Image Recognition using the German Traffic Sign Recognition Benchmark (GTSRB).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The German Traffic Sign Recognition Benchmark **(GTSRB)** dataset offers more than `50,000` images arranged in `42` classes (traffic\n",
    "signs), which allows you to create a **multiclass** classification problem. In a multiclass\n",
    "classification problem, you state the probability of the image’s being part of\n",
    "a class and take the highest probability as the correct answer. For instance, an\n",
    "“Attention: Construction Site” sign will cause the classification algorithm to generate\n",
    "high probabilities for all attention signs. (The highest probability should\n",
    "match its class.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import required packages and modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import zipfile\n",
    "import pprint\n",
    "from skimage.transform import resize\n",
    "from skimage.io import imread\n",
    "import urllib.request\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-beta1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the GTSRB Dataset from source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data can be optained from the authors using the url below. If you face problems with your firewall and if the network is slow you can download the dataset manually from the same site. There is also an alternative site provided below or you can simply search using google."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#url = \"http://benchmark.ini.rub.de/Dataset/GTSRB_Final_Training_Images.zip\"\n",
    "#file_dir = \"./data/GTSRB_Final_Training_Images.zip\"  \n",
    "#urllib.request.urlretrieve(url, file_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternative source of the dataset https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"./data/GTSRB_Final_Training_Images.zip\"\n",
    "archive = zipfile.ZipFile(filename, 'r')\n",
    "file_paths = [file for file in archive.namelist() if '.ppm' in file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 32\n",
    "TEST_SIZE = 0.2\n",
    "X_train, X_test, y_train, y_test = list(), list(), list(), list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for filename in file_paths:\n",
    "    img = imread(archive.open(filename))\n",
    "    img = resize(img, output_shape = (IMG_SIZE, IMG_SIZE), mode = 'reflect')\n",
    "    img_class = int(filename.split('/')[-2])\n",
    "    if (hash(filename) % 1000) / 1000 > TEST_SIZE:\n",
    "        X_train.append(img)\n",
    "        y_train.append(img_class)\n",
    "    else:\n",
    "        X_test.append(img)\n",
    "        y_test.append(img_class)\n",
    "archive.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size:31488 \n",
      "Test size:7721 (0.197)\n"
     ]
    }
   ],
   "source": [
    "print('X_train shape:', X_train.shape)\n",
    "print('X_val shape:', X_test.shape)\n",
    "test_ratio = len(X_test) / len(file_paths)\n",
    "print(\"Train size:{} \\nTest size:{} ({:0.3f})\".format(len(X_train), len(X_test), test_ratio))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The train size is more than `30,000` images, and the test almost is `8,000` (`20%`\n",
    "of the total)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neural networks\n",
    "can learn multiclass problems better when the classes are **balanced** or\n",
    "they tend to concentrate their attention on learning just the more populated\n",
    "classes. The following code checks the distribution of classes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No classes: 43\n"
