CNN_hands-on_2021.ipynb

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   "source": [
    "# Lab 2: Convolutional Neural Network (CNN)"
   ]
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    "\n",
    "\n",
    "\n",
    "In this laboratory we continue to work with Keras. We will focus on Convolutional Neural Network\n",
    "we are going to work with cifar10, a  dataset consisting of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n",
    "Therefore the main goal of this laboratory is to solve a multiclass classification problem with 10 different classes\n",
    "\n",
    "<br><img src= \"https://drive.google.com/uc?export=view&id=1u-htQUPUm40h4HBBvg-JA5r0IfrRHqrq\" width=\"500px\" align=\"middle\"><br>)"
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   "source": [
    "# loading the dataset "
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf \n",
    "import os\n",
    "new_im_size = 32\n",
    "channels = 3\n",
    "\n",
    "(train_X,train_Y),(test_X,test_Y) = tf.keras.datasets.cifar10.load_data()\n"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# 2.1 Dataset pre-processing\n",
    "The first thing that we need to do when we are dealing with a new dataset is to operate some pre-processing operations. Data preprocessing usually refers to the steps applied to make data more suitable for learning. \n",
    "In this section we are going to deal with:\n",
    "\n",
    "* 2.1.1 Normalization\n",
    "* 2.1.2 Standardization\n",
    "* 2.1.3 Splitting and label preprocessing\n"
   ]
  },
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   "source": [
    "## 2.1.1 Normalization\n",
    "One common practice in training a Neural Network is to normalize the images by dividing each pixel value by the maximum value that we can have, i.e. 255.<br>\n",
    "The purpose of this is to obtain a mean close to 0.<br>\n",
    "Normalizing the data generally speeds up learning and leads to faster convergence"
   ]
  },
  {
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   "source": [
    "# Normalizing the data\n",
    "print(\"Normalizing training set..\")\n",
    "train_X = np.asarray(train_X, dtype=np.float32) / 255\t\t\t\t\t\t\t\t\t\t# Normalizing training set\n",
    "print(\"Normalizing test set..\")\n",
    "test_X = np.asarray(test_X, dtype=np.float32) / 255\t\t\t\t\t\t\t\t\t\t\t# Normalizing test set"
   ]
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    "## 2.1.3 Standardization\n",
    "Another common practice in data pre-processing is standardization.<br>\n",
    "The idea about standardization is to compute your dataset mean and standard deviation in order to subtract from every data point $x$ the dataset mean $\\mu$ and then divide by the standard deviation $\\sigma$.<br>\n",
    "That is to apply the following operation:<br>\n",
    "<img src=\"https://drive.google.com/uc?id=1rpuybw_fmI8XK38JQhWWxX2TOExBAV2V\" width=\"150px\"><br>\n",
    "The outcome of this operation is to obtain a distribution with mean equal to 0 and a standard deviation equal to 1.<br>\n",
    "By applying normalization to our data we are making the features more similar to each other and this usually makes the learning process easier.<br>\n",
    "To better understand that we can show an example of what happens after a standardization process is applied to a dataset:\n",
    "<img src=\"https://drive.google.com/uc?id=1wtqTW4hz8n8k7b7q0mUSzCc9X0npNUY2\" width=\"500px\" align=\"left\"><br>"
   ]
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   "source": [
    "# Standardizing the data\n",
    "def standardize_dataset(X):\n",
    "\timage_means = []\n",
    "\timage_stds = []\n",
    "\n",
    "\tfor image in X:\n",
    "\t\timage_means.append(np.mean(image))\t\t\t\t\t\t\t\t\t\t\t# Computing the image mean\n",
    "\t\timage_stds.append(np.std(image))\t\t\t\t\t\t\t\t\t\t\t# Computing the image standard deviation\n",
    "\n",
    "\tdataset_mean = np.mean(image_means)\t\t\t\t\t\t\t\t\t\t\t\t# Computing the dataset mean\n",
    "\tdataset_std = np.mean(image_stds)\t\t\t\t\t\t\t\t\t\t\t\t# Computing the dataset standard deviation\n",
    "\treturn [dataset_mean, dataset_std]\t\t\t\t\t# For every image we subtract to it the dataset mean and we divide by the dataset standard deviation\n"
   ]
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   "source": [
