Traffic_Sign_Classifier (copy).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car\n",
    "\n",
    "## CNN Based Traffic Sign Recognition Classifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = \"train.p\"\n",
    "validation_file= \"valid.p\"\n",
    "testing_file = \"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)\n",
    "    \n",
    "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']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is array containing raw pixel data of the traffic sign images\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 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. **THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 39209\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\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",
    "# What's the shape of an traffic sign image?\n",
    "image_shape = X_train.shape[1:]\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(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import csv\n",
    "\n",
    "def plot_figures(figures, nrows = 1, ncols=1, labels=None):\n",
    "    fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(12, 14))\n",
    "    axs = axs.ravel()\n",
    "    for index, title in zip(range(len(figures)), figures):\n",
    "        axs[index].imshow(figures[title], plt.gray())\n",
    "        if(labels != None):\n",
    "            axs[index].set_title(labels[index])\n",
    "        else:\n",
    "            axs[index].set_title(title)\n",
    "            \n",
    "        axs[index].set_axis_off()\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    \n",
    "name_values = np.genfromtxt('signnames.csv', skip_header=1, dtype=[('myint','i8'), ('mysring','S55')], delimiter=',')\n",
    "    \n",
    "number_to_stop = 8\n",
    "figures = {}\n",
    "labels = {}\n",
    "for i in range(number_to_stop):\n",
    "    index = random.randint(0, n_train-1)\n",
    "    labels[i] = name_values[y_train[index]][1].decode('ascii')\n",
    "    figures[i] = X_train[index]\n",
    "    \n",
    "#plot_figures(figures, 4, 2, labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Personal Note</h1>\n",
    "Data appears good although occasionally for some reason the image cannot be displayed properly.  Maybe bad images in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61b4674908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61b3840a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61b3739470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_train, counts_train = np.unique(y_train, return_counts=True)\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1,43])\n",
    "\n",
    "plt.bar(unique_train, counts_train)\n",
    "plt.grid()\n",
    "plt.title(\"Train Dataset Sign Counts\")\n",
    "plt.savefig('./images/1.png')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "unique_test, counts_test = np.unique(y_test, return_counts=True)\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1,43])\n",
    "plt.bar(unique_test, counts_test)\n",
    "plt.grid()\n",
    "plt.title(\"Test Dataset Sign Counts\")\n",
    "plt.savefig('./images/2.png')\n",
    "plt.show()\n",
    "\n",
    "unique_valid, counts_valid = np.unique(y_valid, return_counts=True)\n",
    "plt.bar(unique_valid, counts_valid)\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1,43])\n",
    "plt.grid()\n",
    "plt.title(\"Valid Dataset Sign Counts\")\n",
    "plt.savefig('./images/3.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture\n",
    "- Generate fake data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Augumentation and greyscale the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anoop/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimention of RGB training set : \n",
      "(39209, 32, 32, 3)\n",
      "Dimention of GRAY training set : \n",
      "(39209, 32, 32, 1)\n",
      "Dimention of RGB test set : \n",
      "(12630, 32, 32, 3)\n",
      "Dimention of GRAY test set : \n",
      "(12630, 32, 32, 1)\n",
      "Dimention of RGB validation set : \n",
      "(4410, 32, 32, 3)\n",
      "Dimention of GRAY validation set : \n",
      "(4410, 32, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "### Preprocess the data here. Preprocessing steps could include normalization, converting to grayscale, etc.\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import flatten\n",
    "from math import ceil\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "# Convert to grayscale\n",
    "\n",
    "# Convert training data to greyscale\n",
    "X_train_rgb = X_train\n",
    "X_train_gray = np.sum(X_train/3, axis=3, keepdims=True)\n",
    "\n",
    "# Convert testing data to greyscale\n",
    "X_test_rgb = X_test\n",
    "X_test_gray = np.sum(X_test/3, axis=3, keepdims=True)\n",
    "\n",
    "# Convert validation data to greyscale\n",
    "X_valid_rgb = X_valid\n",
    "X_valid_gray = np.sum(X_valid/3, axis=3, keepdims=True)\n",
    "\n",
    "# Display Shapes\n",
    "print(\"Dimention of RGB training set : \")\n",
    "print(X_train_rgb.shape)\n",
    "print(\"Dimention of GRAY training set : \")\n",
    "print(X_train_gray.shape)\n",