     ]
    },
    {
     "data": {
      "image/png": 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fkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdeT/ABLj6a6lHjZFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes, dist = np.unique(y_train + y_test, return_counts = True)\n",
    "NUM_CLASSES = len(classes)\n",
    "print(\"No classes: {}\".format(NUM_CLASSES))\n",
    "plt.bar(classes, dist, align = 'center', alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows that the classes aren’t balanced. Some traffic signs appear\n",
    "more frequently than others do which is normal (for instance, while driving, stop signs are\n",
    "encountered more frequently than a deer crossing sign). As a solution, we compute a weight, which is a ratio based on frequencies of\n",
    "classes that the neural network uses to increase the signal it receives from rarer\n",
    "examples (signs) and to dump the more frequent ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weight = {c:dist[c] / np.sum(dist) for c in classes}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the model and Running the classification task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will define the image generator that will retrieve the images in batches (samples of a predefined size) for training\n",
    "and validation, normalizes their values, and applies **augmentation** to fight\n",
    "**overfitting** by slightly shifting and rotating the images. Notice that **augmentation** applies only on the training image generator and not the validation\n",
    "generator, because it’s necessary to test using the original images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the Image Data Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "train_datagen = keras.preprocessing.image.ImageDataGenerator(rescale = 1./255, \n",
    "                                                    rotation_range = 5, \n",
    "                                                    width_shift_range = 0.10, \n",
    "                                                    height_shift_range = 0.10)\n",
    "\n",
    "train_generator = train_datagen.flow(np.array(X_train), keras.utils.to_categorical(y_train), batch_size = batch_size)\n",
    "val_datagen = keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "val_generator = val_datagen.flow(np.array(X_test), keras.utils.to_categorical(y_test), batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The neural network consists of **two convolution layers**, one with `32 channels`, the other\n",
    "with `64`, both working with a kernel of size `(5, 5)`. The convolution-layers are followed\n",
    "by a **dense layer of 768 neurons**. **Dropout** (dropping `40 percent` of the neurons) **regularizes**\n",
    "this last layer and **softmax activates** it (thus the sum of the output probabilities\n",
    "of all classes will sum to `100` percent)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def small_cnn():\n",
    "    model = keras.models.Sequential()\n",
    "    model.add(keras.layers.Conv2D(32, (5, 5), padding='same', input_shape=(IMG_SIZE, IMG_SIZE, 3), activation='relu', name = 'Conv2D_layer_1'))\n",
    "    model.add(keras.layers.Conv2D(64, (5, 5), activation='relu', name = 'Conv2D_layer_2'))\n",
    "    model.add(keras.layers.Flatten(name = 'Flatten'))\n",
    "    model.add(keras.layers.Dense(768, activation='relu', name = 'Dense_layer_1'))\n",
    "    model.add(keras.layers.Dropout(0.4, name = 'Dropout'))\n",
