Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: **German Traffic Sign Classification Using TensorFlow** \n",
    "**In this project, I used Python and TensorFlow to classify traffic signs.**\n",
    "\n",
    "**Dataset used: [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "This dataset has more than 50,000 images of 43 classes.**\n",
    "\n",
    "**I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline architecture:\n",
    "- **Load The Data.**\n",
    "- **Dataset Summary & Exploration**\n",
    "- **Data Preprocessing**.\n",
    "    - Shuffling.\n",
    "    - Grayscaling.\n",
    "    - Local Histogram Equalization.\n",
    "    - Normalization.\n",
    "- **Design a Model Architecture.**\n",
    "    - LeNet-5.\n",
    "    - VGGNet.\n",
    "- **Model Training and Evaluation.**\n",
    "- **Testing the Model Using the Test Set.**\n",
    "- **Testing the Model on New Images.**\n",
    "\n",
    "I'll explain each step in details below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Environement:\n",
    "-  Ubuntu 16.04\n",
    "-  Anaconda 5.0.1\n",
    "-  Python 3.6.6\n",
    "-  TensorFlow 1.12.0 (GPU support)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Python libraries\n",
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import cv2\n",
    "import skimage.morphology as morp\n",
    "from skimage.filters import rank\n",
    "from sklearn.utils import shuffle\n",
    "import csv\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import flatten\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# is it using the GPU?\n",
    "print(tf.test.gpu_device_name())\n",
    "\n",
    "# Show current TensorFlow version\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 1: Load The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the dataset from [here](https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip). This is a pickled dataset in which we've already resized the images to 32x32.\n",
    "\n",
    "We already have three `.p` files of 32x32 resized images:\n",
    "- `train.p`: The training set.\n",
    "- `test.p`: The testing set.\n",
    "- `valid.p`: The validation set.\n",
    "\n",
    "We will use Python `pickle` to load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_file = \"./traffic-signs-data/train.p\"\n",
    "validation_file= \"./traffic-signs-data/valid.p\"\n",
    "testing_file = \"./traffic-signs-data/test.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping ClassID to traffic sign names\n",
    "signs = []\n",
    "with open('signnames.csv', 'r') as csvfile:\n",
    "    signnames = csv.reader(csvfile, delimiter=',')\n",
    "    next(signnames,None)\n",
    "    for row in signnames:\n",
    "        signs.append(row[1])\n",
    "    csvfile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 2: Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
    "- `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
    "- `'sizes'` is a list containing tuples, (width, height) representing the original width and height the image.\n",
    "- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image.\n",
    "\n",
    "The code snippets below will provide a basic summery of the Dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First, we will use `numpy` provide the number of images in each subset, in addition to the image size, and the number of unique classes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples:  34799\n",
      "Number of testing examples:  12630\n",
      "Number of validation examples:  4410\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n",
    "\n",
    "# Number of training examples\n",
    "n_train = X_train.shape[0]\n",
    "\n",
    "# Number of testing examples\n",
    "n_test = X_test.shape[0]\n",
    "\n",
    "# Number of validation examples.\n",
    "n_validation = X_valid.shape[0]\n",
    "\n",
    "# What's the shape of an traffic sign image?\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# How many unique classes/labels there are in the dataset.\n",
    "n_classes = len(np.unique(y_train))\n",
    "\n",
    "print(\"Number of training examples: \", n_train)\n",
    "print(\"Number of testing examples: \", n_test)\n",
    "print(\"Number of validation examples: \", n_validation)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Then, we will use `matplotlib` plot sample images from each subset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def list_images(dataset, dataset_y, ylabel=\"\", cmap=None):\n",
    "    \"\"\"\n",
    "    Display a list of images in a single figure with matplotlib.\n",
    "        Parameters:\n",
    "            images: An np.array compatible with plt.imshow.\n",
    "            lanel (Default = No label): A string to be used as a label for each image.\n",
    "            cmap (Default = None): Used to display gray images.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(15, 16))\n",
    "    for i in range(6):\n",
    "        plt.subplot(1, 6, i+1)\n",
    "        indx = random.randint(0, len(dataset))\n",
    "        #Use gray scale color map if there is only one channel\n",
    "        cmap = 'gray' if len(dataset[indx].shape) == 2 else cmap\n",
    "        plt.imshow(dataset[indx], cmap = cmap)\n",
    "        plt.xlabel(signs[dataset_y[indx]])\n",
    "        plt.ylabel(ylabel)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "    plt.tight_layout(pad=0, h_pad=0, w_pad=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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by+jMNz8ep1tGVd5t49vQzTNEBHXtJiPGZl14dx8PjjNQ2T3nWUhoWTdduMht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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381c45198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UYG8NR+V+aZUnSNhuqTTQ0LMHSUVHCuuWOxFV+ZkwZeXV+VvlhDFUrDrf1anE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\nhmEYhlFb7MWIYRiGYRiGYRiGYRi15YR4jDhxkvla1wobEL+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qOis5P1VVl/m7bilm8CeX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\nBEEQBEEQBEGT5f840dQjiz/g9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381c45438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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he58NsRbEkmViIR7lwONHD0Ls7Tfjuec3Px1j0ygEFpyb6iGV3URyxp66SaMA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\n8C3gn5t3SNrdbXatfz+1aX+bpPXVkR0fRdlZ0mJJP5Z0g39eVon6HB9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381e32b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting sample examples\n",
