cifar_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pckQfL8oZhbN"
   },
   "source": [
    "# CIFAR10 Classification Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eEt4AJqKVj0v"
   },
   "source": [
    "🎯 **Exercise objectives**\n",
    "\n",
    "* 1️⃣ Implement a CNN to solve a **`10-class classification problem`**\n",
    "\n",
    "* 2️⃣ Enhance the CNN's performance with **`Data Augmentation Techniques`**\n",
    "\n",
    "* 3️⃣ Experiment with **`GPUs to Accelerate Image Processing using Google Colab`**\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "👏 You should now have a better feeling of:\n",
    "* how CNNs works, \n",
    "* and especially how convolutions scan images to detect specific spatial features. \n",
    "\n",
    "🚀 It's time to play with images that are a bit more complex than the handwritten digits or the triangles/circles. \n",
    "\n",
    "🎨 From [Wikipedia](https://en.wikipedia.org/wiki/CIFAR-10) (*click on the link for further information*):\n",
    "\n",
    "> The **`CIFAR-10`** dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.\n",
    "\n",
    "<img src=\"https://people.minesparis.psl.eu/fabien.moutarde/ES_MachineLearning/mini-projets/cifar10_notebook_fichiers/cifar_10.png\">\n",
    "\n",
    "⭐️ This dataset is iconic in the research community as many enhancements for image recognition have been achieved on this dataset. After achieving great performance on this dataset, researchers moved to the more advanced CIFAR-100.\n",
    "\n",
    "From the [University of Toronto](https://www.cs.toronto.edu/~kriz/cifar.html):\n",
    "\n",
    "> This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a \"fine\" label (the class to which it belongs) and a \"coarse\" label (the superclass to which it belongs).\n",
    "\n",
    "\n",
    "🔥 In this notebook, let's ***implement a CNN to distinguish the 10 categories from the CIFAR-10 dataset***.\n",
    "\n",
    "❗️ Again, remember that until 10 years ago, this problem was very challenging for the entire research community. As you have been sharpening your CNN skills, it is time to shine!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0rMQW1hVZhbT"
   },
   "source": [
    "## 🛠 Google Colab Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CFWUX30r5wvo"
   },
   "source": [
    "If you are planning to use Google Colab (which is strongly recommended for this challenge), there is a few things you need to take care of first so that you can run everything properly.\n",
    "\n",
    "* If you want to get started, follow these four steps.\n",
    "* You can also read Davy's tutorial on Kitt 👉 [Introduction to Google Colab](https://kitt.lewagon.com/knowledge/tutorials/data_google_colab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i6wVRDoPHetx"
   },
   "source": [
    "### Step 1: Upload the Challenge Folder to Google Drive and Open it in Colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ieSm6iw9HizE"
   },
   "source": [
    "If you want Google Colab to be able to run this notebook, you need to ***upload all the necessary files to your Google Drive***. \n",
    "\n",
    "To do this, simply:\n",
    "1. Access your [Google Drive](https://drive.google.com/)\n",
    "2. Go into the `Colab Notebooks folder`\n",
    "3. Drag-and-drop this `challenge's folder` into it\n",
    "4. Right-click the notebook file and select `Open with` $\\rightarrow$ `Google Colaboratory`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ur5KjRNA4cNR"
   },
   "source": [
    "### Step 2: Mount Google Drive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FxIuwYAoG0TO"
   },
   "source": [
    "The previous was necessary but not enough. For security purposes, Google Colab and Google Drive are NOT connected even if they belong to the same Google Account\n",
    "\n",
    "We need to ***mount the main directory of the Google Drive associated with the account being used for Colab***.\n",
    "\n",
    "*Note: if you're getting errors when authenticating, try to do it in Google Chrome, as other browsers tend to experience issues.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16022,
     "status": "ok",
     "timestamp": 1660229898730,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "_q8uOTzh5vwJ",
    "outputId": "fe44eab3-4992-423d-f420-24341e2ef66e"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tle_V6z6G9Mx"
   },
   "source": [
    "⬅️ On the left sidebar, there is a 🗂 folder icon. \n",
    "* If you click on it, you should now see a folder called `drive/MyDrive`. This is your Google Drive and all its content!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y9tSxSyJIeK3"
   },
   "source": [
    "### Step 3: Navigate to the Challenge Directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A6v8XQ2IIjZp"
   },
   "source": [
    "One more thing: we need to  this notebook in the context of the challenge's folder. \n",
    "\n",
    "Simply run the cell down below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1660229898730,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "7HCO04lsIrcd"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# os.chdir allows you to change directories, like cd in the Terminal\n",
    "os.chdir('/content/drive/MyDrive/Colab Notebooks/data-cifar-classification')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l0_I4eSaJuNy"
   },
   "source": [
    "### Step 4: Toggle GPU Acceleration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x4awrOZ6JxWs"
   },
   "source": [
    "As a last step, we should take advantage of Google Colab by enabling GPU acceleration for our notebook. \n",
    "\n",
    "You can do it by navigating through the menu bar:\n",
    "\n",
    "`Runtime` $\\rightarrow$ `Change runtime type` $\\rightarrow$ `Hardware accelerator`\n",
    "\n",
    "and select \"GPU\" from the dropdown menu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z0MdAwhGJdSR"
   },
   "source": [
    "🚀 You are now ready to start, proceed with the challenge! 🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZY44N5e6T2Il"
   },
   "source": [
    "## (1) Loading the CIFAR10 Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lOWsFV-nZhba"
   },
   "source": [
    "❓ **Question: Loading the CIFAR10 Dataset** ❓\n",
    "\n",
    "\n",
    "* 🎁 We took care of the `data loading and preprocessing` for you. \n",
    "* ▶️ Just run the following cell and make sure you understand the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7846,
     "status": "ok",