    "    model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax', name = 'Dense_layer_2'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Conv2D_layer_1 (Conv2D)      (None, 32, 32, 32)        2432      \n",
      "_________________________________________________________________\n",
      "Conv2D_layer_2 (Conv2D)      (None, 28, 28, 64)        51264     \n",
      "_________________________________________________________________\n",
      "Flatten (Flatten)            (None, 50176)             0         \n",
      "_________________________________________________________________\n",
      "Dense_layer_1 (Dense)        (None, 768)               38535936  \n",
      "_________________________________________________________________\n",
      "Dropout (Dropout)            (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "Dense_layer_2 (Dense)        (None, 43)                33067     \n",
      "=================================================================\n",
      "Total params: 38,622,699\n",
      "Trainable params: 38,622,699\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = small_cnn()\n",
    "epochs = 100\n",
    "optimizer = keras.optimizers.Adam()\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: The training below took about two days to complete on my laptop without *GPU* and which is not *CUDA* enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:From C:\\Users\\Fusi\\anaconda37\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "123/123 - 1473s - loss: 0.1125 - accuracy: 0.0671 - val_loss: 3.5077 - val_accuracy: 0.0852\n",
      "Epoch 2/100\n",
      "123/123 - 1457s - loss: 0.1030 - accuracy: 0.1229 - val_loss: 3.0388 - val_accuracy: 0.2103\n",
      "Epoch 3/100\n",
      "123/123 - 1454s - loss: 0.0794 - accuracy: 0.2678 - val_loss: 1.9857 - val_accuracy: 0.4507\n",
      "Epoch 4/100\n",
      "123/123 - 1461s - loss: 0.0606 - accuracy: 0.3866 - val_loss: 1.5824 - val_accuracy: 0.5286\n",
      "Epoch 5/100\n",
      "123/123 - 1459s - loss: 0.0519 - accuracy: 0.4531 - val_loss: 1.3260 - val_accuracy: 0.5858\n",
      "Epoch 6/100\n",
      "123/123 - 1463s - loss: 0.0468 - accuracy: 0.4982 - val_loss: 1.0846 - val_accuracy: 0.6635\n",
      "Epoch 7/100\n",
      "123/123 - 1468s - loss: 0.0422 - accuracy: 0.5447 - val_loss: 0.9150 - val_accuracy: 0.7191\n",
      "Epoch 8/100\n",
      "123/123 - 1460s - loss: 0.0382 - accuracy: 0.5818 - val_loss: 0.8138 - val_accuracy: 0.7469\n",
      "Epoch 9/100\n",
      "123/123 - 1459s - loss: 0.0348 - accuracy: 0.6174 - val_loss: 0.7283 - val_accuracy: 0.7678\n",
      "Epoch 10/100\n",
      "123/123 - 1457s - loss: 0.0314 - accuracy: 0.6538 - val_loss: 0.5928 - val_accuracy: 0.8199\n",
      "Epoch 11/100\n",
      "123/123 - 1459s - loss: 0.0285 - accuracy: 0.6837 - val_loss: 0.5370 - val_accuracy: 0.8361\n",
      "Epoch 12/100\n",
      "123/123 - 1460s - loss: 0.0262 - accuracy: 0.7065 - val_loss: 0.4588 - val_accuracy: 0.8694\n",
      "Epoch 13/100\n",
      "123/123 - 1458s - loss: 0.0239 - accuracy: 0.7294 - val_loss: 0.4081 - val_accuracy: 0.8809\n",
      "Epoch 14/100\n",
      "123/123 - 1461s - loss: 0.0219 - accuracy: 0.7507 - val_loss: 0.3729 - val_accuracy: 0.8865\n",
      "Epoch 15/100\n",
      "123/123 - 1465s - loss: 0.0202 - accuracy: 0.7655 - val_loss: 0.3402 - val_accuracy: 0.9000\n",
      "Epoch 16/100\n",
      "123/123 - 1465s - loss: 0.0195 - accuracy: 0.7760 - val_loss: 0.3301 - val_accuracy: 0.9029\n",
      "Epoch 17/100\n",
      "123/123 - 1457s - loss: 0.0187 - accuracy: 0.7869 - val_loss: 0.2789 - val_accuracy: 0.9238\n",
      "Epoch 18/100\n",