    "dataset_mean, dataset_std = standardize_dataset(train_X)\n",
    "print(\"Standardizing training set..\")\n",
    "train_X = (train_X-dataset_mean)/dataset_std\t\t\t\t\t\t\t\t\t\t\t\t# Standardizing the training set\n",
    "print(\"Standardizing test set..\")\n",
    "test_X = (test_X-dataset_mean)/dataset_std\t\t\t\t\t\t\t\t\t\t\t\t# Standardizing the test set"
   ]
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   "source": [
    "## 2.1.4 Splitting and label preprocessing\n",
    "Now we just need to split our training set in orer to get the validation set and convert our labels to one-hot representation"
   ]
  },
  {
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     "text": [
      "Splitting training set to create validation set..\n",
      "40000\n",
      "40000\n"
     ]
    }
   ],
   "source": [
    "# Creating the validation set\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(\"Splitting training set to create validation set..\")\n",
    "train_X, valid_X, train_Y, valid_Y = train_test_split(train_X, train_Y, test_size=0.2, random_state=13)\n",
    "\n",
    "# Converting labels to one-hot representation\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "train_Y_one_hot = to_categorical(train_Y)\t\t\t\t\t\t# Converting training labels to one-hot representation\n",
    "valid_Y_one_hot = to_categorical(valid_Y)\t\t\t\t\t\t# Converting validation labels to one-hot representation\n",
    "test_Y_one_hot = to_categorical(test_Y)\t\t\t\t\t\t\t# Converting test labels to one-hot representation\n",
    "\n",
    "print(len(train_X))\n",
    "print(len(train_Y_one_hot))"
   ]
  },
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   "source": [
    "# 2.2 Training a model from scratch\n",
    "Now that we have properly pre-processed our data, we are going to create a convolutional model in Keras. \n",
    "Usually a convolutional model is made by two subsequent part:\n",
    "* A convolutional part\n",
    "* A fully connected\n",
    "\n",
    "We can show an example of the general structure in the next picture:\n",
    "<img src=\"https://drive.google.com/uc?id=1duP8u9bs6ELNu4degUuYP4-YS1mBYn2O\" width=\"600px\"><br>\n",
    "\n",
    "Usually the convolutional part is made by some layers composed by\n",
    "* convolutional layer: performs a spatial convolution over images\n",
    "* pooling layer: used to reduce the output spatial dimension from $n$ to 1 by averaging the $n$ different value or considering the maximum between them \n",
    "* dropout layer: applied to a layer, consists of randomly \"dropping out\" (i.e. set to zero) a number of output features of the layer during training.\n",
    "\n",
    "The convolutional part produces its output and the fully connected part ties together the received information in order to solve the classification problem.\n",
    "Let us start with a shallow architecture with only 2 conv"
   ]
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     "text": [
      "10\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_14 (Conv2D)           (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 128)               1048704   \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,060,138\n",
      "Trainable params: 1,060,138\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Creating the model from scratch\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras import Sequential,Input,Model\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Network parameters\n",
    "batch_size = 64\t\t\t\t\t\t\t\t\t\t\t\t\t# Setting the batch size\n",
    "epochs = 10\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Setting the number of epochs\n",
    "num_classes = 10\t\t\t\t\t\t\t\t\t# Getting the amount of classes\n",
    "print(num_classes)\n",
    "scratch_model = Sequential()\t\n",
    "\n",
    "# Build here your keras model.\n",
    "# You can try to use one or more convolutional layer, joint with pooling layer and dropout layer\n",
    "\n",
    "scratch_model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
    "scratch_model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "\n",
    "# pooling layer \n",
    "scratch_model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "#flatten the features for fully-connected layers of our network \n",
