phase_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 1 of project: Data Wrangling\n",
    "\n",
    "- We will be using the mnist dataset for this project.\n",
    "- The first iteration will use pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: X=(60000, 28, 28), y=(60000,)\n",
      "Test: X=(10000, 28, 28), y=(10000,)\n"
     ]
    }
   ],
   "source": [
    "# load the dataset\n",
    "\n",
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "(train_X, train_y), (test_X, test_y) = mnist.load_data()\n",
    "\n",
    "print('Train: X=%s, y=%s' % (train_X.shape, train_y.shape))\n",
    "print('Test: X=%s, y=%s' % (test_X.shape, test_y.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the dataset\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i in range(9):\n",
    "    plt.subplot(330 + 1 + i)\n",
    "    plt.imshow(train_X[i], cmap=plt.get_cmap('gray'))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the model\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# Conv2D takes input images and converts them into feature maps\n",
    "# MaxPooling2D reduces the size of the feature maps, reducing the number of parameters in the network\n",
    "# Flatten flattens the feature maps into a single vector to pass to the fully connected layers\n",
    "# Dense is a fully connected layer\n",
    "# The last layer is a softmax layer with 10 nodes, one for each class (0-9)\n",
    "\n",
    "model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the model\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-17 13:09:35.398455: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.\n",
      "2022-10-17 13:09:46.531273: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 31360000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300/300 - 12s - loss: 0.2258 - accuracy: 0.9352 - val_loss: 0.0682 - val_accuracy: 0.9789 - 12s/epoch - 39ms/step\n",
      "Epoch 2/10\n",
      "300/300 - 11s - loss: 0.0658 - accuracy: 0.9806 - val_loss: 0.0502 - val_accuracy: 0.9842 - 11s/epoch - 36ms/step\n",
      "Epoch 3/10\n",
      "300/300 - 11s - loss: 0.0449 - accuracy: 0.9863 - val_loss: 0.0368 - val_accuracy: 0.9881 - 11s/epoch - 36ms/step\n",
      "Epoch 4/10\n",
      "300/300 - 11s - loss: 0.0347 - accuracy: 0.9895 - val_loss: 0.0383 - val_accuracy: 0.9873 - 11s/epoch - 36ms/step\n",
      "Epoch 5/10\n",
      "300/300 - 11s - loss: 0.0265 - accuracy: 0.9922 - val_loss: 0.0360 - val_accuracy: 0.9889 - 11s/epoch - 36ms/step\n",
      "Epoch 6/10\n",
      "300/300 - 11s - loss: 0.0206 - accuracy: 0.9935 - val_loss: 0.0329 - val_accuracy: 0.9883 - 11s/epoch - 36ms/step\n",
      "Epoch 7/10\n",
      "300/300 - 11s - loss: 0.0165 - accuracy: 0.9949 - val_loss: 0.0389 - val_accuracy: 0.9877 - 11s/epoch - 36ms/step\n",
      "Epoch 8/10\n",
      "300/300 - 11s - loss: 0.0131 - accuracy: 0.9965 - val_loss: 0.0361 - val_accuracy: 0.9889 - 11s/epoch - 36ms/step\n",
      "Epoch 9/10\n",
      "300/300 - 11s - loss: 0.0105 - accuracy: 0.9969 - val_loss: 0.0389 - val_accuracy: 0.9890 - 11s/epoch - 36ms/step\n",
      "Epoch 10/10\n",
      "300/300 - 11s - loss: 0.0082 - accuracy: 0.9975 - val_loss: 0.0369 - val_accuracy: 0.9891 - 11s/epoch - 36ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f79b8746a40>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model\n",
    "\n",
    "from keras.utils import np_utils\n",
    "\n",
    "train_X = train_X.reshape((train_X.shape[0], 28, 28, 1)).astype('float32')\n",
    "test_X = test_X.reshape((test_X.shape[0], 28, 28, 1)).astype('float32')\n",
    "\n",
    "train_X = train_X / 255\n",
    "test_X = test_X / 255\n",
    "\n",
    "train_y = np_utils.to_categorical(train_y)\n",
    "test_y = np_utils.to_categorical(test_y)\n",
    "\n",
    "model.fit(train_X, train_y, validation_data=(\n",
    "    test_X, test_y), epochs=10, batch_size=200, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-17 13:11:24.293399: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 31360000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN Error: 1.09%\n"
     ]
    }
   ],
   "source": [
    "# validate the model\n",
    "\n",
    "scores = model.evaluate(test_X, test_y, verbose=0)\n",
    "print(\"CNN Error: %.2f%%\" % (100-scores[1]*100))"
   ]
  }
 ],
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