+7TlootXTWpYfDFoRa+MeLnPvc599xzz6UuEC45zSMb38EXvvCF1O8qYn0QECnXR9al1xTVaesJTmw3eeyxx0ovizJMh8B5550X7X2GOMaMGeNuvPHGdC/XINa9997rOA7SZr0cE3n66aenrjkkjjc6ex+PYng/UxAChoBI2ZDQtTAELrvsMoezfut4uHIy0M0331xYHkqoWAR+/OMfN1kdr7POOg61rUI8Aueff75bcsklozbOCVJYv6cNZ5xxRnRIBkZg2oufFrnBjydSHnwZl1ZDtn3YlhwjZJwysJ9VIUwEbrrppsZBHyYvtgBhGa/QGQGObjzkkEMiYgbDQw891L355pudX3au4S6WPeWG/U477aR9+amQG+xIIuXBlm8ptXvllVeGnODEPlmOG/zXv/5VShmUSTYEODzB9wS2yCKLNPxpZ3WWkS3XwYyNBsh3s7nUUku5Cy64IFVl+T5YWzZixgisV+dLpyqQIlWOgEi5chH0dwEOPPDAphOcOFpx9913d5yioxAeAtOmTXMTJkyIjsDk9K0vfvGLuQ5jCK921ZaIwyj8A1RwN5p2vfgnP/mJg8yNnJGJQj0RECnXU+5d1/qb3/xmUwdEZ8JsQZ6Luoa2JwmgzUBz4Rve7bvvvjK6KxjtJ598sunISk7JYk93msAJWzvvvHNEzOutt5579NFH07yqOAOEgEh5gIRZRlXYX+kfPgAZsw55/fXXl5G98siIwMyZMxvHCXJAgs3COI/5rrvuypiSomdBAB/Yo0aNijDHFSlbotIEZtxzzDFH412ueU6uSpOP4oSJgEg5TLkEV6qnnnqqyTqXDn6hhRaSUVBwkvpfgS688EK36qqrRsSwySabaL3yf/D0/Nc///lPd9xxxzUt77COjyOdTgGXpsyUbSDFNqrXX3+902t6PgAIiJQHQIi9rAKGP60nOLEOiXEKTiYUwkMAb1O+4dEaa6yR+VCF8GrVvyXiTGm0E0awLCF84xvf6FghSP2II46I3mMLFgdmKAw2AiLlwZZvV7WDeFtPcBo9enTjSLs99tijcVIQPpEnTZrUUF+//fbbXeWnl7tDAL/inLBlnT+GQ0UcgsAe5l/+8pfdFU5vu4svvtgtt9xykXxwHHLrrbd2RAb8sco2uX7pS1/q+I4i9C8CIuX+lV3PSn722WcPOcFpwQUXdDg5sI4h7soMAKtRWV73TDSxCWPhi8tHk8mHPvShhtp0xowZsfGz3uQ0KNLGzaRCdwi8+uqrTVugwPWAAw5wb7zxRmLCf/jDHxz7mE3GqLY5MENh8BAQKQ+eTHPXCI9EI0eOjD58OgDfZzV7jzmQAO9DbOG44YYb3Kmnntrwbe379OU9fCW/+OKLucuiFzsjgLUu6k2TEZa+hx12mHvmmWc6v5whhm8RLDepGYBLiMoRpZ/+9Kejb+3DH/6ww8Vpp3DaaadFg2OWkc4999xOr+h5nyEgUu4zgfWiuJwGxAlOs8wyS9RJ0MHbqBz/vnQGnTwV4R1q7Nix0XvzzDOP+/a3v92LItc6TQx+TjjhBIdcTEYc+sHaZa8CFsGWFwZ+qGIVukeA8679gz8233xz99BDDyUm/Otf/7rJ6JLjJTt9m4kJ6mFQCIiUgxJHuYX597//7VgbtpkWnS7OP4ycWU+m889q0MX5u2uvvXbUiTP71v7lYmSLlsLfkoaDijTrkkXkjkXwCiusEMkVl5IK3SOAxsM/r5rvsJPzEE5fO/zwwyNZYD+gg0O6l0UIKYiUQ5BCBWVodSRBR2Bk/L73va/h07fbw9jZksP6ppE9zir++te/VlDb/s+SQz44xxcs+cNn8pVXXll6xSCDvfbaKyoHxkr33HNP6eUYxAyvu+66phkw+/9ZJkoKHBri70HXun8SWv3xTKTcH3IqrJQcDtF6gtN73/veRicLKWNMglFJUQEShoyZgUMmkDRkrZAOAbbA+GuPK620kvve976X7uUexmIt0wZxc801l0MNq/AfBDDAeuSRR3LD8fWvf9198IMfjAY+bElkNt0uYENAHH/A9vjjj7eLrvuBIyBSDlxARRXvgQceGHKCk5ExHzPWnJ3WsropC+pr34gM9TZqboV4BFAV+9a2WEDj2pSZarvwpz/9yTGjLivQXnyvVcyg6xbYBsi+cJyC+L6rjSC5xxLRNddckwkaiB1jSUuHgQ9knRTYmoiWi3cwAuN4SYX+Q0Ck3H8yy1RiZqoYX/kEbLNWPt4VV1yxtLWod999t7FvFgMw8p511lkb1sN/+9vfMtVpkCNPmTKlaX2RGRMqyZdeeqljtY3E2dN60UUXdYxfVISDDz44Ig/cSZa1xl1U+fOk89xzzzV2HUB+RpxcF1544cbZ4Rxhym//2Zxzzun23ntvx+ApbUA9jXbE0mEJ45Zbbmn7+p133unwJWDxORwmq01I28T1oBQERMqlwFxNJhiP+J2GqRv5YNmCcckllziMvcoOL7/8cpPj/UUXXTS1X+Cyy1pWfljAH3300Y5tZ9ahsv1s6tSpqYvALNne5co2tcsvvzz1+91EvPTSSx2kY/l3mtV1k1eV77LP+POf/3yTcSTLQfinfuedd4YUDa9crAuzBGEDYwwrGcikGWiRIJbVX/7ylxuDWMOXfent3G5SDrbGWVzaATsjFPoDAZFyf8gpUynZO4yzD/so/SsOPlBzJalBM2XWRWT2avqeijbYYINC17O7KFpprzKLweWivy0GteXkyZNzlYFZGLMxf0sbXqTwCtXrARiz/PXXXz9qd7iWHJSANmfixIlNAw9klmVrGP7j/TOsccaDBTUneKUJ9957r9tyyy0jfNmalrRP+Yc//KEzxy/0AV/96lfTZKM4FSMgUq5YAEVmf/XVV7ull146+mh9MqYDYJtFu9F1keXIkhaDgxNPPDE6FYdyfu1rXwti0JClHnni4izCX2dnj2pR21o49IC9yywRWDsgL04v6jU5084sT/w197OLTma6uCr1B7mopfF6x7M8Ae0HAxbTXLFejOvMtDsTWCv2B7P4GGi35ZCBgO93G5/oTzzxRJ5i652SEBAplwR0L7PhI/PXkegQ7YNn/ZjOOXTXl6zRbbrpplFnTqczqGuThxxySNOywjrrrOOY1fQiTJs2rWl2Rttg3ZcBXC8DxxTadjjyRP3aTwH7B5Z3/JnmAgss0DC2i1NT56kbho5bbbVV1OaxtWA2nmbgzHIH7cgGP1zxVd8uoD0z7QnLDCFY8Lcra93vi5T7uAUwy9x+++2jj40P08iY38y8UCn2U+C8Zta7rbOhfi+88EI/VaFtWbGe9re6zDvvvI0ZV9sXCnzA7Mx3lwm+q622Wk+PcmSWRhs0WbKu2g+BNrj88stH5YYsOYKxV16z8MSG+1LDiSWmk046yaUxgGSt2D8RDGtvliriAstFvlOf8ePHx66Dx72re+UhIFIuD+tCc8LncesJTvZR09niiq9fA9tMMHqykT3GT7j5/Ne//tWXVcKCdpFFFok6XbQXrA2+9tprpdeH2dl2223XNHhba6213M0339yzsrAcYW1z+PDhwR4/yJot346VFSNJVPFp1crdAvib3/zGbbTRRlH+aBoYyKWxnub7AFsr+zbbbBPrA5208MRm8TACS7Lm7rZOej87AiLl7JhV+gZWnn4Hbx8XV0bJnTwAVVr4jJkzu8NjlNURg6X77rsvYyrVRWd7yrLLLhuVHy0GywwhrOk9+uijTapTMKZsd911V08AY5+urwGZMGFCT/LJkygDFd9BC3t9sXxPax2dJ8+kd5CBvxyFQRc+5DsZZz755JOO2a99L9SDGXdcwFLflwcDJ4UwEBAphyGHjqVgNOv7HbYPjysjZDwqzZw5s2M6/RgBL2Q2C4DY6Hi6dQHaSxxQ2zL79JcSUIf2ivC6qQuq0zFjxkQdOe0JC2pmjUWH6dOnN5ZbrO1CPFW2WTxhsbfb5MSWJewvsuwjLhojPz1U075zFqy9sbbuZGCGMd8aa6wRyXSVVVaJ1YRgb4DWxOSBwRj3FKpFQKRcLf4dc+f4Q1Ra1nHYB8QVq018WL/11lsd0+n3CBi/MHsxxyesu2G93GtL4iy4IQcIzsqIjBZffPG2a3xZ0u51XFSnvqEdZd9kk00c94sOp5xySkQEGB2xhltmYAbMHn5bHuHbwk1lqISET2yI1b599kXjqjZpOYdZNevgvp8CPK7FLZmwJc+s9OlTynQ8U6bc+yUvkXLAksIbj3+Ck32UfEAcjI4TjroFfAr7HRQzgm78DBeBH5a6zN6tk0dOGHGdccYZRSRfahrMkH3DIeqCEVLRx0KyTcpX7e+33349rycDO2wVfKJiEIUqvx8CM2BfW8Z6MGropIEpW6UwlrS+Y/7553fnnHPOkOpyljraHYuHc5JOM/IhiehGIQiIlAuBsdhE2OTPTNA+EP+Kugn1aJ0DnRD7RCE+sEHtyPaQXlnHJmHN/lLfkxUdftLWlKS0QnrWuq4JzltssUWhBMaMlYGntW/cSWLkV3RgCxOzc2sv5IfqvBdagKLL3poebZ/tc74/Ak6TYgtaEjnj5MQfBHHKGP7w/YC1N2v9Jg/iQ9YK5SIgUi4X78Tc+HBaT3CyD4S9rP3YiSRWuMuHrCv7nToGMb3a79taVAYF/sCJWTLempg1D1Jgrzhtz9ohql4se4s8TORb3/pWlD75FHUkJevVrMH6PqhZox0Ea2NU1/QXvhMRBjVJB1/wvTBgNFlyZRdHa8CGg2UXi3f88ce3RtH/e4iASLmH4KZN+v7772/aimEfA1dGwddee23apGoZjxOVsMw23LDY7pU2oXUfNevHWO5WZalblsDxNOYbHdk6bBbf3EllZUa26qqrRjJEfdpNID3f8QezvkHameBjgzraH3iAY1Kfcccdd/4K0isAABrLSURBVDRZm2OFfcUVV/hJNnYIbLvttpE8Nttss9gtVk0v6T+FICBSLgTGfImgbmVNyxzVG6lw5ZAGvO4M2swrH1Kd32LmgMGKqZLZDsLpSkWpQ1uPnkRGkFS/rEd2RjBdDIyOfPJkUDJu3LhCjKTYD4zfbvsOmAXmXZIwpyVsE+RQjiTVbrqahx0Lwy52YPjkzIlSSRb/Z511VpPP9a233to9/fTTTRXlYBEzXMRfAF7OFHqLgEi5t/i2TR3DFt/gxDoivAfxIRRFJm0LMKAPcCeKetXwZBtJN/6k2R7DWdO+9Tvreb10ttEPosFNJ8d+Gs4MLFlKYJtRtwFy8Q0cW2dxadK/5557anueMMsBGHSZbDDca7fF7dlnn3X0RRaXK77o/XDbbbc5CN7i7Lvvvpos+AAV/FukXDCgnZKDcH3n9tbQmdlxJNyMGTM6JaHnKRCAiH31JT6Gs/j/ZuaB+s7XYjALyXIqUIpi9nUUZp8YGPkuKdkZgOqZAzG6CSxJ+GvZe+yxRzfJ1e5djLboa3xyRgXdzooeDYjvsAQrb/+4RzQW/rnZLKvhtlOheAREysVjGpsi2xl8i0kjY2ZgHNXHgQwKxSKA1S2W7DbrQrVNR5XksAKiYbuZvYOc0F6cfPLJxRZugFIDMwzslllmmWg2heEbM7BuHHHgEpK96fatMJhtdXnJ///whz8MEJrFVgUyZb+y73M9yYqe78OPy1Y/f6KA+pqlNZNJ66y62NLXM7UGKXNwN4YRdfxDzdXL8PjjjzeN+K0xc8U5w2OPPdYxe/ZXYpxRR/kkrYl1BO6/ETD64qxmw57BETOx1oCVKetmFo9jJJkddArMSnCpWUf5MFuygxOwf7j00ksb7l4NQwY3ECunGuUNF1xwQdN2JvbmEsAbAmF7T1yos1zoL0wuYMPgBRsLa99MBtheGWeoR5/ERMFkiEMR3Pta4B1/iYh97KjB/fDQQw8NGUDZc5bm6vq9IJdOzp4apIwqwgRQxytO34sOdFA0et+hhGHL1gWEkzbg/s7ereMVtX4RgbVJ3xBm1113bThgQSXtLymgssbDU9p1fdKpo1yszhh6+QGjOzxOffSjH41wYYDDQQh5rdTZU+sPrCANZshoMSgH+bUGf7uclbVO1x133LEVEscEjG1RZs+SZKiH9sO3G2BXg78tE+2R2VpwOA5bqQjIgsFSuyUHljfqJIfWurIslhQapMxIqPXFOv2/aG9CkIiNSH0c2XqQxxIUC1I/nbr95kSlogLqvMMOOyxaKzbLUjClg6Hjz7oeuu6669ZaPlihxwWWCc4///ymgw/Mucorr7wS90riPcieb8va/4gRI6J96pBAq2qbGbTFreOVE6/aBQZHONxhsAQ2DETx+93a9vleOAvbt604/PDDo2TZx+772cZ9KRoMw/uqq66K4toP/yQsi1enK5OypNBEyq3m8EkvDsIzO1KuKFJmi0HcCU54Ejr99NNzu60zUu7n4xjztBd8W/OxFknKlAP1XKt2CLLIu93DSBmjpzoFHFUgn3akbFjgrhH1p78WyUQAl5fM3LIG5GS+mv31T4z5/GCknMd620+n335ff/31DbkkkbLViWUFiBRDU2SJZg/r6lZbAJb5+A6NPOnnOJKU8MYbbzSt/UM6pqWIGywZKdsyhJVl0K+4lgU/kXKCpIsiZawUfZ+01nBZT8MNI422myBSLmamjEcjlgJM5YacOLPWtBrMmjHyYg3fD8zAGP23CyLl+JlyK15YtJ955pkOz2v2jaD2POaYY4bMclvftf+zVolalEGuf/SgpefPzETK7WfKhqddmSFzYIUNdui7sKdoXW5goOx7EYOozVHPpEmTovfNXwByaR0siZQ1U7Z2N+TaLSkzymQPoN/J0wjp3Fk34YSnIoJIuTtSRo268847Rx0GMmINmQ6GAOkiL5Mjz2zWzLolo33+IIS4IFJOR8qGHVbxp512WtM6PvjyPaIuTQqcimYEHHf1Z2Yi5fSkbJizz5z1elvWmWOOORquOP3lBvo930c2coDQsRlgt4O968vHHyyJlEXK1t6GXLshZYx7TOXjN76xY8fGWjQOyTzDDZFyflJm/ZiOxWSE2nTixImx6E+ePLlJ48G+TYgY5yG8j9qpdd2ShETK2UjZwMc6mIMiIFKTD/7ETzrppCbLYYvPFY0Fa6Fxs2RLg+cEkXJ2Ujas2TWyww47RHLhGzrqqKOa2j8aQt/4zvCPu/qDJZGySNna2ZBrHlJmW4HfiVgDXHnllV2v1nxFytlJGVWaLycGUPvss8+QNhB3A49Ipn4zq2Gz8o2zKBUp5yNlDrXw1yl9Y6IFFlig4TaVvcrtAloMZOWnYd8j5C1Szk/KhjluZH0f2Cz1oK3wl3jQeti3Ztomk4N/NTW2SFmkbO1ryDULKWPmHzc6515Rp9oMKeB/b4iU05Myxie+sR2qNDqDuBluO7y5/8ILL7iRI0dGMwW/c4EI/CBSzkbK06ZNczvttFO0XMAs7Mgjj2xY/vJN2gAIzIcPH95QdaPy9gPybB0goSK1WTTe3MxDlQy9fOTy/cYTGPuR7Tto1Wg88cQTDo9h9rzdFRmJlEXKbVthGlJGpbnKKqsMaWxYVHM6C9s0eh1Eyp1JGScj/nmxdAqQJQSQJ0C8GBPR8fskYZ2Nb/glUk5Hylj0+sZEzIw5gKLV/SmEi/GXGeCBOcZh3/72tx3GYgRbUmglZpM1s2jz1yxSNlS6v7JPGTsa+w4wlEQrdeCBBzaWhdimZs/8K4MwNBp8S6bBkPV1vDy0JWrYsIY7wFZ48LrCSTOtRgtYJeL5KUmt1ppWt/8XKbcnZUjXP1KQjoBjHPEY1E2I04r4nYy/RiZSTiZlDB5bDz3gRCJmV0mB7VLsifVtAtCCnHHGGY21fpNHO2K2zl+knIRyvmd8X6aJQA5xTpJMPlz5XsxQ0nyai5TjsRcpx5Ay+/RsU701LMiZ0SDbasoOIuWhpBw3aIJIi+qAWTdjNpZEzrbfUKQcT8rMePEe5X9LGAaZV6gRI5Z0k+97oOPnhOVvazpYyBvp8o2acZefmD0vqk34aYf8O8s+5W7rwcEv/lGevl2A9Z12NUNJqa+lvm7b7lrV17iNY63EGpFdsahGHVZVECk3kzKeh/yROSfhoGruVYBcUFdD1KjgfNeDkIFIuZmU7RAEX/2MISTOE/wAKW+w4dhUxMx77JnFmt4neftGuba62hQpd2/o5csr6fe1114bGUf6MuE3Km1bAsK+Q6QsUm7bloyUORjC99NrjYq1ZFO5tE2khAci5f+QMo5YzCoaGeGFCwOhqgJEzdrzWmut1RjI1d2jF8ZYp556qsNy2r4h2i4zVU6Sag2QMiELMTMwuuiii5pmyZYXV5+YRcrlkTJyYashpOvLw34jFyY2GHpJfS1Sbu0Lov8bKVvDsetiiy3mUAGFEupOysxMfQ0Gs+TddtstmIPW6z5Txvfxd77znabDPjDMwhAy6ZhMI2W+s7TEbN9o0tWM8ETK5ZFykjzsmQ2YNFMWKbfl1lZLQVQsjMJDC3UlZVTS9kFzZQ/kpptumstfci9lWndSxvjR5IRBD2fypjGE9EkZ+XQiZmZaLB90Wus3oyKRcjmkjFyYKePvupNstCVKvq8T+2LbMoFxQsiHddeVlH1VGBbWODIIMdSVlLHBMDLmijHWjBkzUouolZR5sRMxtyZu6/225m/r/pC3WQfL0KsVtXL+HycbiNsGS7K+jpeDrK+HDWscWRYPTxh360rKdkrU2muvHYYg2pSirqTMebuQMZbQrXuN20DVdDuOlImQlZibEvX+Y52/SNkDJYCfUl9Lfd22GdqaclFHN7bNqMsHdSdlZj0hh7qSctqjG9vJrh0pE78IYhYpl6O+biffdvdFyiLldm2jcSoNI32RcluIKn1gM2WRcqViaJt5L0n5nX/M7JqYRcoi5baNt4IHOk85BeiaKacAqcIoIuUKwU+RdS9Jmexfe+3NrohZpCxSTtGMS4siUk4BtUg5BUgVRhEpVwh+iqx7TcoU4cUXX85NzCJlkXKKZlxaFJFyCqhFyilAqjCKSLlC8FNkXQYpU4w//nF6g5hTFKkpikhZpNzUICr+j0g5hQDqTMrsF2RPNtsWOoUsLkazxO2Ub51JGfmwTztk+RRNym+99XbUJDD0av2LHqb80QtS7ge5lOn7OqUomqL1wtCrH+QiUm5qBvH/qSMp08mzwR/3oVzZN5gUcCNpnniS4tkz0sUfdBoysXfaXetIyuCG43723YJjJyM3ZEOHlDaQflHyKZKUIeQ55xzhrr3ulkZVNtxoC3fNtTemrVZsvCJJ2ZeL7YWOzfS/N6uUS91IGY9/tk+90/fCt1LV9yJSTvpi/vusjqQMCdOpEGicSb696byzELJBTpqdPg6Lm3StIymbZyRwoaNJ6kAYMPGXNUAwyIdrN6EoUjZC5sSo5Zdfs1EkVNb2O28ZiyRlXy6075DlUidSziIX+jLr+7K0qaK+F5FyCtTrRsqoltkClkbFTKfDTDpvoPHn+QD8/OpGynz8uIiEjDsF4jBDyBvooNqdQ5w2zSJI2Sdk8t18zGfdnXfe1ygCv7uZLRdFylnkAmFz3GfeUIRc6kLK/SYXkXKKr6JupMysKm2HQTx/NsAHcMcddzT9cY9OyO63kn3aAUA7UdWNlOmQwSxNgJCJbyFOPsgjST7ImOd5Q7ekzJoxKmv/TGV+L7PMfwyUpkyZ6kaOzO/NrShS7je51IWUq5BLmgFzu+9JpNwOGe9+XUiZxksHxSyMP34nzWIh41ZyoNNH9c19/kz9ScfPQR4QPnH8wEw7KR8/btzvupCyyWeJJZaI5MPSQbtAx4AMWgdBvGPygbSRB3/8Rnat8bnHX97QLSm3y5cZMoRMWHXV/NqabkmZ74A00sqFAU4Ichl0UvblAt7IKOl7aScX+qYyv5dgSZlOwmZWdqWz4M//f7sPtsj7VZIyhIV1bWtHGVe/otxs0gDTkCQddZzqGtkxuyIdyJ3/84G0+yDIiw4tb6iSlBl0cD5sGvkU5WaTwU07LH0MrTPx79lvCNjkQ9kh8HbESzrIMW/oFSk///wLjdnynuMPcr/9bfMhJFnk0i0pGy5VyAUZ5g1VkHIWuRRlfV2EXOjnwJo/vhcIPOl76UYuQZNy64geMPijQ0G4/C4jVEXKNtOxxsBpSElHRhZBypYn106BhhpHyrxn6VB2Oj3k1S4kkUe7d/z7VZEy7c9kY/VM2j5WBClbngxyOgUw51uJCzYroNxYcSNHBk9xweTT7nncO/69XpGyn4f/2zAy2dD+kuRSBClbnmnkQmcOUcSFLHJB60QdyTtPKJuUDaO0cimClC1PtEydAnKhbHEhi1xMXZ5XLsGSsgFjI3prfFS0HRHYO0VfqyJlOkEE7GMADsxaDj300