    "scratch_model.add(Flatten())\n",
    "\n",
    "# Adding the dense final part\n",
    "scratch_model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
    "scratch_model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "\n",
    "# Compile the model with the Adam optimizer\n",
    "scratch_model.compile(loss=tensorflow.keras.losses.categorical_crossentropy, optimizer=tensorflow.keras.optimizers.Adam(),metrics=['accuracy'])\n",
    "\n",
    "# Visualize the model through the summary function\n",
    "scratch_model.summary()"
   ]
  },
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   "execution_count": 16,
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    "colab_type": "code",
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     "text": [
      "Epoch 1/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 27.2777 - accuracy: 0.2670 - val_loss: 1.7774 - val_accuracy: 0.3639\n",
      "Epoch 2/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 1.6962 - accuracy: 0.3973 - val_loss: 1.7524 - val_accuracy: 0.3948\n",
      "Epoch 3/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 1.5062 - accuracy: 0.4629 - val_loss: 1.6318 - val_accuracy: 0.4351\n",
      "Epoch 4/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 1.3437 - accuracy: 0.5235 - val_loss: 1.5969 - val_accuracy: 0.4560\n",
      "Epoch 5/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 1.1923 - accuracy: 0.5780 - val_loss: 1.6707 - val_accuracy: 0.4619\n",
      "Epoch 6/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 1.0580 - accuracy: 0.6245 - val_loss: 1.6992 - val_accuracy: 0.4694\n",
      "Epoch 7/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 0.9431 - accuracy: 0.6708 - val_loss: 1.8897 - val_accuracy: 0.4707\n",
      "Epoch 8/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 0.8217 - accuracy: 0.7099 - val_loss: 1.9535 - val_accuracy: 0.4657\n",
      "Epoch 9/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 0.7235 - accuracy: 0.7456 - val_loss: 2.1577 - val_accuracy: 0.4652\n",
      "Epoch 10/10\n",
      "625/625 [==============================] - 4s 6ms/step - loss: 0.6509 - accuracy: 0.7734 - val_loss: 2.2684 - val_accuracy: 0.4692\n"
     ]
    }
   ],
   "source": [
    "# Let's train the model!\n",
    "scratch_model_history = scratch_model.fit(train_X, train_Y_one_hot, batch_size=batch_size, shuffle=True, epochs=epochs, validation_data=(valid_X, valid_Y_one_hot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1337,
     "status": "ok",
     "timestamp": 1590579158311,
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   "outputs": [
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     "text": [
      "Test accuracy:  0.4656\n"
     ]
    }
   ],
   "source": [
    "# Getting the results\n",
    "scratch_model_train_acc = scratch_model_history.history['accuracy']\n",
    "scratch_model_valid_acc = scratch_model_history.history['val_accuracy']\n",
    "scratch_model_train_loss = scratch_model_history.history['loss']\n",
    "scratch_model_valid_loss = scratch_model_history.history['val_loss']\n",
    "\n",
    "print(\"Test accuracy: \", accuracy_score(scratch_model.predict_classes(test_X), test_Y))\t\t\t# Testing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TWiB0JKK2iV1"
   },
   "source": [
    "**Is the obtained value coherent with what you expected?**<br>\n",
    "**What are the differences when using a different batch size? Why?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Try to make the network deeper, adding more Conv and Pooling layers. Do the performances improve? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 8, 8, 128)         147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               262272    \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 550,570\n",
      "Trainable params: 550,570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "deeper_model = Sequential()\n",
    "\n",
    "deeper_model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
    "deeper_model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "deeper_model.add(MaxPooling2D((2, 2)))\n",
    "\n",
    "\n",
    "\n",
    "#add convolutional layers here \n",
    "\n",
    "\n",
    "\n",
    "# add fully connected layers for classification \n",
    "\n",
    "# compile model\n",