     "timestamp": 1660229906573,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "ZkKdhZXWVj00",
    "outputId": "155099db-137d-4777-99c6-05e60ae43859"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) (10000, 32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0: 5000,\n",
       " 1: 5000,\n",
       " 2: 5000,\n",
       " 3: 5000,\n",
       " 4: 5000,\n",
       " 5: 5000,\n",
       " 6: 5000,\n",
       " 7: 5000,\n",
       " 8: 5000,\n",
       " 9: 5000}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import cifar10\n",
    "import numpy as np\n",
    "\n",
    "(images_train, labels_train), (images_test, labels_test) = cifar10.load_data()\n",
    "\n",
    "labels = ['airplane', \n",
    "          'automobile',\n",
    "          'bird',\n",
    "          'cat',\n",
    "          'deer',\n",
    "          'dog',\n",
    "          'frog',\n",
    "          'horse',\n",
    "          'ship',\n",
    "          'truck']\n",
    "\n",
    "print(images_train.shape, images_test.shape)\n",
    "unique, counts = np.unique(labels_train, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "18zIIeggZhbb"
   },
   "source": [
    "### (1.1) Working on a smaller dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZI_3K-MHT2In"
   },
   "source": [
    "❓ **Question about the training size** ❓\n",
    "\n",
    "* It will probably take a very long time to train a model on $50 000$ images m...\n",
    "* 👨🏻‍🏫 **Always start with a subsample to iterate quickly** before scaling up! 🆙\n",
    "* Run the next cell where we are reducing the dataset size by `reduction_factor = 10`. Don't try to increase it unless we ask you to do so..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1660229906573,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "wR_JXHmlT2Io",
    "outputId": "113cc420-7b1c-4c57-c17a-f11b4681a945"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------ Before -----------------\n",
      "(50000, 32, 32, 3) (10000, 32, 32, 3)\n",
      "\n",
      "--- After applying the reduction factor ---\n",
      "(5000, 32, 32, 3) (1000, 32, 32, 3)\n",
      "\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0: 479,\n",
       " 1: 538,\n",
       " 2: 532,\n",
       " 3: 468,\n",
       " 4: 491,\n",
       " 5: 501,\n",
       " 6: 468,\n",
       " 7: 488,\n",
       " 8: 509,\n",
       " 9: 526}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Considering only 1/10th of the 50_000 images\n",
    "reduction_factor = 10\n",
    "\n",
    "# Choosing the random indices of small train set and small test set\n",
    "idx_train =  np.random.choice(len(images_train), round(len(images_train)/reduction_factor))\n",
    "idx_test =  np.random.choice(len(images_test), round(len(images_test)/reduction_factor))\n",
    "\n",
    "# Collecting the two subsamples images_train_small and images_test_small from images_train and images_test\n",
    "images_train_small = images_train[idx_train]\n",
    "images_test_small = images_test[idx_test]\n",
    "# and their corresponding labels\n",
    "labels_train_small = labels_train[idx_train]\n",
    "labels_test_small = labels_test[idx_test]\n",
    "\n",
    "print(\"------------------ Before -----------------\")\n",
    "print(images_train.shape, images_test.shape)\n",
    "\n",
    "print(\"\")\n",
    "\n",
    "print(\"--- After applying the reduction factor ---\")\n",
    "print(images_train_small.shape, images_test_small.shape)\n",
    "\n",
    "print(\"\")\n",
    "print(\"-\"*43)\n",
    "\n",
    "unique, counts = np.unique(labels_train_small, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "79M-HvdyZhbc"
   },
   "source": [
    "👇 You are working with images.. so it would be a good idea to have a look at some of them :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 183
    },
    "executionInfo": {
     "elapsed": 470,
     "status": "ok",
     "timestamp": 1660229907040,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "hQ62jK5VVj03",
    "outputId": "6a8438f8-d7e5-42b9-cd88-fa708e2ac8a4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot few images to see what they look like\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "for i in range(6):\n",
    "    plt.subplot(1,6, i+1)\n",
    "    img = images_train[i]\n",
    "    label = labels_train[i][0]\n",
    "    plt.imshow(img)\n",
    "    plt.title(labels[label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3qlCaB_TZhbc"
   },
   "source": [
    "### (1.2) Image preprocesing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WMG4ttoHVj02"
   },
   "source": [
    "👉 As usual, let's:\n",
    "- normalize the pixels' intensities between 0 and 1\n",
    "- turn the `labels_train` and `labels_test` into \"one-hot-encoded\" targets that we will call respectively `y_train` and `y_test`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 695,
     "status": "ok",
     "timestamp": 1660229907732,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "Fm8XRTbiVj02"
   },
   "outputs": [],
   "source": [
    "### Normalizing pixels' intensities\n",
    "X_train = images_train / 255.\n",
    "X_train_small = images_train_small / 255.\n",
    "X_test = images_test / 255.\n",
    "X_test_small = images_test_small / 255.\n",
    "\n",
    "### Encoding the labels\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "y_train = to_categorical(labels_train, 10)\n",
    "y_train_small = to_categorical(labels_train_small, 10)\n",
    "y_test = to_categorical(labels_test, 10)\n",
    "y_test_small = to_categorical(labels_test_small, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_W9Jbjn6Zhbc"
   },
   "source": [
    "## (2) Iterate on your CNN architecture using your small training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ke9YJk7MVj04"
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "❓ **Question** ❓ Time to shine ⭐️⭐️⭐️ !\n",
    "\n",
    "1. Define the CNN architecture of your choice in an `initialize_model()` function:\n",
    "2. Compile your model in a `compile_model()` method:\n",
    "3. Fit your CNN only on the `small training set` and save the training information in an `history` variable\n",
    "---\n",
    "* Feeling lost?\n",
    "* Do you want to improve your performance ? \n",
    "\n",
    "<details>\n",
    "    <summary>🆘 PRO TIPS 🆘</summary>\n",
    "\n",
    "\n",
    "- Do not forget to add the **`input shape`** of your images to the first layer: it has 3 colors\n",
    "- **`Start simple, add complexify later `** after several attempts to get better results\n",