    "list_images(X_train, y_train, \"Training example\")\n",
    "list_images(X_test, y_test, \"Testing example\")\n",
    "list_images(X_valid, y_valid, \"Validation example\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**And finally, we will use `numpy` to plot a histogram of the count of images in each unique class.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histogram_plot(dataset, label):\n",
    "    \"\"\"\n",
    "    Plots a histogram of the input data.\n",
    "        Parameters:\n",
    "            dataset: Input data to be plotted as a histogram.\n",
    "            lanel: A string to be used as a label for the histogram.\n",
    "    \"\"\"\n",
    "    hist, bins = np.histogram(dataset, bins=n_classes)\n",
    "    width = 0.7 * (bins[1] - bins[0])\n",
    "    center = (bins[:-1] + bins[1:]) / 2\n",
    "    plt.bar(center, hist, align='center', width=width)\n",
    "    plt.xlabel(label)\n",
    "    plt.ylabel(\"Image count\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381cc0898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381c1b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381c01630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting histograms of the count of each sign\n",
    "histogram_plot(y_train, \"Training examples\")\n",
    "histogram_plot(y_test, \"Testing examples\")\n",
    "histogram_plot(y_valid, \"Validation examples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Data Preprocessing\n",
    "\n",
    "In this step, we will apply several preprocessing steps to the input images to achieve the best possible results.\n",
    "\n",
    "**We will use the following preprocessing techniques:**\n",
    "1. Shuffling.\n",
    "2. Grayscaling.\n",
    "3. Local Histogram Equalization.\n",
    "4. Normalization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.\n",
    "**Shuffling**: In general, we shuffle the training data to increase randomness and variety in training dataset, in order for the model to be more stable. We will use `sklearn` to shuffle our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = shuffle(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.\n",
    "**Grayscaling**: In their paper [\"Traffic Sign Recognition with Multi-Scale Convolutional Networks\"](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf) published in 2011, P. Sermanet and Y. LeCun stated that using grayscale images instead of color improves the ConvNet's accuracy. We will use `OpenCV` to convert the training images into grey scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gray_scale(image):\n",
    "    \"\"\"\n",
    "    Convert images to gray scale.\n",
    "        Parameters:\n",
    "            image: An np.array compatible with plt.imshow.\n",
    "    \"\"\"\n",
    "    return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jUYy1C5OqLlu2LIoxoSGToTGBGwA89dRTUYzVz549e6IYkwmlzlNHt/K5vp68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\nBI1BlU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381cef550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample images after greyscaling\n",
    "gray_images = list(map(gray_scale, X_train))\n",
    "list_images(gray_images, y_train, \"Gray Scale image\", \"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.\n",
    "**Local Histogram Equalization**: This technique simply spreads out the most frequent intensity values in an image, resulting in enhancing images with low contrast. Applying this technique will be very helpfull in our case since the dataset in hand has real world images, and many of them has low contrast. We will use `skimage` to apply local histogram equalization to the training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def local_histo_equalize(image):\n",
    "    \"\"\"\n",
    "    Apply local histogram equalization to grayscale images.\n",
    "        Parameters:\n",
    "            image: A grayscale image.\n",
    "    \"\"\"\n",
    "    kernel = morp.disk(30)\n",
    "    img_local = rank.equalize(image, selem=kernel)\n",
    "    return img_local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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epGmSrpN0saQFktwRzGGH69lUCdezqRKuZ1MlXM+mSrieTTuQ842Rd0s6RdLS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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa382b88eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample images after Local Histogram Equalization\n",
    "equalized_images = list(map(local_histo_equalize, gray_images))\n",
    "list_images(equalized_images, y_train, \"Equalized Image\", \"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.\n",
    "**Normalization**: Normalization is a process that changes the range of pixel intensity values. Usually the image data should be normalized so that the data has mean zero and equal variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_normalize(image):\n",
    "    \"\"\"\n",
    "    Normalize images to [0, 1] scale.\n",
    "        Parameters:\n",
    "            image: An np.array compatible with plt.imshow.\n",
    "    \"\"\"\n",
    "    image = np.divide(image, 255)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yztl7yfa8yFOEfc0zzzyTxNSzDh48uOb+bXsrte4L2J4PGjQoielFMXHixCSm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\nt946uUb0ImB78vTTT1eXmVPUjVPjTz0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l3ZtpzJAaIVP+x0r/6SgZ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\nZmZmZmZmZgPLgREzMzMzMzMzG1j/AUOgZBfCZFhPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa381c1b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample images after normalization\n",