CHrfEWQsn3oaTBMImXe9wdVSR+Fdfp5G3FVpEwdqRc4WEdj8sE4qnVgUwQp0+nbt9BJRp3kY7iTHr/bBRtgtdanXfzW+2WTMvkjFwYlaeRSBCn3o1zKJuWscimClPtRLsGTcpYRSmtnUNT/d95558bHPdtss7m555671D+Ou6PTQL3rdzD2m1mOOY8ogpTpyNIOejp1+jZKpaymxo6TiZFD3LM094488sgGNrPOOmupsqEtzDfffInyQW6m3i6ClBnopDXCQ5a0j6Rg7Yhru0ERBMdzvsU84ayzzmq8z3nkZX0/8847b6Mdt/tufLkUQcq04Sxy6bQc4MulHe7dyuWcc84JWi5FkHIWuTBTLkIuNhDIO4gNnpTpBKzTpqGmWUvL03EkvbPZZps1Gq//oYT0m5k0M9wiSJmOJS3GNOIkAue5LzvU73HB4sQ9S3Nv/PjxQcsHTKljEaQM3uCaJnTClXRMM0J7hpziQqd04t7x7zEoCel7sbKYXEaPHt0oXzfnKSMXBkFpQic8+f7KkMuJJ54YtFxssHTZZZelgTU2Ti/l0m7A20m+sQX1bvYFKVNe1mDsY2o3cvTqVehPU1+PHTvW3XrrraX+MctAFWp1t6sZL/hYdEvKqMtJn5Fea2BU3koGNlJvjcv/6VT4I/hqxLi0STdthxaXl6mv11lnnVJlQ1ugjsiHEbbJhivyoV7+aLkIUiZtw9WwAFM6MDoJZGLBRuxxa8HEo/MgWCcSlzbPIYl2a9OWV9LV1NfLLrtsafI5//zzG/inkYt1/t2Qchx24E8bADtfLrYcEKeZKFMupr5eeumlS5ML3yqYpJFLETNlvsPW78XaKn2n3++EIpe+IGVA9TuOdiMUA7voq5Fy2ecpWyPhg7c/GlEcsVHnbknZ8mvtxGm8dMxxs2IaPe9Z4F3U6ZTXBgzIz8rPx9jaGTFj8dOwtNJejZT9Dyztu93Eo36tnQsY0bG2Ykg+3ZIy+YGjjx+/rdMhXx938gRbv7348jGjQXuPd6lPqyxaSSUrZkbKo0aNyvpqrvhZ5dItKZtcuFpALoa7PbdnXOPkgjzAvyy5GCmvtlo5B1JklUu3pGy4+3LxZUB/0dqnIRe+Bwt8L61ysX6y3fdifYClkfUaPCkDqHW2PjFbR5S1wnniV0XKNAgET0Ohvn5nHFePbkkZfFsbqeVDQ4x7xjuMfC1QZuLyZx+D/d+ufj24180sjHyrImXyZlCCfMDBr5fh4V+7JWU6C/LyA/j5ARn593jHl5svH4vH1f8zuZEu97uVT9mkTLnBKa1cuiXlOLn4MgFzXwY8g7D9e1nlgoyQC+/lDWWTMuXMIpduSTlJLvSnrTJIKxewb/e9FCGXoEmZijNyBFwCDRCSshGK33k0IvTon6pImep06uj9KndLynQShrWfLr+Rhd+J+M+5z/OsAXl227GQZ5WknEU+3ZIygx/IPynE4cl7eeRDPgyIu/3OqiDlLHLplpQ7yYXncRhy32bTSTKNe1aEXKog5Sxy6ZaU28mFb8H+4vq0quUSNCnTAdkfDRPCsP9zbUcgcY24m3tVknKWcuchZdTSdBh8LK2zMD9vGnFcAyYO5Jq1k+Cddp2Vn2+a31WScpryWZw8pEw7B3vwQj5c2wXkSPzWwDsm59ZnSf/P805celWQclw52t3LQ8pp5UIfRTtHo9JKSFXLpQpSbieDuPt5SNnkQnpx3wuYm5a1XZ9WtVyCJuU4QVVxb5BJmZkV1rGdOuB2DdiXR5ZBkhGN/37e34NMygyEMCTrJB+/s2mHY5ZZWZHyGURSZhCaRi4mC4jZyMDu2TWLXBh4IesiwiCScie5QNpXX311wyskti/YJ+EhMi7wDaQNRcpFpJwC9UEmZapPg+oU0pBypzR69XyQSRnMOsmHTtqfIRfVaRclr0Ek5TRy8fFDPlkGrf67vfo9iKQMVq0aCR8/ZIAs+GOgxGza/3b8uFX9FimnQH7QSTkFBI0OpZ36Os37vYwz6KTcCTtm0Tb658r/QwqDSspJGDMw8mdayCS0wdKgknKSXPxnoU40RMq+lNr8rjspM/K0EWanWVsbCHt6u86kjDxs5G9Xnwx6CnzKxOtIynwzqFIhY9TToREyoqs7KVu/lrIZlxZNpJwC6rqTcgqIKo1SZ1KuFPiUmdeRlFNCU2m0upNypeAnZC5STgDHHomUDYkwryLlMOVipRIpGxJhXUXKYcnDSiNSNiQSriLlBHACeCRSDkAICUUQKSeAU+EjkXKF4CdkLVJOAMceiZQNiTCvIuUw5WKlEikbEmFdRcphycNKI1I2JBKuIuUEcAJ4JFIOQAgJRRApJ4BT4SORcoXgJ2QtUk4Axx6JlA2JMK8i5TDlYqUSKRsSYV1FymHJw0ojUjYkEq4i5QRwAngkUg5ACAlFECkngFPhI5FyheAnZC1STgDHHomUDYkwryLlMOVipRIpGxJhXUXKYcnDSiNSNiQSriLlBHACeCRSDkAICUUQKSeAU+EjkXKF4CdkLVJOAMceiZQNiTCvIuUw5WKlEikbEmFdRcphycNKI1I2JBKuIuUEcAJ4JFIOQAgJRRApJ4BT4SORcoXgJ2QtUk4Axx6JlA2JMK8i5TDlYqUSKRsSYV1FymHJw0ojUjYkEq4i5QRwAngkUg5ACAlFECkngFPhI5FyheAnZC1STgDHHomUDYkwryLlMOVipRIpGxJhXUXKYcnDSiNSNiQSriLlBHACeCRSDkAICUUQKSeAU+EjkXKF4CdkLVJOAMceiZQNiTCvIuUw5WKlEikbEmFdRcphycNKI1I2JBKuIuUEcAJ4JFIOQAgJRRApJ4BT4SORcoXgJ2QtUk4Axx6JlA2JMK8i5TDlYqUSKRsSYV1FymHJw0ojUjYkEq4i5QRwAngkUg5ACAlFECkngFPhI5FyheAnZC1STgDHHomUDYkwryLlMOVipRIpGxJhXUXKYcnDSpOJlOeee243bNgwN2HCBHfsscfW5u9Tn/pUo97777+/4RbkdamllmqUc/z48bWRDe1w7NixjXpvueWWQcrFCrXuuus2yrnDDjvUSj477bRTo96jRo0yKIK6rr/++o3ybbfddrWSy7hx4xr1Xn311YOShxVm4403bpRvm222qZVcdtlll0a9V155ZYMi9jqMuyNGjGhEhpjr+HfEEUfEghPKzZVWWqmWcrG2SCcTcthss81qLZ8NN9wwSPGMGTOm1nJh0hFi2GqrrWotl9GjRyeKpUHKt912W61GLL424KSTTnLPP/98IkhVP5w8eXJt5XP88ce7p556qmoRJOb/6KOPOpZC/HZVl9/Um/qHGH7/+9+74447rrZyeeSRR0IUi5syZUpt5TJx4kT30EMPJcqlQcqJMfRQCAgBISAEhIAQKAUBkXIpMCsTISAEhIAQEAKdERApd8ZIMYSAEBACQkAIlIKASLkUmJWJEBACQkAICIHOCIiUO2OkGEJACAgBISAESkFApFwKzMpECAgBISAEhEBnBETKnTFSDCEgBISAEBACpSAgUi4FZmUiBISAEBACQqAzAiLlzhgphhAQAkJACAiBUhAQKZcCszIRAkJACAgBIdAZAZFyZ4wUQwgIASEgBIRAKQiIlEuBWZkIASEgBISAEOiMgEi5M0aKIQSEgBAQAkKgFAREyqXArEyEgBAQAkJACHRGQKTcGSPFEAJCQAgIASFQCgL/DyCGEDOwibrxAAAAAElFTkSuQmCC)\n", + "\n", + "The main components of DenseNet are “dense blocks” and “transition layers”. The former defines the connection mode of inputs and outputs, while the latter controls the number of channels so as not to be too large.\n", + "\n", + "It has some improvements, such as:\n", + "1. Dense connectivity: Connect any layer to any other layer.\n", + "2. The growth rate parameter indicates the speed at which the number of features increases as the network becomes deeper.\n", + "3. Continuous function: BatchNorm-Relu-Conv, from the Wide ResNet paper, from the improvement of the ResNet paper.\n", + "\n", + "Bottlenecks-Compressed DenseNets provide more performance advantages, such as reducing the number of parameters with similar or better performance.\n", + "1. Consider the DenseNet-100–12 model (with nearly 7 million parameters), while the DenseNet-BC-100–12 model has only 800,000 parameters. Compared with the 4.10%\n", + "2. error of the original model, the BC model achieves an error of 4.51% The best original model DenseNet-100–24 (27.2 million parameters) achieves an error of 3.74%, while DenseNet-BC-190–40 (25.6 million parameters) achieves The 3.46% error is the latest technology level of CIFAR-10." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mChxP3gBGCSq", + "colab_type": "text" + }, + "source": [ + "## 3. Dense Blocks\n", + "\n", + "DenseNet uses ResNet’s modified “batch normalization, activation and convolution”. One can implement this convolution block structure as follows\n", + "\n", + "```\n", + "from d2l import tensorflow as d2l\n", + "import tensorflow as tf\n", + "\n", + "class ConvBlock(tf.keras.layers.Layer):\n", + " def __init__(self, num_channels):\n", + " super(ConvBlock, self).__init__()\n", + " self.bn = tf.keras.layers.BatchNormalization()\n", + " self.relu = tf.keras.layers.ReLU()\n", + " self.conv = tf.keras.layers.Conv2D(\n", + " filters=num_channels, kernel_size=(3, 3), padding='same')\n", + "\n", + " self.listLayers = [self.bn, self.relu, self.conv]\n", + "\n", + " def call(self, x):\n", + " y = x\n", + " for layer in self.listLayers.layers:\n", + " y = layer(y)\n", + " y = tf.keras.layers.concatenate([x,y], axis=-1)\n", + " return y\n", + "```\n", + "The dense block is composed of multiple convolutional blocks, and each convolutional block uses the same number of output channels. However, in forward propagation, we concatenate the input and output of each convolution block on the channel dimension:\n", + "\n", + "```\n", + "class DenseBlock(tf.keras.layers.Layer):\n", + " def __init__(self, num_convs, num_channels):\n", + " super(DenseBlock, self).__init__()\n", + " self.listLayers = []\n", + " for _ in range(num_convs):\n", + " self.listLayers.append(ConvBlock(num_channels))\n", + "\n", + " def call(self, x):\n", + " for layer in self.listLayers.layers:\n", + " x = layer(x)\n", + " return x\n", + "```\n", + "In the example below, we define a DenseBlock instance, which has 2 convolutional blocks, and each convolutional block has 10 output channels. When using an input with 3 channels, we will get an output of 3 + 2×10 = 23 channels. The growth rate can be defined as the number of channels in the convolution block, controls the growth in the number of output channels relative to the number of input channels.\n", + "\n", + "## 4. Transition Layers\n", + "Since each dense block increases the number of channels, adding too many channels will make the model too complex. The transition layer is used to control the complexity of the model. It reduces the number of channels by using a 1×1 convolutional layer, and halves the height and width of the average pooling layer with a step size of 2, which further reduces the complexity of the model.\n", + "\n", + "```\n", + "class TransitionBlock(tf.keras.layers.Layer):\n", + " def __init__(self, num_channels, **kwargs):\n", + " super(TransitionBlock, self).__init__(**kwargs)\n", + " self.batch_norm = tf.keras.layers.BatchNormalization()\n", + " self.relu = tf.keras.layers.ReLU()\n", + " self.conv = tf.keras.layers.Conv2D(num_channels, kernel_size=1)\n", + " self.avg_pool = tf.keras.layers.AvgPool2D(pool_size=2, strides=2)\n", + "\n", + " def call(self, x):\n", + " x = self.batch_norm(x)\n", + " x = self.relu(x)\n", + " x = self.conv(x)\n", + " return self.avg_pool(x)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q-1bWM1MGg30", + "colab_type": "text" + }, + "source": [ + "## 5. Build a DenseNet Model using Keras\n", + "After setting up the python environment, you should be able to import the keras package:" + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "aC_foy-xzpgf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "aac936c6-bd43-46cc-d44c-e51f372272d7" + }, + "source": [ + "import keras\n", + "keras.__version__" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'2.4.3'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YDmXSwKhzpgn", + "colab_type": "text" + }, + "source": [ + "## 6. Preparing the Data\n", + "First one needs to load the dataset from the MNIST library:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZCajnu4hzpgo", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from keras.datasets import mnist\n", + "\n", + "# obtenemos los datos para train y test \n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tpcHPaRhzpgr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "63858351-00c0-4f89-f215-3cf29c3ee4c8" + }, + "source": [ + "print(x_train.ndim) " + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "3\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LzBpH03Ozpgu", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "2ccdf895-443a-4cdf-8241-a6ebf04a88ca" + }, + "source": [ + "print(x_train.shape)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(60000, 28, 28)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1usAMTejzpgx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "9ac70ac2-b676-4312-f9e2-ae43f95566d3" + }, + "source": [ + "print(x_train.dtype)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "uint8\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8TFyTX5Izpg1", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "e70c35f7-ac6b-4fb0-91bc-447d1d43c6e8" + }, + "source": [ + "len (y_train)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "60000" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mAZ-l-_wHmmC", + "colab_type": "text" + }, + "source": [ + "to see one of the images on the dataset just loaded you can use matplotlib as follows and plot its contents as can be seen on the image below:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "m3KDGNSKzphJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "outputId": "43490c15-b03a-40d6-c96f-57cdebf3d022" + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.imshow(x_train[8], cmap=plt.cm.binary)\n", + "print(y_train[8])" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o3XZyexxHum4", + "colab_type": "text" + }, + "source": [ + "to view the matrix values of the same element selected previously one can use numpy as stated bellow and the result output is on image below the code" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6uuhvKOUzphN", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 493 + }, + "outputId": "e80d4380-d00f-4086-b565-5451eeac79bb" + }, + "source": [ + "import numpy\n", + "from numpy import linalg\n", + "numpy.set_printoptions(precision=2, suppress=True, linewidth=120)\n", + "print(numpy.matrix(x_train[8]))\n", + "\n" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 5 63 197 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 20 254 230 24 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 20 254 254 48 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 20 254 255 48 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 20 254 254 57 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 20 254 254 108 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 16 239 254 143 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 178 254 143 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 178 254 143 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 178 254 162 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 178 254 240 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 113 254 240 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 83 254 245 31 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 79 254 246 38 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 214 254 150 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 144 241 8 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 144 240 2 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 144 254 82 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 230 247 40 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 168 209 31 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "p9NE_biwzphR", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "outputId": "67ad3942-328f-417e-e1e0-d8695f5b1f5f" + }, + "source": [ + "plt.imshow(x_train[9], cmap=plt.cm.binary)\n", + "print(y_train[9])" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "4\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IppVG7Q-H1yX", + "colab_type": "text" + }, + "source": [ + "We also need to normalize the image pixel values ​​on the dataset from [0,255] to [0,1] to make our network easier to train (smaller centering values ​​usually lead to better results). Since Keras requires three-dimensional dimensions, we also adjust the shape of each image from (28, 28) to (28, 28, 1)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Kap8tttJzphc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_train = x_train.astype('float32')\n", + "x_test = x_test.astype('float32')\n", + "\n", + "x_train /= 255\n", + "x_test /= 255\n" + ], + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0l1XPht3zphf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 969 + }, + "outputId": "63c45411-05ed-4a69-c8c8-b3b58578e31c" + }, + "source": [ + "print(numpy.matrix(x_train[8]))" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.02 0.25 0.77 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.08 1. 0.9 0.09 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.08 1. 1. 0.19 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.08 1. 1. 0.19 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.08 1. 1. 0.22 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.08 1. 1. 0.42 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.06 0.94 1. 0.56 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.7 1. 0.56 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.7 1. 0.56 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.7 1. 0.64 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.7 1. 0.94 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.44 1. 0.94 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.33 1. 0.96 0.12 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.31 1. 0.96 0.15 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.84 1. 0.59 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.56 0.95 0.03 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.56 0.94 0.01 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.56 1. 