    "- The task is complex: **`Try at least 3 or 4 convolutions`**, you want your **images to go through different magnifying glasses / kernels from different convolutional layers!**\n",
    "- The Kernel Size does not need to be large for such a small picture resolution!\n",
    "- Add some **`MaxPooling2D`** (but not too much, otherwise the activation \"image\" will become too small)\n",
    "- Keep `padding = \"same\"` and `stride = (1,1)` to start with.\n",
    "- Once your model overfits, try to add some **`Dropout Layers` to regularize the network**. A good tip is to **increase the Dropout Rate/Strength as you move closer to the prediction layer** to prevent your CNN from overfitting\n",
    "- Images are so small in CIFAR10, you can afford to use a larger batch size (32 or 64) to benefit even more from **GPU parallelization**!\n",
    "</details>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1660229907733,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "GNOtE5xhVj04"
   },
   "outputs": [],
   "source": [
    "def initialize_model():\n",
    "    '''instanciate and return the CNN architecture of your choice with less than 150,000 params'''\n",
    "    # YOUR CODE HERE\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1660229907733,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "7C9AL4uBT2Iq"
   },
   "outputs": [],
   "source": [
    "def compile_model(model):\n",
    "    '''return a compiled model suited for the CIFAR-10 task'''\n",
    "    # YOUR CODE HERE\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1660229907733,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "bq3loiPXVj04",
    "tags": [
     "delete_begin"
    ]
   },
   "outputs": [],
   "source": [
    "########################\n",
    "# A POSSIBLE ANSWER    #\n",
    "########################\n",
    "\n",
    "from tensorflow.keras import Sequential, layers\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense\n",
    "\n",
    "def initialize_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(16, (3, 3), activation = 'relu', padding = 'same', input_shape=(32, 32, 3)))\n",
    "    model.add(MaxPooling2D((2, 2)))\n",
    "    \n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(32, (3, 3), activation = 'relu', padding = 'same'))\n",
    "    model.add(MaxPooling2D((2, 2)))\n",
    "    \n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(64, (2, 2), activation = 'relu', padding = 'same'))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(MaxPooling2D((2, 2)))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(100, activation = 'relu'))\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(10, activation = 'softmax'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3013,
     "status": "ok",
     "timestamp": 1660229910743,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "5TEm4HZSVj05",
    "outputId": "738732f6-c5c3-4b2b-fafd-a91db742b636"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 32, 32, 16)        448       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 16)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 16, 16, 16)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 16, 16, 32)        4640      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 8, 8, 32)          0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 8, 8, 64)          8256      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 8, 8, 64)          0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1024)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100)               102500    \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 100)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 116,854\n",
      "Trainable params: 116,854\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-14 17:41:13.538994: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = initialize_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1660229910743,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "rfQMT3y_Vj05"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import optimizers\n",
    "\n",
    "def compile_model(model):\n",
    "    model.compile(loss = 'categorical_crossentropy',\n",
    "                  optimizer = 'adam',\n",
    "                  metrics = ['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 31675,
     "status": "ok",
     "timestamp": 1660229942414,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "czerPhY2XPCs",
    "outputId": "92233eed-ccb8-4ccb-f94a-99d009b5e611",
    "tags": [
     "delete_end"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "55/55 [==============================] - 2s 31ms/step - loss: 2.2895 - accuracy: 0.1223 - val_loss: 2.2474 - val_accuracy: 0.1567\n",
      "Epoch 2/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 2.1498 - accuracy: 0.1980 - val_loss: 2.1264 - val_accuracy: 0.2973\n",
      "Epoch 3/100\n",
      "55/55 [==============================] - 2s 31ms/step - loss: 2.0532 - accuracy: 0.2446 - val_loss: 1.9976 - val_accuracy: 0.3167\n",
      "Epoch 4/100\n",
      "55/55 [==============================] - 2s 30ms/step - loss: 1.8991 - accuracy: 0.3074 - val_loss: 1.9037 - val_accuracy: 0.3633\n",
      "Epoch 5/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.8360 - accuracy: 0.3097 - val_loss: 1.9034 - val_accuracy: 0.3320\n",
      "Epoch 6/100\n",
      "55/55 [==============================] - 2s 30ms/step - loss: 1.7336 - accuracy: 0.3511 - val_loss: 1.7448 - val_accuracy: 0.4187\n",
      "Epoch 7/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.7015 - accuracy: 0.3777 - val_loss: 1.7060 - val_accuracy: 0.4307\n",
      "Epoch 8/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.6667 - accuracy: 0.3731 - val_loss: 1.6587 - val_accuracy: 0.4313\n",
      "Epoch 9/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.6214 - accuracy: 0.3911 - val_loss: 1.6220 - val_accuracy: 0.4553\n",
      "Epoch 10/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.5627 - accuracy: 0.4214 - val_loss: 1.5578 - val_accuracy: 0.4713\n",
      "Epoch 11/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.5579 - accuracy: 0.4203 - val_loss: 1.6356 - val_accuracy: 0.4380\n",
      "Epoch 12/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.5415 - accuracy: 0.4397 - val_loss: 1.5664 - val_accuracy: 0.4593\n",
      "Epoch 13/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.5217 - accuracy: 0.4431 - val_loss: 1.5078 - val_accuracy: 0.4847\n",
      "Epoch 14/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.4588 - accuracy: 0.4711 - val_loss: 1.4882 - val_accuracy: 0.4833\n",