    "deeper_model.compile(loss=tensorflow.keras.losses.categorical_crossentropy, optimizer=tensorflow.keras.optimizers.Adam(),metrics=['accuracy'])\n",
    "\n",
    "deeper_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Epoch 1/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 7.1947 - accuracy: 0.3625 - val_loss: 1.5627 - val_accuracy: 0.4353\n",
      "Epoch 2/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 1.3952 - accuracy: 0.4982 - val_loss: 1.3241 - val_accuracy: 0.5364\n",
      "Epoch 3/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 1.2355 - accuracy: 0.5596 - val_loss: 1.1878 - val_accuracy: 0.5833\n",
      "Epoch 4/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 1.1156 - accuracy: 0.6042 - val_loss: 1.1650 - val_accuracy: 0.5845\n",
      "Epoch 5/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 1.0248 - accuracy: 0.6375 - val_loss: 1.1218 - val_accuracy: 0.6104\n",
      "Epoch 6/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 0.9421 - accuracy: 0.6701 - val_loss: 1.1036 - val_accuracy: 0.6229\n",
      "Epoch 7/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 0.8820 - accuracy: 0.6903 - val_loss: 1.0776 - val_accuracy: 0.6340\n",
      "Epoch 8/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 0.8247 - accuracy: 0.7098 - val_loss: 0.9977 - val_accuracy: 0.6587\n",
      "Epoch 9/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 0.7580 - accuracy: 0.7338 - val_loss: 1.0661 - val_accuracy: 0.6515\n",
      "Epoch 10/10\n",
      "625/625 [==============================] - 6s 10ms/step - loss: 0.7121 - accuracy: 0.7506 - val_loss: 0.9681 - val_accuracy: 0.6742\n"
     ]
    }
   ],
   "source": [
    "deeper_model_history = deeper_model.fit(train_X, train_Y_one_hot, batch_size=batch_size, shuffle=True, epochs=epochs, validation_data=(valid_X, valid_Y_one_hot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-10-cc6763a17549>:6: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "Test accuracy:  0.6697\n"
     ]
    }
   ],
   "source": [
    "# Getting the results\n",
    "deeper_model_train_acc = deeper_model_history.history['accuracy']\n",
    "deeper_model_valid_acc = deeper_model_history.history['val_accuracy']\n",
    "deeper_model_train_loss = deeper_model_history.history['loss']\n",
    "deeper_model_valid_loss = deeper_model_history.history['val_loss']\n",
    "print(\"Test accuracy: \", accuracy_score(deeper_model.predict_classes(test_X), test_Y))\t\t\t# Testing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IkRwEUtQ2iV1"
   },
   "source": [
    "# 2.3 Data Augmentation\n",
    "Before even starting to load the dataset we should ask ourself whether the available amount of data is sufficient to our purposes.<br>\n",
    "When the answer is negative we could need to do \"data augmentation\".<br>\n",
    "Doing data augmentation means to increase the number of available data points. In terms of images, it may mean that increasing the number of images in the dataset. A common way to do this is to generate new images by applying a linear transformation to the original images in the dataset.<br>\n",
    "The most common linear transformations are the following:<br>\n",
    "* Rotation\n",
    "* Shifting\n",
    "* Blurring\n",
    "* Change lighting conditions\n",
    "\n",
    "In the picture below we show an example of augmentation:<br>\n",
    "<img src=\"https://drive.google.com/uc?id=1B74snda_oJKkhVzxch9Ov8Y1XL63U3w5\" width=\"600px\" align=\"left\"><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dU3jpQx12iV2"
   },
   "outputs": [],
   "source": [
    "# Data augmentation\n",
    "# The augm_factor determine the copy amount created from every single image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "datagen_train = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1,rotation_range=45, horizontal_flip=True)\n",
    "(train_X,train_Y),(test_X,test_Y) = tf.keras.datasets.cifar10.load_data()\n",
    "# Normalizing the data\n",
    "print(\"Normalizing training set..\")\n",
    "train_X = np.asarray(train_X, dtype=np.float32) / 255\t\t\t\t\t\t\t\t\t\t# Normalizing training set\n",
    "print(\"Normalizing test set..\")\n",
    "test_X = np.asarray(test_X, dtype=np.float32) / 255\t\t\t\t\t\t\t\t\t\t\t# Normalizing test set\n",
    "batch_size = 64\t\t\t\t\t\t\t\t\t\t\t\t\t# Setting the batch size\n",