    "n_training = X_train.shape\n",
    "normalized_images = np.zeros((n_training[0], n_training[1], n_training[2]))\n",
    "for i, img in enumerate(equalized_images):\n",
    "    normalized_images[i] = image_normalize(img)\n",
    "list_images(normalized_images, y_train, \"Normalized Image\", \"gray\")\n",
    "normalized_images = normalized_images[..., None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(data):\n",
    "    \"\"\"\n",
    "    Applying the preprocessing steps to the input data.\n",
    "        Parameters:\n",
    "            data: An np.array compatible with plt.imshow.\n",
    "    \"\"\"\n",
    "    gray_images = list(map(gray_scale, data))\n",
    "    equalized_images = list(map(local_histo_equalize, gray_images))\n",
    "    n_training = data.shape\n",
    "    normalized_images = np.zeros((n_training[0], n_training[1], n_training[2]))\n",
    "    for i, img in enumerate(equalized_images):\n",
    "        normalized_images[i] = image_normalize(img)\n",
    "    normalized_images = normalized_images[..., None]\n",
    "    return normalized_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Design a Model Architecture\n",
    "\n",
    "In this step, we will design and implement a deep learning model that learns to recognize traffic signs from our dataset [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "We'll use Convolutional Neural Networks to classify the images in this dataset. The reason behind choosing ConvNets is that they are designed to recognize visual patterns directly from pixel images with minimal preprocessing. They automatically learn hierarchies of invariant features at every level from data.\n",
    "We will implement two of the most famous ConvNets. Our goal is to reach an accuracy of +95% on the validation set.\n",
    "\n",
    "I'll start by explaining each network architecture, then implement it using TensorFlow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notes**:\n",
    "1. We specify the learning rate of 0.001, which tells the network how quickly to update the weights.\n",
    "2. We minimize the loss function using the Adaptive Moment Estimation (Adam) Algorithm. Adam is an optimization algorithm introduced by D. Kingma and J. Lei Ba in a 2015 paper named [Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980). Adam algorithm computes adaptive learning rates for each parameter. In addition to storing an exponentially decaying average of past squared gradients like [Adadelta](https://arxiv.org/pdf/1212.5701.pdf) and [RMSprop](https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf) algorithms, Adam also keeps an exponentially decaying average of past gradients mtmt, similar to [momentum algorithm](http://www.sciencedirect.com/science/article/pii/S0893608098001166?via%3Dihub), which in turn produce better results.\n",
    "3. we will run `minimize()` function on the optimizer which use backprobagation to update the network and minimize our training loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.  LeNet-5\n",
    "LeNet-5 is a convolutional network designed for handwritten and machine-printed character recognition. It was introduced by the famous [Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun) in his paper [Gradient-Based Learning Applied to Document Recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) in 1998. Although this ConvNet is intended to classify hand-written digits, we're confident it have a very high accuracy when dealing with traffic signs, given that both hand-written digits and traffic signs are given to the computer in the form of pixel images.\n",
    "\n",
    "**LeNet-5 architecture:**\n",
    "<figure>\n",
    " <img src=\"LeNet.png\" width=\"1072\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ConvNet follows these steps:\n",
    "\n",
    "Input => Convolution => ReLU => Pooling => Convolution => ReLU => Pooling => FullyConnected => ReLU => FullyConnected\n",
    "\n",
    "**Layer 1 (Convolutional):** The output shape should be 28x28x6.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Pooling.** The output shape should be 14x14x6.\n",
    "\n",
    "**Layer 2 (Convolutional):** The output shape should be 10x10x16.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Pooling.** The output shape should be 5x5x16.\n",
    "\n",
    "**Flattening:** Flatten the output shape of the final pooling layer such that it's 1D instead of 3D.\n",
    "\n",
    "**Layer 3 (Fully Connected):** This should have 120 outputs.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 4 (Fully Connected):** This should have 84 outputs.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 5 (Fully Connected):** This should have 43 outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LaNet:  \n",
    "\n",
    "    def __init__(self, n_out=43, mu=0, sigma=0.1, learning_rate=0.001):\n",
    "        # Hyperparameters\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "\n",
    "        # Layer 1 (Convolutional): Input = 32x32x1. Output = 28x28x6.\n",
    "        self.filter1_width = 5\n",
    "        self.filter1_height = 5\n",
    "        self.input1_channels = 1\n",
    "        self.conv1_output = 6\n",
    "        # Weight and bias\n",
    "        self.conv1_weight = tf.Variable(tf.truncated_normal(\n",
    "            shape=(self.filter1_width, self.filter1_height, self.input1_channels, self.conv1_output),\n",
    "            mean = self.mu, stddev = self.sigma))\n",
    "        self.conv1_bias = tf.Variable(tf.zeros(self.conv1_output))\n",
    "        # Apply Convolution\n",
    "        self.conv1 = tf.nn.conv2d(x, self.conv1_weight, strides=[1, 1, 1, 1], padding='VALID') + self.conv1_bias\n",
    "        \n",
    "        # Activation:\n",
    "        self.conv1 = tf.nn.relu(self.conv1)\n",
    "        \n",
    "        # Pooling: Input = 28x28x6. Output = 14x14x6.\n",
    "        self.conv1 = tf.nn.max_pool(self.conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "        \n",
    "        # Layer 2 (Convolutional): Output = 10x10x16.\n",
    "        self.filter2_width = 5\n",