      "Epoch 15/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.4650 - accuracy: 0.4660 - val_loss: 1.4582 - val_accuracy: 0.4953\n",
      "Epoch 16/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.4349 - accuracy: 0.4663 - val_loss: 1.4976 - val_accuracy: 0.4940\n",
      "Epoch 17/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.4110 - accuracy: 0.4877 - val_loss: 1.4311 - val_accuracy: 0.4947\n",
      "Epoch 18/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.3731 - accuracy: 0.4917 - val_loss: 1.4272 - val_accuracy: 0.4873\n",
      "Epoch 19/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.3858 - accuracy: 0.4931 - val_loss: 1.4031 - val_accuracy: 0.5187\n",
      "Epoch 20/100\n",
      "55/55 [==============================] - 2s 28ms/step - loss: 1.3572 - accuracy: 0.5011 - val_loss: 1.3798 - val_accuracy: 0.5160\n",
      "Epoch 21/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.3353 - accuracy: 0.5154 - val_loss: 1.4034 - val_accuracy: 0.5000\n",
      "Epoch 22/100\n",
      "55/55 [==============================] - 2s 30ms/step - loss: 1.3359 - accuracy: 0.5031 - val_loss: 1.3888 - val_accuracy: 0.5267\n",
      "Epoch 23/100\n",
      "55/55 [==============================] - 2s 30ms/step - loss: 1.3038 - accuracy: 0.5191 - val_loss: 1.3556 - val_accuracy: 0.5313\n",
      "Epoch 24/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.2876 - accuracy: 0.5329 - val_loss: 1.3539 - val_accuracy: 0.5307\n",
      "Epoch 25/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.2924 - accuracy: 0.5300 - val_loss: 1.3835 - val_accuracy: 0.5147\n",
      "Epoch 26/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.2757 - accuracy: 0.5303 - val_loss: 1.3507 - val_accuracy: 0.5287\n",
      "Epoch 27/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.2543 - accuracy: 0.5377 - val_loss: 1.3498 - val_accuracy: 0.5380\n",
      "Epoch 28/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.2654 - accuracy: 0.5357 - val_loss: 1.3245 - val_accuracy: 0.5300\n",
      "Epoch 29/100\n",
      "55/55 [==============================] - 2s 30ms/step - loss: 1.2470 - accuracy: 0.5509 - val_loss: 1.3065 - val_accuracy: 0.5333\n",
      "Epoch 30/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.2076 - accuracy: 0.5511 - val_loss: 1.2809 - val_accuracy: 0.5460\n",
      "Epoch 31/100\n",
      "55/55 [==============================] - 2s 30ms/step - loss: 1.2022 - accuracy: 0.5529 - val_loss: 1.2861 - val_accuracy: 0.5427\n",
      "Epoch 32/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.1943 - accuracy: 0.5634 - val_loss: 1.3077 - val_accuracy: 0.5473\n",
      "Epoch 33/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.1980 - accuracy: 0.5646 - val_loss: 1.3187 - val_accuracy: 0.5400\n",
      "Epoch 34/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.1772 - accuracy: 0.5706 - val_loss: 1.3003 - val_accuracy: 0.5273\n",
      "Epoch 35/100\n",
      "55/55 [==============================] - 2s 29ms/step - loss: 1.1748 - accuracy: 0.5714 - val_loss: 1.2990 - val_accuracy: 0.5393\n",
      "Epoch 00035: early stopping\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "model_small = initialize_model()\n",
    "model_small = compile_model(model)\n",
    "\n",
    "es = EarlyStopping(patience = 5, verbose = 2)\n",
    "\n",
    "history_small = model_small.fit(X_train_small, y_train_small, \n",
    "                    validation_split = 0.3,\n",
    "                    callbacks = [es], \n",
    "                    epochs = 100, \n",
    "                    batch_size = 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YcPQtVjdVj06"
   },
   "source": [
    "❓ **Question: History of your training** ❓ \n",
    "\n",
    "Run the following function on the previous history \n",
    "_(keep the default arguments, these are intended for future plots in the notebook)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 10196,
     "status": "ok",
     "timestamp": 1660229952607,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "lIR_L7UfVj06"
   },
   "outputs": [],
   "source": [
    "def plot_history(history, title='', axs=None, exp_name=\"\"):\n",
    "    if axs is not None:\n",
    "        ax1, ax2 = axs\n",
    "    else:\n",
    "        f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "    \n",
    "    if len(exp_name) > 0 and exp_name[0] != '_':\n",
    "        exp_name = '_' + exp_name\n",
    "    ax1.plot(history.history['loss'], label = 'train' + exp_name)\n",
    "    ax1.plot(history.history['val_loss'], label = 'val' + exp_name)\n",
    "    ax1.set_ylim(0., 2.2)\n",
    "    ax1.set_title('loss')\n",
    "    ax1.legend()\n",
    "\n",
    "    ax2.plot(history.history['accuracy'], label='train accuracy'  + exp_name)\n",
    "    ax2.plot(history.history['val_accuracy'], label='val accuracy'  + exp_name)\n",
    "    ax2.set_ylim(0.25, 1.)\n",
    "    ax2.set_title('Accuracy')\n",
    "    ax2.legend()\n",
    "    return (ax1, ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "executionInfo": {
     "elapsed": 443,
     "status": "ok",
     "timestamp": 1660229953048,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "bDxbLDt2Vj07",
    "outputId": "a213b9e1-eed3-45de-8840-00c9800055ac",
    "tags": [
     "challengify"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_small)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OR7l9LxMVj07"
   },
   "source": [
    "❓ **Question: Evaluating your CNN** ❓ \n",
    "\n",
    "* Evaluate your model on the test data and compare it with a baseline accuracy. \n",
    "* Are you satisfied with this performance?\n",
    "* Look at the `PRO TIPS` above and iterate a bit if you want to improve your performance!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 209,
     "status": "ok",
     "timestamp": 1660229953254,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "WwnwvmV5Vj08",
    "outputId": "5a60d70d-3e07-4e60-820a-36470256e388",
    "tags": [
     "challengify"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 52.0% compared to a chance level of 10.0%\n"
     ]
    }
   ],
   "source": [
    "res = model_small.evaluate(X_test_small, y_test_small, verbose = 0)\n",
    "\n",
    "print(f'The accuracy is {res[1]*100:.1f}% compared to a chance level of {1./len(labels)*100}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z7P9y-LwVjdx"
   },
   "source": [
    "## (3) Increase the size of your training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UMHH_qsbVT_0"
   },
   "source": [