    "epochs = 5\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Setting the number of epochs\n",
    "num_classes = 10\n",
    "# Creating the validation set\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(\"Splitting training set to create validation set..\")\n",
    "train_X, valid_X, train_Y, valid_Y = train_test_split(train_X, train_Y, test_size=0.2, random_state=13)\n",
    "\n",
    "# Converting labels to one-hot representation\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "train_Y_one_hot = to_categorical(train_Y)\t\t\t\t\t\t# Converting training labels to one-hot representation\n",
    "valid_Y_one_hot = to_categorical(valid_Y)\t\t\t\t\t\t# Converting validation labels to one-hot representation\n",
    "test_Y_one_hot = to_categorical(test_Y)\t\t\t\t\t\t\t# Converting test labels to one-hot representation\n",
    "print(len(train_X))\n",
    "print(len(train_Y_one_hot))\n",
    "train = datagen_train.flow(train_X,train_Y_one_hot, batch_size=batch_size)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i4HmIwcZ2iWK"
   },
   "source": [
    "Now try to build a new model dealing with the augmented dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deeper_model.fit_generator(train,steps_per_epoch = int(train_X.shape[0]/batch_size),shuffle=True, epochs=epochs, validation_data=(valid_X, valid_Y_one_hot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.6697\n"
     ]
    }
   ],
   "source": [
    "# Getting the results\n",
    "deeper_model_train_acc = deeper_model_history.history['accuracy']\n",
    "deeper_model_valid_acc = deeper_model_history.history['val_accuracy']\n",
    "deeper_model_train_loss = deeper_model_history.history['loss']\n",
    "deeper_model_valid_loss = deeper_model_history.history['val_loss']\n",
    "print(\"Test accuracy: \", accuracy_score(deeper_model.predict_classes(test_X), test_Y))\t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZEQiqXC42iWP"
   },
   "source": [
    "**What is the performance obtained on this new augmented dataset?**<br>\n",
    "**How can you explain the obtained result?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s3_XbZQg2iWQ"
   },
   "source": [
    "# 2.4 Using a pre-trained model\n",
    "A common alternative to train a model from scratch consists in using a pre-trained model.<br>\n",
    "The idea is to replace the convolutional part with a highly optimized convolutional part engineered and trained previously by someone else.<br>\n",
    "Usually the models that we can use through keras.applications have been trained over the image net dataset. <br>\n",
    "Today we are going to use the Xception Net model. Its architecture it is shown below:\n",
    "<img src=\"https://drive.google.com/uc?id=1eKivBCSKnWKyBxmGe5s64oOyhzhuCaqU\" width=\"600px\"><br>\n",
    "After the convolutional part replacement we still need to set up a fully connected part.<br>\n",
    "**Why in this lab we cannot use the fully connected part of Xception Net?<br>\n",
    "What should we do to use it?<br>\n",
    "And more in general in which situations we can do that?**\n",
    "\n",
    "Moreover, using a pre-trained network is not always the best choice<br>\n",
    "**Can you guess in which situations could be useful to use a pre-trained model?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nc8dz23k2iWR"
   },
   "outputs": [],
   "source": [
    "# Creating the model based over the pretrained Xception network\n",
    "from tensorflow.keras import applications\n",
    "import tensorflow\n",
    "from tensorflow.keras import models\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import optimizers\n",
    "model = models.Sequential()\n",
    "\n",
    "model.add(tensorflow.keras.layers.UpSampling2D(size=(7,7),input_shape=(32,32,3)))\n",
    "\n",
    "Xception_model = applications.Xception(weights = \"imagenet\", include_top=False, input_shape = (224, 224, channels))\n",
    "\n",
    "for layer in Xception_model.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "Inputs = layers.Input(shape=(32,32,3))\n",
    "x = model(Inputs)\n",
    "x = Xception_model(x)\n",
    "x = layers.Flatten()(x)\n",
    "# let's add a fully-connected layer\n",
    "x = layers.Dense(128, activation='relu')(x)\n",
    "# and a logistic layer -- let's say we have 200 classes\n",
    "predictions = layers.Dense(10, activation='softmax')(x)\n",