    "        self.filter2_height = 5\n",
    "        self.input2_channels = 6\n",
    "        self.conv2_output = 16\n",
    "        # Weight and bias\n",
    "        self.conv2_weight = tf.Variable(tf.truncated_normal(\n",
    "            shape=(self.filter2_width, self.filter2_height, self.input2_channels, self.conv2_output),\n",
    "            mean = self.mu, stddev = self.sigma))\n",
    "        self.conv2_bias = tf.Variable(tf.zeros(self.conv2_output))\n",
    "        # Apply Convolution\n",
    "        self.conv2 = tf.nn.conv2d(self.conv1, self.conv2_weight, strides=[1, 1, 1, 1], padding='VALID') + self.conv2_bias\n",
    "        \n",
    "        # Activation:\n",
    "        self.conv2 = tf.nn.relu(self.conv2)\n",
    "        \n",
    "        # Pooling: Input = 10x10x16. Output = 5x5x16.\n",
    "        self.conv2 = tf.nn.max_pool(self.conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "        \n",
    "        # Flattening: Input = 5x5x16. Output = 400.\n",
    "        self.fully_connected0 = flatten(self.conv2)\n",
    "        \n",
    "        # Layer 3 (Fully Connected): Input = 400. Output = 120.\n",
    "        self.connected1_weights = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = self.mu, stddev = self.sigma))\n",
    "        self.connected1_bias = tf.Variable(tf.zeros(120))\n",
    "        self.fully_connected1 = tf.add((tf.matmul(self.fully_connected0, self.connected1_weights)), self.connected1_bias)\n",
    "        \n",
    "        # Activation:\n",
    "        self.fully_connected1 = tf.nn.relu(self.fully_connected1)\n",
    "    \n",
    "        # Layer 4 (Fully Connected): Input = 120. Output = 84.\n",
    "        self.connected2_weights = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = self.mu, stddev = self.sigma))\n",
    "        self.connected2_bias = tf.Variable(tf.zeros(84))\n",
    "        self.fully_connected2 = tf.add((tf.matmul(self.fully_connected1, self.connected2_weights)), self.connected2_bias)\n",
    "        \n",
    "        # Activation.\n",
    "        self.fully_connected2 = tf.nn.relu(self.fully_connected2)\n",
    "    \n",
    "        # Layer 5 (Fully Connected): Input = 84. Output = 43.\n",
    "        self.output_weights = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = self.mu, stddev = self.sigma))\n",
    "        self.output_bias = tf.Variable(tf.zeros(43))\n",
    "        self.logits =  tf.add((tf.matmul(self.fully_connected2, self.output_weights)), self.output_bias)\n",
    "\n",
    "        # Training operation\n",
    "        self.one_hot_y = tf.one_hot(y, n_out)\n",
    "        self.cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.logits, labels=self.one_hot_y)\n",
    "        self.loss_operation = tf.reduce_mean(self.cross_entropy)\n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)\n",
    "        self.training_operation = self.optimizer.minimize(self.loss_operation)\n",
    "\n",
    "        # Accuracy operation\n",
    "        self.correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.one_hot_y, 1))\n",
    "        self.accuracy_operation = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))\n",
    "\n",
    "        # Saving all variables\n",
    "        self.saver = tf.train.Saver()\n",
    "    \n",
    "    def y_predict(self, X_data, BATCH_SIZE=64):\n",
    "        num_examples = len(X_data)\n",
    "        y_pred = np.zeros(num_examples, dtype=np.int32)\n",
    "        sess = tf.get_default_session()\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            batch_x = X_data[offset:offset+BATCH_SIZE]\n",
    "            y_pred[offset:offset+BATCH_SIZE] = sess.run(tf.argmax(self.logits, 1), \n",
    "                               feed_dict={x:batch_x, keep_prob:1, keep_prob_conv:1})\n",
    "        return y_pred\n",
    "    \n",
    "    def evaluate(self, X_data, y_data, BATCH_SIZE=64):\n",
    "        num_examples = len(X_data)\n",
    "        total_accuracy = 0\n",
    "        sess = tf.get_default_session()\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
    "            accuracy = sess.run(self.accuracy_operation, \n",
    "                                feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0, keep_prob_conv: 1.0 })\n",
    "            total_accuracy += (accuracy * len(batch_x))\n",
    "        return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.  VGGNet\n",
    "VGGNet was first introduced in 2014 by K. Simonyan and A. Zisserman from the University of Oxford in a paper called [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf). They were investigating the convolutional network depth on its accuracy in the large-scale image recognition setting. Their main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.\n",
    "\n",
    "**VGGNet architecture:**\n",
    "<figure>\n",
    " <img src=\"VGGNet.png\" width=\"1072\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original VGGNet architecture has 16-19 layers, but I've excluded some of them and implemented a modified version of only 12 layers to save computational resources.\n",
    "\n",
    "This ConvNet follows these steps:\n",
    "\n",
    "Input => Convolution => ReLU => Convolution => ReLU => Pooling => Convolution => ReLU => Convolution => ReLU => Pooling => Convolution => ReLU => Convolution => ReLU => Pooling => FullyConnected => ReLU => FullyConnected => ReLU => FullyConnected\n",
    "\n",
    "**Layer 1 (Convolutional):** The output shape should be 32x32x32.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 2 (Convolutional):** The output shape should be 32x32x32.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 3 (Pooling)** The output shape should be 16x16x32.\n",
    "\n",
    "**Layer 4 (Convolutional):** The output shape should be 16x16x64.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 5 (Convolutional):** The output shape should be 16x16x64.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 6 (Pooling)** The output shape should be 8x8x64.\n",
    "\n",