    "❓ **Question: train your model on the full dataset** ❓ \n",
    "\n",
    "- Switch to **Colab** if you haven't done it before\n",
    "- Make sure to use the **GPU acceleration** by clicking on `Runtime` $\\rightarrow$ `Change runtime` $\\rightarrow$ `GPU`\n",
    "\n",
    "💡 Training neural networks on images (in each batch) can be parallelized, and this **`parallelization procedure`** can be done on **`GPU`**.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 936
    },
    "executionInfo": {
     "elapsed": 45648,
     "status": "error",
     "timestamp": 1660229998900,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "EJxVfUfhVj06",
    "outputId": "c7e010ff-4b12-4203-febb-e961003ec178",
    "tags": [
     "delete_begin"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "547/547 [==============================] - 16s 28ms/step - loss: 1.9245 - accuracy: 0.2822 - val_loss: 1.6691 - val_accuracy: 0.4309\n",
      "Epoch 2/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.6113 - accuracy: 0.4069 - val_loss: 1.4999 - val_accuracy: 0.4780\n",
      "Epoch 3/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 1.4947 - accuracy: 0.4537 - val_loss: 1.4175 - val_accuracy: 0.5041\n",
      "Epoch 4/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.4175 - accuracy: 0.4824 - val_loss: 1.3117 - val_accuracy: 0.5474\n",
      "Epoch 5/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.3583 - accuracy: 0.5088 - val_loss: 1.2504 - val_accuracy: 0.5745\n",
      "Epoch 6/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.3110 - accuracy: 0.5262 - val_loss: 1.1925 - val_accuracy: 0.5853\n",
      "Epoch 7/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.2754 - accuracy: 0.5412 - val_loss: 1.1751 - val_accuracy: 0.5977\n",
      "Epoch 8/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.2452 - accuracy: 0.5525 - val_loss: 1.1284 - val_accuracy: 0.6165\n",
      "Epoch 9/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.2139 - accuracy: 0.5653 - val_loss: 1.0983 - val_accuracy: 0.6263\n",
      "Epoch 10/100\n",
      "547/547 [==============================] - 16s 28ms/step - loss: 1.1780 - accuracy: 0.5773 - val_loss: 1.0608 - val_accuracy: 0.6353\n",
      "Epoch 11/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.1676 - accuracy: 0.5847 - val_loss: 1.0167 - val_accuracy: 0.6533\n",
      "Epoch 12/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.1431 - accuracy: 0.5930 - val_loss: 1.0557 - val_accuracy: 0.6441\n",
      "Epoch 13/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 1.1260 - accuracy: 0.6001 - val_loss: 1.0331 - val_accuracy: 0.6607\n",
      "Epoch 14/100\n",
      "547/547 [==============================] - 22s 40ms/step - loss: 1.1057 - accuracy: 0.6059 - val_loss: 1.0057 - val_accuracy: 0.6631\n",
      "Epoch 15/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 1.0939 - accuracy: 0.6092 - val_loss: 1.0126 - val_accuracy: 0.6519\n",
      "Epoch 16/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 1.0734 - accuracy: 0.6182 - val_loss: 0.9727 - val_accuracy: 0.6690\n",
      "Epoch 17/100\n",
      "547/547 [==============================] - 17s 31ms/step - loss: 1.0606 - accuracy: 0.6264 - val_loss: 0.9471 - val_accuracy: 0.6809\n",
      "Epoch 18/100\n",
      "547/547 [==============================] - 16s 28ms/step - loss: 1.0552 - accuracy: 0.6256 - val_loss: 0.9752 - val_accuracy: 0.6719\n",
      "Epoch 19/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 1.0427 - accuracy: 0.6322 - val_loss: 0.9600 - val_accuracy: 0.6717\n",
      "Epoch 20/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 1.0251 - accuracy: 0.6380 - val_loss: 0.9350 - val_accuracy: 0.6759\n",
      "Epoch 21/100\n",
      "547/547 [==============================] - 14s 26ms/step - loss: 1.0261 - accuracy: 0.6398 - val_loss: 0.8992 - val_accuracy: 0.6946\n",
      "Epoch 22/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 1.0167 - accuracy: 0.6411 - val_loss: 0.9027 - val_accuracy: 0.6924\n",
      "Epoch 23/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 1.0069 - accuracy: 0.6428 - val_loss: 0.8925 - val_accuracy: 0.6976\n",
      "Epoch 24/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 0.9946 - accuracy: 0.6480 - val_loss: 0.8928 - val_accuracy: 0.6962\n",
      "Epoch 25/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 0.9846 - accuracy: 0.6535 - val_loss: 0.8749 - val_accuracy: 0.7025\n",
      "Epoch 26/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.9779 - accuracy: 0.6526 - val_loss: 0.8787 - val_accuracy: 0.7026\n",
      "Epoch 27/100\n",
      "547/547 [==============================] - 16s 28ms/step - loss: 0.9737 - accuracy: 0.6591 - val_loss: 0.8866 - val_accuracy: 0.6956\n",
      "Epoch 28/100\n",
      "547/547 [==============================] - 16s 28ms/step - loss: 0.9683 - accuracy: 0.6549 - val_loss: 0.8689 - val_accuracy: 0.7039\n",
      "Epoch 29/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.9662 - accuracy: 0.6598 - val_loss: 0.8893 - val_accuracy: 0.6914\n",
      "Epoch 30/100\n",
      "547/547 [==============================] - 19s 35ms/step - loss: 0.9623 - accuracy: 0.6619 - val_loss: 0.8935 - val_accuracy: 0.6907\n",
      "Epoch 31/100\n",
      "547/547 [==============================] - 19s 35ms/step - loss: 0.9562 - accuracy: 0.6630 - val_loss: 0.8564 - val_accuracy: 0.7067\n",
      "Epoch 32/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 0.9481 - accuracy: 0.6664 - val_loss: 0.8325 - val_accuracy: 0.7157\n",
      "Epoch 33/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.9435 - accuracy: 0.6690 - val_loss: 0.8343 - val_accuracy: 0.7171\n",
      "Epoch 34/100\n",
      "547/547 [==============================] - 17s 31ms/step - loss: 0.9422 - accuracy: 0.6664 - val_loss: 0.8317 - val_accuracy: 0.7200\n",
      "Epoch 35/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.9274 - accuracy: 0.6731 - val_loss: 0.8382 - val_accuracy: 0.7115\n",
      "Epoch 36/100\n",
      "547/547 [==============================] - 16s 28ms/step - loss: 0.9295 - accuracy: 0.6715 - val_loss: 0.8464 - val_accuracy: 0.7095\n",
      "Epoch 37/100\n",
      "547/547 [==============================] - 14s 26ms/step - loss: 0.9251 - accuracy: 0.6755 - val_loss: 0.8337 - val_accuracy: 0.7129\n",
      "Epoch 38/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.9233 - accuracy: 0.6719 - val_loss: 0.8509 - val_accuracy: 0.7114\n",
      "Epoch 39/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.9141 - accuracy: 0.6774 - val_loss: 0.8292 - val_accuracy: 0.7139\n",