      "123/123 - 1458s - loss: 0.0171 - accuracy: 0.8014 - val_loss: 0.2644 - val_accuracy: 0.9272\n",
      "Epoch 19/100\n",
      "123/123 - 1462s - loss: 0.0160 - accuracy: 0.8077 - val_loss: 0.2371 - val_accuracy: 0.9358\n",
      "Epoch 20/100\n",
      "123/123 - 1466s - loss: 0.0158 - accuracy: 0.8145 - val_loss: 0.2313 - val_accuracy: 0.9332\n",
      "Epoch 21/100\n",
      "123/123 - 1462s - loss: 0.0149 - accuracy: 0.8236 - val_loss: 0.2056 - val_accuracy: 0.9414\n",
      "Epoch 22/100\n",
      "123/123 - 1456s - loss: 0.0141 - accuracy: 0.8337 - val_loss: 0.1881 - val_accuracy: 0.9495\n",
      "Epoch 23/100\n",
      "123/123 - 1467s - loss: 0.0135 - accuracy: 0.8377 - val_loss: 0.1989 - val_accuracy: 0.9457\n",
      "Epoch 24/100\n",
      "123/123 - 1460s - loss: 0.0131 - accuracy: 0.8461 - val_loss: 0.1784 - val_accuracy: 0.9492\n",
      "Epoch 25/100\n",
      "123/123 - 1455s - loss: 0.0130 - accuracy: 0.8455 - val_loss: 0.1599 - val_accuracy: 0.9551\n",
      "Epoch 26/100\n",
      "123/123 - 1474s - loss: 0.0122 - accuracy: 0.8565 - val_loss: 0.1673 - val_accuracy: 0.9490\n",
      "Epoch 27/100\n",
      "123/123 - 1473s - loss: 0.0115 - accuracy: 0.8615 - val_loss: 0.1510 - val_accuracy: 0.9582\n",
      "Epoch 28/100\n",
      "123/123 - 1471s - loss: 0.0112 - accuracy: 0.8648 - val_loss: 0.1558 - val_accuracy: 0.9543\n",
      "Epoch 29/100\n",
      "123/123 - 1467s - loss: 0.0111 - accuracy: 0.8665 - val_loss: 0.1436 - val_accuracy: 0.9586\n",
      "Epoch 30/100\n",
      "123/123 - 1441s - loss: 0.0108 - accuracy: 0.8691 - val_loss: 0.1429 - val_accuracy: 0.9590\n",
      "Epoch 31/100\n",
      "123/123 - 1441s - loss: 0.0106 - accuracy: 0.8717 - val_loss: 0.1249 - val_accuracy: 0.9659\n",
      "Epoch 32/100\n",
      "123/123 - 1438s - loss: 0.0103 - accuracy: 0.8752 - val_loss: 0.1419 - val_accuracy: 0.9569\n",
      "Epoch 33/100\n",
      "123/123 - 1448s - loss: 0.0103 - accuracy: 0.8748 - val_loss: 0.1127 - val_accuracy: 0.9693\n",
      "Epoch 34/100\n",
      "123/123 - 1438s - loss: 0.0096 - accuracy: 0.8825 - val_loss: 0.1141 - val_accuracy: 0.9699\n",
      "Epoch 35/100\n",
      "123/123 - 1436s - loss: 0.0095 - accuracy: 0.8825 - val_loss: 0.1105 - val_accuracy: 0.9697\n",
      "Epoch 36/100\n",
      "123/123 - 1435s - loss: 0.0092 - accuracy: 0.8859 - val_loss: 0.0994 - val_accuracy: 0.9741\n",
      "Epoch 37/100\n",
      "123/123 - 1432s - loss: 0.0089 - accuracy: 0.8879 - val_loss: 0.1009 - val_accuracy: 0.9712\n",
      "Epoch 38/100\n",
      "123/123 - 1436s - loss: 0.0091 - accuracy: 0.8887 - val_loss: 0.0973 - val_accuracy: 0.9740\n",
      "Epoch 39/100\n",
      "123/123 - 1433s - loss: 0.0084 - accuracy: 0.8947 - val_loss: 0.1001 - val_accuracy: 0.9712\n",
      "Epoch 40/100\n",
      "123/123 - 1436s - loss: 0.0085 - accuracy: 0.8932 - val_loss: 0.0959 - val_accuracy: 0.9766\n",
      "Epoch 41/100\n",
      "123/123 - 1434s - loss: 0.0086 - accuracy: 0.8967 - val_loss: 0.0959 - val_accuracy: 0.9733\n",
      "Epoch 42/100\n",
      "123/123 - 1436s - loss: 0.0083 - accuracy: 0.8986 - val_loss: 0.0885 - val_accuracy: 0.9749\n",
      "Epoch 43/100\n",
      "123/123 - 1433s - loss: 0.0077 - accuracy: 0.9035 - val_loss: 0.0875 - val_accuracy: 0.9749\n",
      "Epoch 44/100\n",
      "123/123 - 1440s - loss: 0.0078 - accuracy: 0.9026 - val_loss: 0.0955 - val_accuracy: 0.9740\n",
      "Epoch 45/100\n",
      "123/123 - 1443s - loss: 0.0076 - accuracy: 0.9049 - val_loss: 0.0888 - val_accuracy: 0.9745\n",
      "Epoch 46/100\n",