    "\n",
    "# this is the model we will train\n",
    "pre_trained_model = tensorflow.keras.Model(Inputs, outputs=predictions)\n",
    "pre_trained_model.compile(loss=tensorflow.keras.losses.categorical_crossentropy, optimizer=tensorflow.keras.optimizers.Adam(),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kyHEx6bS2iWU"
   },
   "outputs": [],
   "source": [
    "# Visualize the model through the summary function\n",
    "pre_trained_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E1Dkp9ix2iWX"
   },
   "outputs": [],
   "source": [
    "# Let's train the model!\n",
    "pretrained_model_history = pre_trained_model.fit(train_X, train_Y_one_hot, epochs=epochs, batch_size=batch_size, validation_data=(valid_X, valid_Y_one_hot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WJaZdXQT2iWb"
   },
   "outputs": [],
   "source": [
    "# Getting the results\n",
    "pretrained_model_train_acc = pretrained_model_history.history['acc']\n",
    "pretrained_model_valid_acc = pretrained_model_history.history['val_acc']\n",
    "pretrained_model_train_loss = pretrained_model_history.history['loss']\n",
    "pretrained_model_valid_loss = pretrained_model_history.history['val_loss']\n",
    "\n",
    "test_X_feature = pre_trained_model.predict(test_X)\t\t\t\t\t\t# Producing the test feature\n",
    "print(\"Test accuracy: \", accuracy_score(pretrained_model.predict_classes(test_X_feature), test_Y)) # Testing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o_2_LXsy2iWg"
   },
   "source": [
    "# 2.5 Comparing the models\n",
    "\n",
    "Now that we trained both the \"from scratch\" and the \"pre-trained\" models, we are going to compare the obtained results obtained during the training. We are going to consider accuracy and loss.<br>\n",
    "**What can you expect from these plots?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vovtvdUg2iWh",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Producing accuracy over epochs plot\n",
      "Producing loss over epochs plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create here the plots to compare the \"from scratch\" model and the \"pretrained\" model\n",
    "# Try to produce a comparison plot about the accuracies (train and validation) and another plot for the losses\n",
    "# Creating the plots to compare the \"from scratch\" model and the \"pretrained\" model\n",
    "# Producing accuracy over epochs plot\n",
    "print(\"Producing accuracy over epochs plot\")\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(16,7))\n",
    "\n",
    "plt.plot(scratch_model_train_acc, label=\"Scratch Train Acc.\", color=\"#4db8ff\")\n",
    "plt.plot(scratch_model_valid_acc, label=\"Scratch Valid. Acc.\", color=\"#006bb3\")\n",
    "\n",
    "plt.plot(deeper_model_train_acc, label=\"Deeper Train Acc.\", color=\"#ff4d4d\")\n",
    "plt.plot(deeper_model_valid_acc, label=\"Deeper Valid. Acc.\", color=\"#b30000\")\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy(%)')\n",
    "plt.legend(loc='lower right', fancybox=True, shadow=True, ncol=4)\n",
    "plt.grid()\n",
    "plt.savefig('acc_epochs.png', dpi=300)\n",
    "\n",
    "\n",
    "# Producing loss over epochs plot\n",
    "print(\"Producing loss over epochs plot\")\n",
    "fig = plt.figure(figsize=(16,7))\n",
    "\n",
    "plt.plot(scratch_model_train_loss, label=\"Scratch Train Loss\", color=\"#4db8ff\")\n",
    "plt.plot(scratch_model_valid_loss, label=\"Scratch Valid. Loss\", color=\"#006bb3\")\n",
    "\n",
    "plt.plot(deeper_model_train_loss, label=\"Deeper Train Loss\", color=\"#ff4d4d\")\n",
    "plt.plot(deeper_model_valid_loss, label=\"Deeper Valid. Loss\", color=\"#b30000\")\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc='upper right', fancybox=True, shadow=True, ncol=4)\n",
    "plt.grid()\n",
    "plt.savefig('loss_epochs.png', dpi=300)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bszQNCEz2iWq"
   },
   "source": [
    "**What information can you get from these plots?**<br>\n",
    "**Try to visualize the differences between the deeper model and the pre-trained xception model!\n",
    "**Are they showing what you expected?**"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "CNN_complete.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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}