      "Epoch 40/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.9163 - accuracy: 0.6745 - val_loss: 0.8193 - val_accuracy: 0.7218\n",
      "Epoch 41/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.9087 - accuracy: 0.6798 - val_loss: 0.8155 - val_accuracy: 0.7230\n",
      "Epoch 42/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.9047 - accuracy: 0.6843 - val_loss: 0.8149 - val_accuracy: 0.7211\n",
      "Epoch 43/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.8905 - accuracy: 0.6851 - val_loss: 0.8082 - val_accuracy: 0.7221\n",
      "Epoch 44/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.9004 - accuracy: 0.6820 - val_loss: 0.8439 - val_accuracy: 0.7079\n",
      "Epoch 45/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.8946 - accuracy: 0.6822 - val_loss: 0.8077 - val_accuracy: 0.7237\n",
      "Epoch 46/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.8875 - accuracy: 0.6870 - val_loss: 0.8426 - val_accuracy: 0.7101\n",
      "Epoch 47/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 0.8901 - accuracy: 0.6855 - val_loss: 0.8130 - val_accuracy: 0.7193\n",
      "Epoch 48/100\n",
      "547/547 [==============================] - 17s 32ms/step - loss: 0.8840 - accuracy: 0.6871 - val_loss: 0.7951 - val_accuracy: 0.7305\n",
      "Epoch 49/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 0.8799 - accuracy: 0.6882 - val_loss: 0.7996 - val_accuracy: 0.7326\n",
      "Epoch 50/100\n",
      "547/547 [==============================] - 15s 28ms/step - loss: 0.8717 - accuracy: 0.6918 - val_loss: 0.8216 - val_accuracy: 0.7164\n",
      "Epoch 51/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.8753 - accuracy: 0.6900 - val_loss: 0.7957 - val_accuracy: 0.7301\n",
      "Epoch 52/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.8740 - accuracy: 0.6897 - val_loss: 0.7868 - val_accuracy: 0.7345\n",
      "Epoch 53/100\n",
      "547/547 [==============================] - 15s 27ms/step - loss: 0.8696 - accuracy: 0.6929 - val_loss: 0.7811 - val_accuracy: 0.7324\n",
      "Epoch 54/100\n",
      "547/547 [==============================] - 16s 30ms/step - loss: 0.8709 - accuracy: 0.6921 - val_loss: 0.8107 - val_accuracy: 0.7214\n",
      "Epoch 55/100\n",
      "547/547 [==============================] - 20s 36ms/step - loss: 0.8670 - accuracy: 0.6919 - val_loss: 0.7819 - val_accuracy: 0.7369\n",
      "Epoch 56/100\n",
      "547/547 [==============================] - 18s 32ms/step - loss: 0.8632 - accuracy: 0.6967 - val_loss: 0.8347 - val_accuracy: 0.7127\n",
      "Epoch 57/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.8706 - accuracy: 0.6914 - val_loss: 0.7812 - val_accuracy: 0.7356\n",
      "Epoch 58/100\n",
      "547/547 [==============================] - 16s 29ms/step - loss: 0.8595 - accuracy: 0.6955 - val_loss: 0.8144 - val_accuracy: 0.7187\n",
      "CPU times: user 46min 44s, sys: 23min 2s, total: 1h 9min 47s\n",
      "Wall time: 15min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "model = initialize_model()\n",
    "model = compile_model(model)\n",
    "\n",
    "es = EarlyStopping(patience = 5)\n",
    "\n",
    "history = model.fit(X_train, y_train, \n",
    "                    validation_split = 0.3,\n",
    "                    callbacks = [es], \n",
    "                    epochs = 100, \n",
    "                    batch_size = 64,\n",
    "                   verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "executionInfo": {
     "elapsed": 202,
     "status": "error",
     "timestamp": 1660230013394,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "DPR4iFdna6Gt",
    "outputId": "6f8e6f00-2967-431f-f619-d0684c579893"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 231,
     "status": "aborted",
     "timestamp": 1660229998897,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "jFeihjy_WqZ5",
    "tags": [
     "delete_end"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 72.050% compared to a chance level of 10.0%\n"
     ]
    }
   ],
   "source": [
    "res = model.evaluate(X_test, y_test, verbose = 0)\n",
    "\n",
    "print(f'The accuracy is {res[1]*100:.3f}% compared to a chance level of {1./len(labels)*100}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BFMcHMwfZhbh"
   },
   "source": [
    "You should observe significant performance improvement\n",
    "\n",
    "Welcome to the Deep Learning paradigm, where big data makes a significant difference.\n",
    "\n",
    "But what happens if I can access only a limited amount of pictures? Think about biologists studying rare species. What can they do?\n",
    "* To improve the accuracy of a model without much work, we can **generate new data**. \n",
    "* The process is called... ⭐️ **`Data Augmentation`** ⭐️ ! \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RIBg-oEkZhbh"
   },
   "source": [
    "## (4) 🎁📚 Data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ℹ️ This section contains questions and the answers are also provided. Read them carefully before moving on to the `Transfer Learning` challenge.\n",
    "\n",
    "> 👨🏻‍🏫 If you do not have time to do the `Transfer Learning` challenge, don't worry, we can talk about it during the Recap session, we are aware that this unit about Convolutional Neural Networks is quite packed... but also one of the most exciting applications of Deep Learning ❤️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HmPinqUzZhbh"
   },
   "source": [
    "* 👩🏻‍🏫 <b><u>Data Augmentation</u></b>\n",
    "    * This technique is widely-used and consists of applying little transformations to the input images without changing their labels: ***mirroring***, ***cropping***, ***intensity changes***, etc... \n",
    "    * The _improved performance_ simply results from the CNN training with more images (the original pictures + the \"augmented\" ones).\n",
    "    \n",
    "\n",
    "* 👉 <b><u>Theoretically:</u></b>\n",
    "    * (1) We could generate these new images by applying some transformations on copies of the original pictures\n",
    "    * (2) Train the model on the original images + new images. \n",
    "    \n",
    "    \n",
    "* 🚨  <b><u>Problem:</u></b>\n",
    "    * Such a procedure requires storing all these images in memory...\n",
    "    * It can be very intensive, so much so that your computer's RAM cannot hold them all\n",
    "    \n",
    "    \n",
    "* 🦄 <b><u>In Practice:</u></b>\n",