      "123/123 - 1438s - loss: 0.0078 - accuracy: 0.9047 - val_loss: 0.0955 - val_accuracy: 0.9737\n",
      "Epoch 47/100\n",
      "123/123 - 1429s - loss: 0.0074 - accuracy: 0.9095 - val_loss: 0.0909 - val_accuracy: 0.9738\n",
      "Epoch 48/100\n",
      "123/123 - 1475s - loss: 0.0075 - accuracy: 0.9066 - val_loss: 0.0819 - val_accuracy: 0.9773\n",
      "Epoch 49/100\n",
      "123/123 - 1429s - loss: 0.0075 - accuracy: 0.9072 - val_loss: 0.0795 - val_accuracy: 0.9784\n",
      "Epoch 50/100\n",
      "123/123 - 1423s - loss: 0.0067 - accuracy: 0.9153 - val_loss: 0.0748 - val_accuracy: 0.9792\n",
      "Epoch 51/100\n",
      "123/123 - 1436s - loss: 0.0071 - accuracy: 0.9105 - val_loss: 0.0733 - val_accuracy: 0.9810\n",
      "Epoch 52/100\n",
      "123/123 - 1435s - loss: 0.0072 - accuracy: 0.9125 - val_loss: 0.0760 - val_accuracy: 0.9793\n",
      "Epoch 53/100\n",
      "123/123 - 1428s - loss: 0.0066 - accuracy: 0.9174 - val_loss: 0.0887 - val_accuracy: 0.9751\n",
      "Epoch 54/100\n",
      "123/123 - 1420s - loss: 0.0069 - accuracy: 0.9136 - val_loss: 0.0829 - val_accuracy: 0.9786\n",
      "Epoch 55/100\n",
      "123/123 - 1431s - loss: 0.0069 - accuracy: 0.9133 - val_loss: 0.0700 - val_accuracy: 0.9816\n",
      "Epoch 56/100\n",
      "123/123 - 1426s - loss: 0.0064 - accuracy: 0.9191 - val_loss: 0.0797 - val_accuracy: 0.9780\n",
      "Epoch 57/100\n",
      "123/123 - 1424s - loss: 0.0064 - accuracy: 0.9202 - val_loss: 0.0744 - val_accuracy: 0.9781\n",
      "Epoch 58/100\n",
      "123/123 - 1412s - loss: 0.0062 - accuracy: 0.9220 - val_loss: 0.0729 - val_accuracy: 0.9797\n",
      "Epoch 59/100\n",
      "123/123 - 1428s - loss: 0.0061 - accuracy: 0.9231 - val_loss: 0.0623 - val_accuracy: 0.9839\n",
      "Epoch 60/100\n",
      "123/123 - 1458s - loss: 0.0058 - accuracy: 0.9265 - val_loss: 0.0692 - val_accuracy: 0.9824\n",
      "Epoch 61/100\n",
      "123/123 - 1460s - loss: 0.0062 - accuracy: 0.9209 - val_loss: 0.0661 - val_accuracy: 0.9835\n",
      "Epoch 62/100\n",
      "123/123 - 1429s - loss: 0.0058 - accuracy: 0.9263 - val_loss: 0.0619 - val_accuracy: 0.9829\n",
      "Epoch 63/100\n",
      "123/123 - 1422s - loss: 0.0055 - accuracy: 0.9271 - val_loss: 0.0602 - val_accuracy: 0.9842\n",
      "Epoch 64/100\n",
      "123/123 - 1418s - loss: 0.0058 - accuracy: 0.9267 - val_loss: 0.0595 - val_accuracy: 0.9858\n",
      "Epoch 65/100\n",
      "123/123 - 1418s - loss: 0.0059 - accuracy: 0.9263 - val_loss: 0.0588 - val_accuracy: 0.9836\n",
      "Epoch 66/100\n",
      "123/123 - 1415s - loss: 0.0059 - accuracy: 0.9246 - val_loss: 0.0602 - val_accuracy: 0.9839\n",
      "Epoch 67/100\n",
      "123/123 - 1413s - loss: 0.0056 - accuracy: 0.9291 - val_loss: 0.0609 - val_accuracy: 0.9828\n",
      "Epoch 68/100\n",
      "123/123 - 1434s - loss: 0.0056 - accuracy: 0.9285 - val_loss: 0.0626 - val_accuracy: 0.9845\n",
      "Epoch 69/100\n",
      "123/123 - 1418s - loss: 0.0052 - accuracy: 0.9347 - val_loss: 0.0553 - val_accuracy: 0.9849\n",
      "Epoch 70/100\n",
      "123/123 - 1409s - loss: 0.0052 - accuracy: 0.9325 - val_loss: 0.0614 - val_accuracy: 0.9842\n",
      "Epoch 71/100\n",
      "123/123 - 1408s - loss: 0.0052 - accuracy: 0.9344 - val_loss: 0.0558 - val_accuracy: 0.9867\n",
      "Epoch 72/100\n",
      "123/123 - 1407s - loss: 0.0053 - accuracy: 0.9332 - val_loss: 0.0537 - val_accuracy: 0.9867\n",
      "Epoch 73/100\n",
      "123/123 - 1397s - loss: 0.0052 - accuracy: 0.9349 - val_loss: 0.0677 - val_accuracy: 0.9805\n",
      "Epoch 74/100\n",
      "123/123 - 1399s - loss: 0.0051 - accuracy: 0.9353 - val_loss: 0.0569 - val_accuracy: 0.9846\n",