    "**Layer 7 (Convolutional):** The output shape should be 8x8x128.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 8 (Convolutional):** The output shape should be 8x8x128.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 9 (Pooling)** The output shape should be 4x4x128.\n",
    "\n",
    "**Flattening:** Flatten the output shape of the final pooling layer such that it's 1D instead of 3D.\n",
    "\n",
    "**Layer 10 (Fully Connected):** This should have 128 outputs.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 11 (Fully Connected):** This should have 128 outputs.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 12 (Fully Connected):** This should have 43 outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VGGnet:  \n",
    "\n",
    "    def __init__(self, n_out=43, mu=0, sigma=0.1, learning_rate=0.001):\n",
    "        # Hyperparameters\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "\n",
    "        # Layer 1 (Convolutional): Input = 32x32x1. Output = 32x32x32.\n",
    "        self.conv1_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 1, 32), mean = self.mu, stddev = self.sigma))\n",
    "        self.conv1_b = tf.Variable(tf.zeros(32))\n",
    "        self.conv1   = tf.nn.conv2d(x, self.conv1_W, strides=[1, 1, 1, 1], padding='SAME') + self.conv1_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.conv1 = tf.nn.relu(self.conv1)\n",
    "\n",
    "        # Layer 2 (Convolutional): Input = 32x32x32. Output = 32x32x32.\n",
    "        self.conv2_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 32, 32), mean = self.mu, stddev = self.sigma))\n",
    "        self.conv2_b = tf.Variable(tf.zeros(32))\n",
    "        self.conv2   = tf.nn.conv2d(self.conv1, self.conv2_W, strides=[1, 1, 1, 1], padding='SAME') + self.conv2_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.conv2 = tf.nn.relu(self.conv2)\n",
    "\n",
    "        # Layer 3 (Pooling): Input = 32x32x32. Output = 16x16x32.\n",
    "        self.conv2 = tf.nn.max_pool(self.conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "        self.conv2 = tf.nn.dropout(self.conv2, keep_prob_conv)\n",
    "\n",
    "        # Layer 4 (Convolutional): Input = 16x16x32. Output = 16x16x64.\n",
    "        self.conv3_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 32, 64), mean = self.mu, stddev = self.sigma))\n",
    "        self.conv3_b = tf.Variable(tf.zeros(64))\n",
    "        self.conv3   = tf.nn.conv2d(self.conv2, self.conv3_W, strides=[1, 1, 1, 1], padding='SAME') + self.conv3_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.conv3 = tf.nn.relu(self.conv3)\n",
    "\n",
    "        # Layer 5 (Convolutional): Input = 16x16x64. Output = 16x16x64.\n",
    "        self.conv4_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 64, 64), mean = self.mu, stddev = self.sigma))\n",
    "        self.conv4_b = tf.Variable(tf.zeros(64))\n",
    "        self.conv4   = tf.nn.conv2d(self.conv3, self.conv4_W, strides=[1, 1, 1, 1], padding='SAME') + self.conv4_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.conv4 = tf.nn.relu(self.conv4)\n",
    "\n",
    "        # Layer 6 (Pooling): Input = 16x16x64. Output = 8x8x64.\n",
    "        self.conv4 = tf.nn.max_pool(self.conv4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "        self.conv4 = tf.nn.dropout(self.conv4, keep_prob_conv) # dropout\n",
    "\n",
    "        # Layer 7 (Convolutional): Input = 8x8x64. Output = 8x8x128.\n",
    "        self.conv5_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 64, 128), mean = self.mu, stddev = self.sigma))\n",
    "        self.conv5_b = tf.Variable(tf.zeros(128))\n",
    "        self.conv5   = tf.nn.conv2d(self.conv4, self.conv5_W, strides=[1, 1, 1, 1], padding='SAME') + self.conv5_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.conv5 = tf.nn.relu(self.conv5)\n",
    "\n",
    "        # Layer 8 (Convolutional): Input = 8x8x128. Output = 8x8x128.\n",
    "        self.conv6_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 128, 128), mean = self.mu, stddev = self.sigma))\n",
    "        self.conv6_b = tf.Variable(tf.zeros(128))\n",
    "        self.conv6   = tf.nn.conv2d(self.conv5, self.conv6_W, strides=[1, 1, 1, 1], padding='SAME') + self.conv6_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.conv6 = tf.nn.relu(self.conv6)\n",
    "\n",
    "        # Layer 9 (Pooling): Input = 8x8x128. Output = 4x4x128.\n",
    "        self.conv6 = tf.nn.max_pool(self.conv6, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "        self.conv6 = tf.nn.dropout(self.conv6, keep_prob_conv) # dropout\n",
    "\n",
    "        # Flatten. Input = 4x4x128. Output = 2048.\n",
    "        self.fc0   = flatten(self.conv6)\n",
    "\n",
    "        # Layer 10 (Fully Connected): Input = 2048. Output = 128.\n",
    "        self.fc1_W = tf.Variable(tf.truncated_normal(shape=(2048, 128), mean = self.mu, stddev = self.sigma))\n",
    "        self.fc1_b = tf.Variable(tf.zeros(128))\n",
    "        self.fc1   = tf.matmul(self.fc0, self.fc1_W) + self.fc1_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.fc1    = tf.nn.relu(self.fc1)\n",
    "        self.fc1    = tf.nn.dropout(self.fc1, keep_prob) # dropout\n",
    "\n",
    "        # Layer 11 (Fully Connected): Input = 128. Output = 128.\n",
    "        self.fc2_W  = tf.Variable(tf.truncated_normal(shape=(128, 128), mean = self.mu, stddev = self.sigma))\n",
    "        self.fc2_b  = tf.Variable(tf.zeros(128))\n",
    "        self.fc2    = tf.matmul(self.fc1, self.fc2_W) + self.fc2_b\n",
    "\n",
    "        # ReLu Activation.\n",
    "        self.fc2    = tf.nn.relu(self.fc2)\n",
    "        self.fc2    = tf.nn.dropout(self.fc2, keep_prob) # dropout\n",
    "\n",
    "        # Layer 12 (Fully Connected): Input = 128. Output = n_out.\n",
    "        self.fc3_W  = tf.Variable(tf.truncated_normal(shape=(128, n_out), mean = self.mu, stddev = self.sigma))\n",
    "        self.fc3_b  = tf.Variable(tf.zeros(n_out))\n",
    "        self.logits = tf.matmul(self.fc2, self.fc3_W) + self.fc3_b\n",
    "\n",
    "        # Training operation\n",
    "        self.one_hot_y = tf.one_hot(y, n_out)\n",