0.32 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.9 0.97 0.16 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.66 0.82 0.12 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. ]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gX3BeSSvzphj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "83c3d736-ac6e-4126-bbb3-570142ae340a" + }, + "source": [ + "\n", + "x_train = x_train.reshape(60000, 784)\n", + "x_test = x_test.reshape(10000, 784)\n", + "\n", + "\n", + "print(x_train.shape)\n", + "print(x_test.shape)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(60000, 784)\n", + "(10000, 784)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YwE3ri33zphq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "818f0400-7c37-40a4-c4ce-e203e5934722" + }, + "source": [ + "print(y_test[0])" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "7\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kahjmM-gzphu", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "c622dd80-a01c-459e-bc4b-83de382fb937" + }, + "source": [ + "print(y_train[0])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "5\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "P9RbfbHFzphx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "b928905b-469f-4170-85b3-516d8c771468" + }, + "source": [ + "print(y_train.shape)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(60000,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iO3Fr5T8zphz", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "d543b0b4-3959-493e-f2f3-eb80d787b4ec" + }, + "source": [ + "print(x_test.shape)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(10000, 784)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kOrbiuypH9Av", + "colab_type": "text" + }, + "source": [ + "encoding labels to categorical data, i.e., simply converting each value in a column to a number as follows :" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mYBIFLxfzph2", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from keras.utils import to_categorical\n", + "\n", + "y_train = to_categorical(y_train, num_classes=10)\n", + "y_test = to_categorical(y_test, num_classes=10)" + ], + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KEGK2v2Tzph8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "9ba4f5dd-7f8d-4e57-b20c-ed7f06d773a3" + }, + "source": [ + "print(y_test[0])" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IUUZ52EZIBaO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "wgygSQK-zpiA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "23bb5837-7c21-4175-e5e9-9dc494d27ed6" + }, + "source": [ + "print(y_train[0])" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9uzPR4sPzpiF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7e43edf9-bb50-42ae-87c4-d1aba205bd02" + }, + "source": [ + "print(y_train.shape)" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(60000, 10)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2zh8nKwZzpiI", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "f8a832b9-effd-48fe-e97a-48e746cfd386" + }, + "source": [ + "print(y_test.shape)" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(10000, 10)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zsh193_tIC3w", + "colab_type": "text" + }, + "source": [ + "## 7. Building the Model\n", + "Each Keras model is either built using Sequential classes that represent a linear layer stack, or built using customizable functional Model classes. We will use a simpler sequential model because our CNN will be a linear layer stack.\n", + "We start by instantiating a Sequential model:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uZcNXEmrzpiL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "f827ae39-4c3d-41f6-97f0-7796a120090b" + }, + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.optimizers import SGD\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(10, activation='sigmoid', input_shape=(784,)))\n", + "model.add(Dense(10, activation='softmax'))\n", + "\n", + "model.summary()" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense (Dense) (None, 10) 7850 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 10) 110 \n", + "=================================================================\n", + "Total params: 7,960\n", + "Trainable params: 7,960\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wjecSCMqzpiO", + "colab_type": "text" + }, + "source": [ + "\n", + "## 8. Compiling and fitting the Model\n", + "Before starting the training, we need to configure the training process. We have identified 3 key factors in the compilation step:\n", + "* The optimizer. We will keep a very good default value: an optimizer based on Adam gradient. Keras also provides many other optimizers.\n", + "* The loss function. Since we are using the Softmax output layer, we will use cross entropy loss. Keras distinguishes binary_crossentropy (2 classes) and categorical_crossentropy (> 2 classes), so we will use the latter. See all Keras losses.\n", + "* A list of metrics. Since this is a classification problem, we only need Keras to report accuracy metrics.\n", + "\n", + "Next, you need to specify some parameters, and then start the model fitting. There are many possible parameters, but we will only provide these parameters:\n", + "* The training data (images and labels), commonly known as X and Y, respectively.\n", + "* The number of epochs (iterations over the entire dataset) to train for.\n", + "* The batch size (number of samples processed before the model is updated)\n", + "\n", + "Calling model.fit()performs the analysis of the given input, then calculates the indicator function specified in model.compile() based on train_images and train_labels, and returns the calculated indicator value as output.