      "Epoch 75/100\n",
      "123/123 - 1395s - loss: 0.0048 - accuracy: 0.9373 - val_loss: 0.0611 - val_accuracy: 0.9846\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 76/100\n",
      "123/123 - 1387s - loss: 0.0052 - accuracy: 0.9337 - val_loss: 0.0592 - val_accuracy: 0.9840\n",
      "Epoch 77/100\n",
      "123/123 - 1387s - loss: 0.0050 - accuracy: 0.9357 - val_loss: 0.0474 - val_accuracy: 0.9882\n",
      "Epoch 78/100\n",
      "123/123 - 1402s - loss: 0.0047 - accuracy: 0.9393 - val_loss: 0.0464 - val_accuracy: 0.9870\n",
      "Epoch 79/100\n",
      "123/123 - 1398s - loss: 0.0047 - accuracy: 0.9378 - val_loss: 0.0502 - val_accuracy: 0.9879\n",
      "Epoch 80/100\n",
      "123/123 - 1399s - loss: 0.0048 - accuracy: 0.9380 - val_loss: 0.0558 - val_accuracy: 0.9842\n",
      "Epoch 81/100\n",
      "123/123 - 1392s - loss: 0.0046 - accuracy: 0.9393 - val_loss: 0.0462 - val_accuracy: 0.9879\n",
      "Epoch 82/100\n",
      "123/123 - 1390s - loss: 0.0046 - accuracy: 0.9403 - val_loss: 0.0452 - val_accuracy: 0.9887\n",
      "Epoch 83/100\n",
      "123/123 - 1387s - loss: 0.0045 - accuracy: 0.9426 - val_loss: 0.0427 - val_accuracy: 0.9884\n",
      "Epoch 84/100\n",
      "123/123 - 1379s - loss: 0.0044 - accuracy: 0.9434 - val_loss: 0.0424 - val_accuracy: 0.9892\n",
      "Epoch 85/100\n",
      "123/123 - 1375s - loss: 0.0046 - accuracy: 0.9404 - val_loss: 0.0463 - val_accuracy: 0.9879\n",
      "Epoch 86/100\n",
      "123/123 - 1371s - loss: 0.0046 - accuracy: 0.9421 - val_loss: 0.0435 - val_accuracy: 0.9883\n",
      "Epoch 87/100\n",
      "123/123 - 1370s - loss: 0.0045 - accuracy: 0.9439 - val_loss: 0.0501 - val_accuracy: 0.9875\n",
      "Epoch 88/100\n",
      "123/123 - 1378s - loss: 0.0046 - accuracy: 0.9415 - val_loss: 0.0455 - val_accuracy: 0.9888\n",
      "Epoch 89/100\n",
      "123/123 - 1399s - loss: 0.0043 - accuracy: 0.9435 - val_loss: 0.0488 - val_accuracy: 0.9871\n",
      "Epoch 90/100\n",
      "123/123 - 1397s - loss: 0.0044 - accuracy: 0.9427 - val_loss: 0.0467 - val_accuracy: 0.9888\n",
      "Epoch 91/100\n",
      "123/123 - 1398s - loss: 0.0043 - accuracy: 0.9440 - val_loss: 0.0432 - val_accuracy: 0.9896\n",
      "Epoch 92/100\n",
      "123/123 - 1397s - loss: 0.0042 - accuracy: 0.9470 - val_loss: 0.0430 - val_accuracy: 0.9892\n",
      "Epoch 93/100\n",
      "123/123 - 1379s - loss: 0.0041 - accuracy: 0.9463 - val_loss: 0.0478 - val_accuracy: 0.9878\n",
      "Epoch 94/100\n",
      "123/123 - 1379s - loss: 0.0043 - accuracy: 0.9451 - val_loss: 0.0580 - val_accuracy: 0.9827\n",
      "Epoch 95/100\n",
      "123/123 - 1397s - loss: 0.0042 - accuracy: 0.9470 - val_loss: 0.0468 - val_accuracy: 0.9875\n",
      "Epoch 96/100\n",
      "123/123 - 1377s - loss: 0.0040 - accuracy: 0.9485 - val_loss: 0.0350 - val_accuracy: 0.9915\n",
      "Epoch 97/100\n",
      "123/123 - 1371s - loss: 0.0039 - accuracy: 0.9500 - val_loss: 0.0371 - val_accuracy: 0.9911\n",
      "Epoch 98/100\n",
      "123/123 - 1372s - loss: 0.0040 - accuracy: 0.9490 - val_loss: 0.0516 - val_accuracy: 0.9837\n",
      "Epoch 99/100\n",
      "123/123 - 1395s - loss: 0.0040 - accuracy: 0.9487 - val_loss: 0.0431 - val_accuracy: 0.9893\n",
      "Epoch 100/100\n",
      "123/123 - 1360s - loss: 0.0039 - accuracy: 0.9488 - val_loss: 0.0376 - val_accuracy: 0.9901\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator, \n",
    "                              steps_per_epoch = len(X_train) // batch_size, \n",
    "                              validation_data = val_generator, \n",
    "                              validation_steps = len(X_test) // batch_size, \n",