    "        self.cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.logits, labels=self.one_hot_y)\n",
    "        self.loss_operation = tf.reduce_mean(self.cross_entropy)\n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)\n",
    "        self.training_operation = self.optimizer.minimize(self.loss_operation)\n",
    "\n",
    "        # Accuracy operation\n",
    "        self.correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.one_hot_y, 1))\n",
    "        self.accuracy_operation = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))\n",
    "\n",
    "        # Saving all variables\n",
    "        self.saver = tf.train.Saver()\n",
    "        \n",
    "    def y_predict(self, X_data, BATCH_SIZE=64):\n",
    "        num_examples = len(X_data)\n",
    "        y_pred = np.zeros(num_examples, dtype=np.int32)\n",
    "        sess = tf.get_default_session()\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            batch_x = X_data[offset:offset+BATCH_SIZE]\n",
    "            y_pred[offset:offset+BATCH_SIZE] = sess.run(tf.argmax(self.logits, 1), \n",
    "                               feed_dict={x:batch_x, keep_prob:1, keep_prob_conv:1})\n",
    "        return y_pred\n",
    "    \n",
    "    def evaluate(self, X_data, y_data, BATCH_SIZE=64):\n",
    "        num_examples = len(X_data)\n",
    "        total_accuracy = 0\n",
    "        sess = tf.get_default_session()\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
    "            accuracy = sess.run(self.accuracy_operation, \n",
    "                                feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0, keep_prob_conv: 1.0 })\n",
    "            total_accuracy += (accuracy * len(batch_x))\n",
    "        return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4: Model Training and Evaluation\n",
    "\n",
    "In this step, we will train our model using `normalized_images`, then we'll compute softmax cross entropy between `logits` and `labels` to measure the model's error probability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`x` is a placeholder for a batch of input images.\n",
    "`y` is a placeholder for a batch of output labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
    "y = tf.placeholder(tf.int32, (None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `keep_prob` and `keep_prob_conv` variables will be used to control the dropout rate when training the neural network.\n",
    "Overfitting is a serious problem in deep nural networks. Dropout is a technique for addressing this problem.\n",
    "The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different “thinned” networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. This technique was introduced by N. Srivastava, G. Hinton, A. Krizhevsky I. Sutskever, and R. Salakhutdinov in their paper [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_prob = tf.placeholder(tf.float32)       # For fully-connected layers\n",
    "keep_prob_conv = tf.placeholder(tf.float32)  # For convolutional layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validation set preprocessing\n",
    "X_valid_preprocessed = preprocess(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 30\n",
    "BATCH_SIZE = 64\n",
    "DIR = 'Saved_Models'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll run the training data through the training pipeline to train the model.\n",
    "- Before each epoch, we'll shuffle the training set.\n",
    "- After each epoch, we measure the loss and accuracy of the validation set.\n",
    "- And after training, we will save the model.\n",
    "- A low accuracy on the training and validation sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LeNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training ...\n",
      "\n",
      "EPOCH 1 : Validation Accuracy = 81.451%\n",
      "EPOCH 2 : Validation Accuracy = 87.755%\n",
      "EPOCH 3 : Validation Accuracy = 90.113%\n",
      "EPOCH 4 : Validation Accuracy = 91.519%\n",
      "EPOCH 5 : Validation Accuracy = 90.658%\n",
      "EPOCH 6 : Validation Accuracy = 92.608%\n",
      "EPOCH 7 : Validation Accuracy = 92.902%\n",
      "EPOCH 8 : Validation Accuracy = 92.585%\n",
      "EPOCH 9 : Validation Accuracy = 92.993%\n",
      "EPOCH 10 : Validation Accuracy = 92.766%\n",
      "EPOCH 11 : Validation Accuracy = 93.356%\n",
      "EPOCH 12 : Validation Accuracy = 93.469%\n",
      "EPOCH 13 : Validation Accuracy = 93.832%\n",
      "EPOCH 14 : Validation Accuracy = 94.603%\n",
      "EPOCH 15 : Validation Accuracy = 93.333%\n",
      "EPOCH 16 : Validation Accuracy = 93.787%\n",
      "EPOCH 17 : Validation Accuracy = 94.263%\n",
      "EPOCH 18 : Validation Accuracy = 92.857%\n",
      "EPOCH 19 : Validation Accuracy = 93.832%\n",
      "EPOCH 20 : Validation Accuracy = 93.605%\n",
      "EPOCH 21 : Validation Accuracy = 93.447%\n",
      "EPOCH 22 : Validation Accuracy = 94.286%\n",
      "EPOCH 23 : Validation Accuracy = 94.671%\n",
      "EPOCH 24 : Validation Accuracy = 94.172%\n",
      "EPOCH 25 : Validation Accuracy = 94.399%\n",
      "EPOCH 26 : Validation Accuracy = 95.057%\n",
      "EPOCH 27 : Validation Accuracy = 95.329%\n",
      "EPOCH 28 : Validation Accuracy = 94.218%\n",
      "EPOCH 29 : Validation Accuracy = 94.286%\n",
      "EPOCH 30 : Validation Accuracy = 94.853%\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "LeNet_Model = LaNet(n_out = n_classes)\n",
    "model_name = \"LeNet\"\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(y_train)\n",
    "    print(\"Training ...\")\n",
    "    print()\n",
    "    for i in range(EPOCHS):\n",
    "        normalized_images, y_train = shuffle(normalized_images, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = normalized_images[offset:end], y_train[offset:end]\n",
    "            sess.run(LeNet_Model.training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob : 0.5, keep_prob_conv: 0.7})\n",
    "            \n",
    "        validation_accuracy = LeNet_Model.evaluate(X_valid_preprocessed, y_valid)\n",
    "        print(\"EPOCH {} : Validation Accuracy = {:.3f}%\".format(i+1, (validation_accuracy*100)))\n",