    "    * When a Neural Network operates a forward/backward propagation, it requires to see only 16 pictures at a time if you chose $ batch size = 16 $ for example. It doesn't need to store all the original images or the augmented images in the RAM.\n",
    "    * For this reason, we will **augment the data on the fly (batch per batch)**. What does that mean? For every epoch and every batch, during the ***.fit()*** training procedure, we will:\n",
    "        1. Generate some `augmented data/images`\n",
    "        2. Fit the model on the images and their augmented versions\n",
    "        3. Delete the images and their augmented versions from the RAM\n",
    "        4. Repeat steps 1-2-3\n",
    "        \n",
    "* 📚 <a href= \"https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator\"><b><u>tf/keras/preprocessing/image/ImageDataGenerator</u></b></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wk8ZZVunVj08"
   },
   "source": [
    "❓ **Question: using an ImageDataGenerator** ❓\n",
    "\n",
    "Look at the following code down below 👇\n",
    "* The general syntax may look strange but don't worry: \n",
    "    * First, focus on the arguments of the *ImageDataGenerator* which define the augmentation techniques that we are using\n",
    "    * Then, check the 📚 <a href=\"https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator\">**`ImageDataGenerator`**</a> documentation later\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 232,
     "status": "aborted",
     "timestamp": 1660229998898,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "DlOpbos5Vj09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.ImageDataGenerator at 0x1688ec4f0>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "datagen = ImageDataGenerator(\n",
    "    featurewise_center = False,\n",
    "    featurewise_std_normalization = False,\n",
    "    rotation_range = 10,\n",
    "    width_shift_range = 0.1,\n",
    "    height_shift_range = 0.1,\n",
    "    horizontal_flip = True,\n",
    "    zoom_range = (0.8, 1.2),\n",
    "    ) \n",
    "\n",
    "datagen.fit(X_train)\n",
    "datagen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 231,
     "status": "aborted",
     "timestamp": 1660229998898,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "Zcl17dW1aA0G"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.NumpyArrayIterator at 0x16893b790>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_augmented_iterator = datagen.flow(X_train, shuffle=False, batch_size=1)\n",
    "X_augmented_iterator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RdMqg8grVj09"
   },
   "source": [
    "❗️ Always **visualize the augmented images** in order to double-check whether you can still recognize the labels yourself or not ❗️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 231,
     "status": "aborted",
     "timestamp": 1660229998898,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "IsqMF0adVj09"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "for i, (raw_image, augmented_image) in enumerate(zip(X_train, X_augmented_iterator)):\n",
    "    _, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 2))\n",
    "    ax1.imshow(raw_image)\n",
    "    ax2.imshow(augmented_image[0])\n",
    "    plt.show()\n",
    "    \n",
    "    if i > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LyLlJQrKVj09"
   },
   "source": [
    "❗ **Remarks** ❗ \n",
    "\n",
    "* Each image from **`X_augmented_iterator`** is an ***augmented image*** of one image located in the original `X_train` image dataset\n",
    "* This augmentation process is done once per epoch.\n",
    "* During one epoch, the model will:\n",
    "    1. *create the augmented version* of each picture from `X_train`, \n",
    "    2. for each image of `X_train`, *the model will randomly pick either the original version in `X_train` or its augmented version in `X_augmented_iterator`*\n",
    "    3. and the model will be *fitted on the combination of some original images + some augmented images*\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZCKCFU-KZhbj"
   },
   "source": [
    "❓ **Question: what is the validation set when we have augmented images** ❓ \n",
    "\n",
    "* Previously, we used the `validation_split` argument to let the model separate the training set into a Train/Validation split when fitting the model for each epoch.\n",
    "* It is not possible to use this kind of Train/Val Split here as **using an image in the training set and its transformation in the validation set is considered `data leakage`** !. \n",
    "* Therefore, we have to define the **`validation_data`** manually with the following commands: take time to understand the cell down below:👇 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 232,
     "status": "aborted",
     "timestamp": 1660229998899,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "lYOkY7LOVj09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "625/625 [==============================] - 42s 66ms/step - loss: 1.8821 - accuracy: 0.3052 - val_loss: 1.6503 - val_accuracy: 0.4134\n",
      "Epoch 2/50\n",
      "625/625 [==============================] - 24s 39ms/step - loss: 1.6448 - accuracy: 0.3989 - val_loss: 1.4813 - val_accuracy: 0.4857\n",
      "Epoch 3/50\n",
      "625/625 [==============================] - 25s 41ms/step - loss: 1.5547 - accuracy: 0.4311 - val_loss: 1.3779 - val_accuracy: 0.5217\n",
      "Epoch 4/50\n",
      "625/625 [==============================] - 25s 41ms/step - loss: 1.4879 - accuracy: 0.4593 - val_loss: 1.3675 - val_accuracy: 0.5003\n",
      "Epoch 5/50\n",
      "625/625 [==============================] - 25s 40ms/step - loss: 1.4370 - accuracy: 0.4819 - val_loss: 1.2560 - val_accuracy: 0.5686\n",
      "Epoch 6/50\n",
      "625/625 [==============================] - 25s 40ms/step - loss: 1.4031 - accuracy: 0.4936 - val_loss: 1.2045 - val_accuracy: 0.5905\n",
      "Epoch 7/50\n",
      "625/625 [==============================] - 24s 38ms/step - loss: 1.3705 - accuracy: 0.5077 - val_loss: 1.1659 - val_accuracy: 0.5996\n",
      "Epoch 8/50\n",
      "625/625 [==============================] - 24s 39ms/step - loss: 1.3499 - accuracy: 0.5166 - val_loss: 1.1482 - val_accuracy: 0.5940\n",
      "Epoch 9/50\n",
      "625/625 [==============================] - 24s 39ms/step - loss: 1.3158 - accuracy: 0.5307 - val_loss: 1.1237 - val_accuracy: 0.6030\n",
      "Epoch 10/50\n",
      "625/625 [==============================] - 26s 42ms/step - loss: 1.2965 - accuracy: 0.5355 - val_loss: 1.1318 - val_accuracy: 0.6026\n",
      "Epoch 11/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.2785 - accuracy: 0.5431 - val_loss: 1.1008 - val_accuracy: 0.6046\n",