    "                              class_weight = class_weight, \n",
    "                              epochs = epochs,\n",
    "                              verbose = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the call to `model.fit()` or `model.fit_generator()` returns a `History object`. This object has a member\n",
    "`history`, which is a *dictionary* containing data about everything that happened during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Training accuracy: 0.950 or 95.0%\n"
     ]
    }
   ],
   "source": [
    "print('Best Training accuracy: {:0.3f} or {}%'.format(np.max(train_acc), round(np.max(train_acc) * 100, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation accuracy: 0.992 or 99.15%\n"
     ]
    }
   ],
   "source": [
    "print('Best validation accuracy: {:0.3f} or {}%'.format(np.max(val_acc), round(np.max(val_acc) * 100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_loss, training_acc = model.evaluate_generator(train_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 98.53%\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy: {}%'.format(round(float(training_acc) * 100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_loss, validation_acc = model.evaluate_generator(val_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 99.02%\n"
     ]
    }
   ],
   "source": [
    "print('Validation Accuracy: {}%'.format(round(float(validation_acc) * 100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize training performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x249e5a5fbc8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 100\n",
    "x_epochs = range(1, epochs + 1)\n",
    "\n",
    "plt.plot(x_epochs, train_acc, 'b.', label='Training acc')\n",
    "plt.plot(x_epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(x_epochs, train_loss, 'b.', label='Training loss')\n",
    "plt.plot(x_epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the plots no *overfitting* of the training data occurred! Also notice how the training and validation accuracies are nearly similar (they nearly converge) at the end of training. We attain a training accuracy above `98%` and a validation accuracy above `99%`! The **validation accuracy is always better than the training accuracy** when using data *augmentation* during training. That is because the validation images are real images (i.e without *augmentation* applied to them) and therefore more “easier” for the model to predict than the training images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Readme-Images-Final-test.txt file from the authors of the dataset: \"*If you want to publish results based on GTSRB, \n",
    "this data set is to be used for final testing purposes\n",
    "only... There is one directory that contains all test images (`12,630` images) \n",
    "and one text file with annotations (GT-final_test.csv)*.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_file = \"./data/GTSRB_Final_Test_Images.zip\"\n",
    "test_archive = zipfile.ZipFile(test_file, 'r')\n",
    "test_file_paths = [file for file in test_archive.namelist() if '.ppm' in file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_final = list()\n",
    "for filename in test_file_paths:\n",
    "    img = imread(test_archive.open(filename))\n",
    "    img = resize(img, output_shape = (IMG_SIZE, IMG_SIZE), mode = 'reflect')\n",
    "    X_test_final.append(img)  \n",
    "test_archive.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of final test data: 12630\n"
     ]
    }
   ],
   "source": [