    "    LeNet_Model.saver.save(sess, os.path.join(DIR, model_name))\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we've been able to reach a maximum accuracy of **95.3%** on the validation set over 30 epochs, using a learning rate of 0.001.\n",
    "\n",
    "Now, we'll train the VGGNet model and evaluate it's accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VGGNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 : Validation Accuracy = 31.655%\n",
      "EPOCH 2 : Validation Accuracy = 59.592%\n",
      "EPOCH 3 : Validation Accuracy = 78.639%\n",
      "EPOCH 4 : Validation Accuracy = 88.617%\n",
      "EPOCH 5 : Validation Accuracy = 92.812%\n",
      "EPOCH 6 : Validation Accuracy = 95.601%\n",
      "EPOCH 7 : Validation Accuracy = 96.667%\n",
      "EPOCH 8 : Validation Accuracy = 97.528%\n",
      "EPOCH 9 : Validation Accuracy = 98.390%\n",
      "EPOCH 10 : Validation Accuracy = 98.322%\n",
      "EPOCH 11 : Validation Accuracy = 98.776%\n",
      "EPOCH 12 : Validation Accuracy = 98.730%\n",
      "EPOCH 13 : Validation Accuracy = 98.617%\n",
      "EPOCH 14 : Validation Accuracy = 98.571%\n",
      "EPOCH 15 : Validation Accuracy = 99.025%\n",
      "EPOCH 16 : Validation Accuracy = 99.116%\n",
      "EPOCH 17 : Validation Accuracy = 98.776%\n",
      "EPOCH 18 : Validation Accuracy = 98.707%\n",
      "EPOCH 19 : Validation Accuracy = 98.526%\n",
      "EPOCH 20 : Validation Accuracy = 98.685%\n",
      "EPOCH 21 : Validation Accuracy = 99.297%\n",
      "EPOCH 22 : Validation Accuracy = 99.320%\n",
      "EPOCH 23 : Validation Accuracy = 99.297%\n",
      "EPOCH 24 : Validation Accuracy = 99.161%\n",
      "EPOCH 25 : Validation Accuracy = 98.798%\n",
      "EPOCH 26 : Validation Accuracy = 98.707%\n",
      "EPOCH 27 : Validation Accuracy = 99.048%\n",
      "EPOCH 28 : Validation Accuracy = 99.116%\n",
      "EPOCH 29 : Validation Accuracy = 98.458%\n",
      "EPOCH 30 : Validation Accuracy = 99.161%\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "VGGNet_Model = VGGnet(n_out = n_classes)\n",
    "model_name = \"VGGNet\"\n",
    "\n",
    "# Validation set preprocessing\n",
    "X_valid_preprocessed = preprocess(X_valid)\n",
    "one_hot_y_valid = tf.one_hot(y_valid, 43)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(y_train)\n",
    "    print(\"Training...\")\n",
    "    print()\n",
    "    for i in range(EPOCHS):\n",
    "        normalized_images, y_train = shuffle(normalized_images, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = normalized_images[offset:end], y_train[offset:end]\n",
    "            sess.run(VGGNet_Model.training_operation, \n",
    "            feed_dict={x: batch_x, y: batch_y, keep_prob : 0.5, keep_prob_conv: 0.7})\n",
    "\n",
    "        validation_accuracy = VGGNet_Model.evaluate(X_valid_preprocessed, y_valid)\n",
    "        print(\"EPOCH {} : Validation Accuracy = {:.3f}%\".format(i+1, (validation_accuracy*100)))\n",
    "    VGGNet_Model.saver.save(sess, os.path.join(DIR, model_name))\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using VGGNet, we've been able to reach a maximum **validation accuracy of 99.3%**. As you can observe, the model has nearly saturated after only 10 epochs, so we can reduce the epochs to 10 and save computational resources.\n",
    "\n",
    "We'll use this model to predict the labels of the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 5: Testing the Model using the Test Set\n",
    "\n",
    "Now, we'll use the testing set to measure the accuracy of the model over unknown examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test set preprocessing\n",
    "X_test_preprocessed = preprocess(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy = 97.6%\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    VGGNet_Model.saver.restore(sess, os.path.join(DIR, \"VGGNet\"))\n",
    "    y_pred = VGGNet_Model.y_predict(X_test_preprocessed)\n",
    "    test_accuracy = sum(y_test == y_pred)/len(y_test)\n",
    "    print(\"Test Accuracy = {:.1f}%\".format(test_accuracy*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Accuracy = 97.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A remarkable performance!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll plot the confusion matrix to see where the model actually fails."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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omFkpDhpmVoqDhpmV4qBhZqU4aAwCkt6WNEvSPZIulvSuOtY1ueJOyc/0dUex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\nxUHDzEpx0DCzUv4/RZa047piXokAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa35dd43b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "cm = np.log(.0001 + cm)\n",
    "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.title('Log of normalized Confusion Matrix')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe some clusters in the confusion matrix above. It turns out that the various speed limits are sometimes misclassified among themselves. Similarly, traffic signs with traingular shape are misclassified among themselves. We can further improve on the model using hierarchical CNNs to first identify broader groups (like speed signs) and then have CNNs to classify finer features (such as the actual speed limit)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 6: Testing the Model on New Images\n",
    "\n",
    "In this step, we will use the model to predict traffic signs type of 5 random images of German traffic signs from the web our model's performance on these images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of new testing examples:  5\n"
     ]
    }
   ],
   "source": [
    "# Loading and resizing new test images\n",
    "new_test_images = []\n",
    "path = './traffic-signs-data/new_test_images/'\n",
    "for image in os.listdir(path):\n",
    "    img = cv2.imread(path + image)\n",
    "    img = cv2.resize(img, (32,32))\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    new_test_images.append(img)\n",
    "new_IDs = [13, 3, 14, 27, 17]\n",