      "Epoch 12/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.2674 - accuracy: 0.5514 - val_loss: 1.0649 - val_accuracy: 0.6283\n",
      "Epoch 13/50\n",
      "625/625 [==============================] - 24s 38ms/step - loss: 1.2566 - accuracy: 0.5491 - val_loss: 1.0381 - val_accuracy: 0.6388\n",
      "Epoch 14/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.2399 - accuracy: 0.5593 - val_loss: 1.0096 - val_accuracy: 0.6473\n",
      "Epoch 15/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.2282 - accuracy: 0.5639 - val_loss: 1.0039 - val_accuracy: 0.6596\n",
      "Epoch 16/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.2186 - accuracy: 0.5705 - val_loss: 0.9892 - val_accuracy: 0.6570\n",
      "Epoch 17/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.2086 - accuracy: 0.5717 - val_loss: 1.0310 - val_accuracy: 0.6312\n",
      "Epoch 18/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.1939 - accuracy: 0.5758 - val_loss: 0.9863 - val_accuracy: 0.6567\n",
      "Epoch 19/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.1869 - accuracy: 0.5799 - val_loss: 0.9694 - val_accuracy: 0.6639\n",
      "Epoch 20/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.1804 - accuracy: 0.5777 - val_loss: 1.0075 - val_accuracy: 0.6405\n",
      "Epoch 21/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.1799 - accuracy: 0.5821 - val_loss: 0.9326 - val_accuracy: 0.6789\n",
      "Epoch 22/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.1658 - accuracy: 0.5875 - val_loss: 0.9843 - val_accuracy: 0.6543\n",
      "Epoch 23/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.1519 - accuracy: 0.5933 - val_loss: 0.9208 - val_accuracy: 0.6822\n",
      "Epoch 24/50\n",
      "625/625 [==============================] - 23s 37ms/step - loss: 1.1562 - accuracy: 0.5875 - val_loss: 0.9662 - val_accuracy: 0.6550\n",
      "Epoch 25/50\n",
      "625/625 [==============================] - 24s 38ms/step - loss: 1.1513 - accuracy: 0.5901 - val_loss: 0.9710 - val_accuracy: 0.6546\n",
      "Epoch 26/50\n",
      "625/625 [==============================] - 24s 38ms/step - loss: 1.1381 - accuracy: 0.6004 - val_loss: 0.9282 - val_accuracy: 0.6718\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# The model\n",
    "model_aug = initialize_model()\n",
    "model_aug = compile_model(model_aug)\n",
    "\n",
    "# The data generator\n",
    "X_tr = X_train[:40000]\n",
    "y_tr = y_train[:40000]\n",
    "X_val = X_train[40000:]\n",
    "y_val = y_train[40000:]\n",
    "train_flow = datagen.flow(X_tr, y_tr, batch_size = 64)\n",
    "\n",
    "# The early stopping criterion\n",
    "es = EarlyStopping(patience = 3)\n",
    "\n",
    "# The fit\n",
    "history_aug = model_aug.fit(train_flow, \n",
    "                        epochs = 50, \n",
    "                        callbacks = [es], \n",
    "                        validation_data = (X_val, y_val))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NmQ9t_cMVj0-"
   },
   "source": [
    "🚨 The training can be quite long here...\n",
    "\n",
    "👉 Feel free to move on to the next exercise and come back to this notebook later to finish the last questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NSLb7gWRZhbj"
   },
   "source": [
    "❓ **Question: How did the model with an augmented dataset perform?** ❓ \n",
    "\n",
    "Let's plot the previous and current run histories. What do you think of the data augmentation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 232,
     "status": "aborted",
     "timestamp": 1660229998899,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "o9r4Fau2Vj0-",
    "tags": [
     "challengify"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = plot_history(history_aug, exp_name = 'data_augmentation')\n",
    "plot_history(history ,axs = axs, exp_name='baseline')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 232,
     "status": "aborted",
     "timestamp": 1660229998899,
     "user": {
      "displayName": "Bruno Castro Brunckhorst",
      "userId": "00660085536154297213"
     },
     "user_tz": -120
    },
    "id": "IMu70iq7Vj0-",
    "tags": [
     "delete"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.9372 - accuracy: 0.6715\n",
      "Accuracy without data augmentation 72.05%\n",
      "Accuracy with data augmentation 67.15%\n"
     ]
    }
   ],
   "source": [
    "res_1 = model.evaluate(X_test, y_test, verbose = 0)\n",
    "\n",
    "res_2 = model_aug.evaluate(X_test, y_test, verbose = 1)\n",
    "\n",
    "print(f'Accuracy without data augmentation {res_1[1]*100:.2f}%')\n",
    "print(f'Accuracy with data augmentation {res_2[1]*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jm055RyNVj0-"
   },
   "source": [
    "🥡 <b><u>Some takeaways from Data Augmentation:</u></b>\n",
    "\n",
    "* Data augmentation may not improve your performance easily...\n",
    "\n",
    "* Here it even decreased the performance!\n",
    "\n",
    "* Its impact strongly depends on: \n",
    "    * the model architecture you used\n",
    "    * the learning rate, \n",
    "    * the type of augmentation chosen, etc...\n",
    "\n",
    "* Image classification is an art that requires months and years of practice to master!\n",
    "\n",
    "🚨 **Don't spend too much time trying to finetune your model for the moment!  You have other interesting challenges to investigate!** 🚨\n",
    "\n",
    "📚 [Here is a good example of a solution for future reference](https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/).<br>\n",
    "They managed to reach an accuracy level of approx. 80%!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oy4DCYLGVj0-"
   },
   "source": [
    "---\n",
    "\n",
    "🏁 **Congratulations** 🏁 \n",
    "\n",
    "1. Download this notebook from your `Google Drive` or directly from `Google Colab` \n",
    "2. Drag-and-drop it from your `Downloads` folder to your local challenge folder  \n",
    "\n",
    "\n",
    "💾 Don't forget to push your code\n",
    "\n",
    "3. Follow the usual procedure on your terminal, inside the challenge folder:\n",
    "      * *git add cifar_classification.ipynb*\n",
    "      * *git commit -m \"I am the god of CNNs\"*\n",
    "      * *git push origin master*\n",
    "\n",
    "*Hint*: To find where this Colab notebook has been saved, click on `File` $\\rightarrow$ `Locate in Drive`.\n",
    "\n",
    "🚀 It is time to move on to the **Transfer Learning** challenge!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}