    "print('Size of final test data:', len(X_test_final))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View sample images in the final testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "for i in range(20):\n",
    "    plt.subplot(4,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(X_test_final[i], cmap=plt.cm.binary)\n",
    "    #plt.xlabel(class_names[train_labels[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and explore the annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Roi.X1</th>\n",
       "      <th>Roi.Y1</th>\n",
       "      <th>Roi.X2</th>\n",
       "      <th>Roi.Y2</th>\n",
       "      <th>ClassId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000.ppm</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00001.ppm</td>\n",
       "      <td>42</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002.ppm</td>\n",
       "      <td>48</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00003.ppm</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00004.ppm</td>\n",
       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>52</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Filename  Width  Height  Roi.X1  Roi.Y1  Roi.X2  Roi.Y2  ClassId\n",
       "0  00000.ppm     53      54       6       5      48      49       16\n",
       "1  00001.ppm     42      45       5       5      36      40        1\n",
       "2  00002.ppm     48      52       6       6      43      47       38\n",
       "3  00003.ppm     27      29       5       5      22      24       33\n",
       "4  00004.ppm     60      57       5       5      55      52       11"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "annotation_file = './data/GT-final_test.csv'\n",
    "test_df = pd.read_csv(annotation_file, sep=';')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of final test classes: 12630\n"
     ]
    }
   ],
   "source": [
    "y_test_final = test_df['ClassId'].values\n",
    "print('Size of final test classes:', len(y_test_final))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create final test data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_final = np.array(X_test_final)\n",
    "y_test_final = keras.utils.to_categorical(y_test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All images will be rescaled by 1./255\n",
    "test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "test_generator = test_datagen.flow(X_test_final, y_test_final, batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss, test_acc = model.evaluate_generator(test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 87.47%\n"
     ]
    }
   ],
   "source": [
    "print('Test Accuracy: {}%'.format(round(float(test_acc) * 100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of testing our model on unseen data is `87.47%` which is very low! There is therefore room for tweaking the model to obtain a better test score. One could try **batch normalization** and some **hyperparameter optimization**. There is no symtoms of *overfitting* so I won't try to combat any!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the model as a Keras model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's good practice to always save your best model and load it later for inferences!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = './model/model_GTSRB.h5'\n",
    "model.save(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load model for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GTSRB_model = keras.models.load_model(model_path)"
   ]
  }
 ],
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