    "print(\"Number of new testing examples: \", len(new_test_images))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying the new testing examples, with their respective ground-truth labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X/NoYwC/yPChjJgvOL2Pyw/llTH44v4zJD+eXMRNPO640xwHYE8BNAJCm6YMh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\nA4A+YWO5cYHnsDqNkHEN47WwSFzd5tr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4/i6qD6V6vdcpypY3y+L9maxNfvWVZ2PfLVvn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\nqao28bsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa35dc4e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 16))\n",
    "for i in range(len(new_test_images)):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(new_test_images[i])\n",
    "    plt.xlabel(signs[new_IDs[i]])\n",
    "    plt.ylabel(\"New testing image\")\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.tight_layout(pad=0, h_pad=0, w_pad=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These test images include some easy to predict signs, and other signs are considered hard for the model to predict.\n",
    "\n",
    "For instance, we have easy to predict signs like the \"Stop\" and the \"No entry\". The two signs are clear and belong to classes where the model can predict with  high accuracy.\n",
    "\n",
    "On the other hand, we have signs belong to classes where has poor accuracy, like the \"Speed limit\" sign, because as stated above it turns out that the various speed limits are sometimes misclassified among themselves, and the \"Pedestrians\" sign, because traffic signs with traingular shape are misclassified among themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# New test data preprocessing\n",
    "new_test_images_preprocessed = preprocess(np.asarray(new_test_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Images Test Accuracy = 100.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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by1m/osrFhqUVjCrp7vu06CN/vOL2PVxs9yE7B+29jxzlcl579F0X63DwsKBd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9QvfBa3MrlBoplz9BngpxExByAinmtxjgOpmfMTwLjs9XjgqRbmN+Ai4npg9jK67A+cEXW3AGtI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\nwbkt3/+SqzvsyGOD8kH//H7X5oYbf+Hqtt0uTD87Z/ZbXBsRE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa35d728ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def y_predict_model(Input_data, top_k=5):\n",
    "    \"\"\"\n",
    "    Generates the predictions of the model over the input data, and outputs the top softmax probabilities.\n",
    "        Parameters:\n",
    "            X_data: Input data.\n",
    "            top_k (Default = 5): The number of top softmax probabilities to be generated.\n",
    "    \"\"\"\n",
    "    num_examples = len(Input_data)\n",
    "    y_pred = np.zeros((num_examples, top_k), dtype=np.int32)\n",
    "    y_prob = np.zeros((num_examples, top_k))\n",
    "    with tf.Session() as sess:\n",
    "        VGGNet_Model.saver.restore(sess, os.path.join(DIR, \"VGGNet\"))\n",
    "        y_prob, y_pred = sess.run(tf.nn.top_k(tf.nn.softmax(VGGNet_Model.logits), k=top_k), \n",
    "                             feed_dict={x:Input_data, keep_prob:1, keep_prob_conv:1})\n",
    "    return y_prob, y_pred\n",
    "\n",
    "y_prob, y_pred = y_predict_model(new_test_images_preprocessed)\n",
    "\n",
    "test_accuracy = 0\n",
    "for i in enumerate(new_test_images_preprocessed):\n",
    "    accu = new_IDs[i[0]] == np.asarray(y_pred[i[0]])[0]\n",
    "    if accu == True:\n",
    "        test_accuracy += 0.2\n",
    "print(\"New Images Test Accuracy = {:.1f}%\".format(test_accuracy*100))\n",
    "\n",
    "plt.figure(figsize=(15, 16))\n",
    "new_test_images_len=len(new_test_images_preprocessed)\n",
    "for i in range(new_test_images_len):\n",
    "    plt.subplot(new_test_images_len, 2, 2*i+1)\n",
    "    plt.imshow(new_test_images[i]) \n",
    "    plt.title(signs[y_pred[i][0]])\n",
    "    plt.axis('off')\n",
    "    plt.subplot(new_test_images_len, 2, 2*i+2)\n",
    "    plt.barh(np.arange(1, 6, 1), y_prob[i, :])\n",
    "    labels = [signs[j] for j in y_pred[i]]\n",
    "    plt.yticks(np.arange(1, 6, 1), labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can notice from the top 5 softmax probabilities, the model has very high confidence (100%) when it comes to predict simple signs, like the \"Stop\" and the \"No entry\" sign, and even high confidence when predicting simple triangular signs in a very clear image, like the \"Yield\" sign.\n",
    "\n",
    "On the other hand, the model's confidence slightly reduces with more complex triangular sign in a \"pretty noisy\" image, in the \"Pedestrian\" sign image, we have a triangular sign with a shape inside it, and the images copyrights adds some noise to the image, the model was able to predict the true class, but with 80% confidence.\n",
    "\n",
    "And in the \"Speed limit\" sign, we can observe that the model accurately predicted that it's a \"Speed limit\" sign, but was somehow confused between the different speed limits. However, it predicted the true class at the end.\n",
    "\n",
    "The VGGNet model was able to predict the right class for each of the 5 new test images. Test Accuracy = 100.0%.\n",
    "In all cases, the model was very certain (80% - 100%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Using VGGNet, we've been able to reach a very high accuracy rate. We can observe that the models saturate after nearly 10 epochs, so we can save some computational resources and reduce the number of epochs to 10.\n",
    "We can also try other preprocessing techniques to further improve the model's accuracy..\n",
    "We can further improve on the model using hierarchical CNNs to first identify broader groups (like speed signs) and then have CNNs to classify finer features (such as the actual speed limit)\n",
    "This model will only work on input examples where the traffic signs are centered in the middle of the image. It doesn't have the capability to detect signs in the image corners."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
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