\n", + "\n", + "We must pay attention to one thing: Keras expects the training target to be a 10-dimensional vector, because there are 10 nodes in our hidden layers.\n", + "\n", + "The compilation looks like this:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-FTm728IzpiO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "outputId": "2e5a2eee-5552-48dc-f9c1-4d0265de5f87" + }, + "source": [ + "batch_size = 50\n", + "num_classes = 10\n", + "epochs=10\n", + "\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer='sgd',\n", + " metrics=['accuracy'])\n", + "\n", + "model.fit(x_train, y_train,\n", + " batch_size=batch_size,\n", + " epochs=epochs,\n", + " verbose=1\n", + " )\n" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 2.0782 - accuracy: 0.4062\n", + "Epoch 2/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 1.6672 - accuracy: 0.6637\n", + "Epoch 3/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 1.3254 - accuracy: 0.7399\n", + "Epoch 4/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 1.0804 - accuracy: 0.7790\n", + "Epoch 5/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 0.9147 - accuracy: 0.8075\n", + "Epoch 6/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 0.7993 - accuracy: 0.8297\n", + "Epoch 7/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 0.7159 - accuracy: 0.8437\n", + "Epoch 8/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 0.6536 - accuracy: 0.8546\n", + "Epoch 9/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 0.6057 - accuracy: 0.8614\n", + "Epoch 10/10\n", + "1200/1200 [==============================] - 1s 1ms/step - loss: 0.5679 - accuracy: 0.8667\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5c85q-NWIqA1", + "colab_type": "text" + }, + "source": [ + "## 9. Evaluating the Model\n", + "Evaluating a model from Keras actually only involves calling the fucntionevaluate(). The model.evaluate function\n", + "* The validation data (or test data), which is used during training to periodically measure the performance of the network based on data that has never been seen before\n", + "\n", + "The evaluation results are retrieved form the variables assigned to the function evaluate()." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LJ9BiDuuIqpv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "6de4df1d-1939-4458-b46f-f7b54d00af30" + }, + "source": [ + "test_loss, test_acc = model.evaluate(x_test, y_test)\n", + "\n", + "print('Test loss:', test_loss)\n", + "print('Test accuracy:', test_acc)" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "313/313 [==============================] - 0s 858us/step - loss: 0.5391 - accuracy: 0.8737\n", + "Test loss: 0.5390798449516296\n", + "Test accuracy: 0.8737000226974487\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HKG39fBJzpiW", + "colab_type": "text" + }, + "source": [ + "## 10. Predicting the Model\n", + "\n", + "Predicting a model from Keras actually only involves calling the functionmodel.predict().\n", + "* The validation data (or test data), which is used during training to periodically measure the performance of the network based on data that has never been seen before\n", + "\n", + "The prediction results are retrieved from the dataset assigned to the function predict()." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VCY68_NlzpiX", + "colab_type": "code", + "colab": {} + }, + "source": [ + "predictions = model.predict(x_test)" + ], + "execution_count": 27, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "coFGRCXbJL4w", + "colab_type": "text" + }, + "source": [ + "Remember the result data is encoded to categorial data and this means each element position on the vector represents the integer value at test. For instance, to view the predictions results for the 10th element in the dataset :" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HvYHs-Xizpia", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "5d42d4f7-6e72-46dd-ce57-317a764647ec" + }, + "source": [ + "print(predictions[10])" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0.78 0. 0.04 0.04 0. 0.07 0. 0.01 0.05 0. ]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r2XcgiqsJa4Q", + "colab_type": "text" + }, + "source": [ + "The predicted element with the higher probability, in the categorical dataset, corresponds to the predicted number, for the same 11th element, can be calculated as follows :" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EYJW-JMvzpig", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ac9af29a-cb6c-4ac1-8f0a-ac01d44c292d" + }, + "source": [ + "numpy.sum(predictions[10])" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hxPhBDVyJgyp", + "colab_type": "text" + }, + "source": [ + "To view the predicted value (number) one can call the argmax value or “maximum arguments”, the index of the maximum value of each element in the list:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dVojR9O-zpil", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "811869e2-d12d-45c9-cd79-3a72f513d35b" + }, + "source": [ + "numpy.argmax(predictions[10])" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 30 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_VV89hUpJjQx", + "colab_type": "text" + }, + "source": [ + "## 11. View the Confusion Matrix\n", + "The code below is taken directly from the SKLEARN website, which is a good way to view the confusion matrix. The function presented below prints and plots the confusion matrix. Normalization can be applied by setting “normalize = True”." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nXgPTfVMzpip", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Look at confusion matrix \n", + "#Note, this code is taken straight from the SKLEARN website, an nice way of viewing confusion matrix.\n", + "def plot_confusion_matrix(cm, classes,\n", + " normalize=False,\n", + " title='Confusion matrix',\n", + " cmap=plt.cm.Blues):\n", + " \"\"\"\n", + " This function prints and plots the confusion matrix.\n", + " Normalization can be applied by setting `normalize=True`.\n", + " \"\"\"\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " tick_marks = numpy.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=45)\n", + " plt.yticks(tick_marks, classes)\n", + "\n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + "\n", + " thresh = cm.max() / 2.\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, cm[i, j],\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + "\n", + " plt.tight_layout()\n", + " plt.ylabel('Actual class')\n", + " plt.xlabel('Predicted class')\n", + "\n" + ], + "execution_count": 31, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KEPxuxfcJu7X", + "colab_type": "text" + }, + "source": [ + "Next, is needed to “one hot encode” the categorical data so i can be easily understandable. One hot encoding is a way of represent categorical variables as binary vectors. This requires mapping the classification values to integer values. Then, each integer value is represented as a binary vector, which has all zero values ​​except for the integer index (the index marked with 1)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ICQFgxEyzpis", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from collections import Counter\n", + "from sklearn.metrics import confusion_matrix\n", + "import itertools\n", + "\n", + "# Predict the values from the validation dataset\n", + "Y_pred = model.predict(x_test)\n", + "# Convert predictions classes to one hot vectors \n", + "Y_pred_classes = numpy.argmax(Y_pred, axis = 1) \n", + "# Convert validation observations to one hot vectors\n", + "Y_true = numpy.argmax(y_test, axis = 1) \n" + ], + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cp0Rr_QeJ3SV", + "colab_type": "text" + }, + "source": [ + "and finally compute and plot the confusion matrix in a nice and readable graph chart type as follows :" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Rqo3f7F-J4Nf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 311 + }, + "outputId": "22b3b775-d512-4b88-8602-5838df0e6e1d" + }, + "source": [ + "# compute the confusion matrix\n", + "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n", + "# plot the confusion matrix\n", + "plot_confusion_matrix(confusion_mtx, classes = range(10))" + ], + "execution_count": 33, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8AHw3nnTKBga", + "colab_type": "text" + }, + "source": [ + "## 12. References\n", + "[1] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Y0CMk2YQ6_OV", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 33, + "outputs": [] + } + ] +} \ No newline at end of file From c17977a149d83b5a20c685ff67812834a83a89b6 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Sun, 6 Sep 2020 19:59:09 +0200 Subject: [PATCH 12/13] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 1bba79c4..8aaaea24 100644 --- a/README.md +++ b/README.md @@ -29,6 +29,8 @@ It is suitable for beginners who want to find clear and concise examples about T - **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. - **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. +- **Short Tutorial with code on Convolutional Neural Network (low-level)** ([notebook](https://github.com/aeonSolutions/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/Convolutional_neural_networks.ipynb)). Short Tutorial on how to implement a convolutional neural network to classify MNIST digits dataset. +- **Densely Connected Networks (low-level)** ([notebook](https://github.com/aeonSolutions/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/Densely_connected_networks_ipy.ipynb)). Short Tutorial on how to implement a DesNet to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. - **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. - **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API. From d0ca36c85f3742107c4bda87cb972c81ffb80f21 Mon Sep 17 00:00:00 2001 From: Miguel Silva Date: Sun, 6 Sep 2020 19:59:59 +0200 Subject: [PATCH 13/13] Update README.md --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 8aaaea24..848c1e11 100644 --- a/README.md +++ b/README.md @@ -4,8 +4,6 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (05/16/2020):** Moving all default examples to TF2. For TF v1 examples: [check here](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1). - ## Tutorial index #### 0 - Prerequisite