    "###################################################################################################################\n",
    "print(\"Dimention of RGB test set : \")\n",
    "print(X_test_rgb.shape)\n",
    "print(\"Dimention of GRAY test set : \")\n",
    "print(X_test_gray.shape)\n",
    "###################################################################################################################\n",
    "print(\"Dimention of RGB validation set : \")\n",
    "print(X_valid_rgb.shape)\n",
    "print(\"Dimention of GRAY validation set : \")\n",
    "print(X_valid_gray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the Greyscale images as the training, testing and validation data\n",
    "\n",
    "X_train = X_train_gray\n",
    "X_test = X_test_gray\n",
    "X_valid = X_valid_gray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test the data availabe so that we can see that data had been greyscaled\n",
    "image_depth_channels = X_train.shape[3]\n",
    "\n",
    "number_to_stop = 8\n",
    "figures = {}\n",
    "random_signs = []\n",
    "for i in range(number_to_stop):\n",
    "    index = random.randint(0, n_train-1)\n",
    "    labels[i] = name_values[y_train[index]][1].decode('ascii')\n",
    "    figures[i] = X_train[index].squeeze()\n",
    "    random_signs.append(index)\n",
    "    \n",
    "#plot_figures(figures, int(number_to_stop/2), 2, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augumentation (Make Duplicate data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "more_X_train = []\n",
    "more_y_train = []\n",
    "\n",
    "more2_X_train = []\n",
    "more2_y_train = []\n",
    "\n",
    "new_counts_train = counts_train\n",
    "#print(new_counts_train)\n",
    "for i in range(n_train):\n",
    "    if(new_counts_train[y_train[i]] < 3000):\n",
    "        for j in range(3):\n",
    "            # cv2.warpAffine crops the input image \n",
    "            dx, dy = np.random.randint(-1.7, 1.8, 2)\n",
    "            M = np.float32([[1, 0, dx], [0, 1, dy]])\n",
    "            dst = cv2.warpAffine(X_train[i], M, (X_train[i].shape[0], X_train[i].shape[1]))\n",
    "            dst = dst[:,:,None]\n",
    "            more_X_train.append(dst)\n",
    "            more_y_train.append(y_train[i])\n",
    "\n",
    "            #cv2.getPerspectiveTransform ,transforms and saves\n",
    "            random_higher_bound = random.randint(27, 32)\n",
    "            random_lower_bound = random.randint(0, 5)\n",
    "            points_one = np.float32([[0,0],[32,0],[0,32],[32,32]])\n",
    "            points_two = np.float32([[0, 0], [random_higher_bound, random_lower_bound], [random_lower_bound, 32],[32, random_higher_bound]])\n",
    "            M = cv2.getPerspectiveTransform(points_one, points_two)\n",
    "            dst = cv2.warpPerspective(X_train[i], M, (32,32))\n",
    "            more2_X_train.append(dst)\n",
    "            more2_y_train.append(y_train[i])\n",
    "            \n",
    "            #cv2.getRotationMatrix2D rotates the image\n",
    "            tilt = random.randint(-12, 12)\n",
    "            M = cv2.getRotationMatrix2D((X_train[i].shape[0]/2, X_train[i].shape[1]/2), tilt, 1)\n",
    "            dst = cv2.warpAffine(X_train[i], M, (X_train[i].shape[0], X_train[i].shape[1]))\n",
    "            more2_X_train.append(dst)\n",
    "            more2_y_train.append(y_train[i])\n",
    "            \n",
    "            new_counts_train[y_train[i]] += 2\n",
    "    \n",
    "more_X_train = np.array(more_X_train)\n",
    "more_y_train = np.array(more_y_train)\n",
    "X_train = np.concatenate((X_train, more_X_train), axis=0)\n",
    "y_train = np.concatenate((y_train, more_y_train), axis=0)\n",
    "\n",
    "more2_X_train = np.array(more_X_train)\n",
    "more2_y_train = np.array(more_y_train)\n",
    "more2_X_train = np.reshape(more2_X_train, (np.shape(more2_X_train)[0], 32, 32, 1))\n",
    "X_train = np.concatenate((X_train, more2_X_train), axis=0)\n",
    "y_train = np.concatenate((y_train, more2_y_train), axis=0)\n",
    "\n",
    "X_train = np.concatenate((X_train, X_valid), axis=0)\n",
    "y_train = np.concatenate((y_train, y_valid), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Dataset Size : 94444\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61826e8748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61826e86d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1hAWcJEmSJLWEBZwkSZIktYQFnCRJkiS1hAWcJEmSJLWEBZwkSZIktYQFnCRJkiS1hAWcJEmSJLVEXwVcku1Jtia5Pcnmpu3YJLck+ULz9ZimPUn+LMm2JHckec5cvgBJkiRJGhWHz6Dvqqr6atf+OuDWqlqfZF2zfwVwLnBq8+d5wNuar5IkSZL0CMvX3dTz+GUrDvDKHn22rz9/0CEtWLNZQrka2NBsbwAu6Gq/tjpuA5YmOWkW55EkSZIkAamq6Tsl/wLsAQp4e1W9I8neqlra1WdPVR2T5MPA+qr6eNN+K3BFVW2eMOZaYC3A2NjY6Rs3bhzIC9q/fz9Llizpu//We/cd8rlWLDvasbqMHQm7H1x4cY3CWObe3C+ksQZpurh65X+YcfUyirlv62vsxdzPzkL9NzSb3E8cbzZxTRbboLQ1rpm+37bRqlWrtlTV+HT9+i3gnlBV9yU5AbgFeA1w4xQF3E3AlRMKuMurastU44+Pj9fmzZunOjwjmzZtYuXKlX33n266tpeJU7WjPtZlKw5w1dapV+Uuhte4UMcy9+Z+IY01SP0sqZkq/8OMq5dRzH1bX2Mv5n52Fuq/odnkfuJ4s4lrstgGpa1xzfT9to2S9FXA9bWEsqrua77eD1wPnAHsPrg0svl6f9N9J3BK17efDNzXf+iSJEmSpMlMW8AleWySow5uAz8LfA64EVjTdFsD3NBs3wi8onka5ZnAvqraNfDIJUmSJGnE9PMUyjHg+iQH+7+3qv4uyaeA65JcDHwJuLDpfzNwHrANeAC4aOBRS5IkSdIImraAq6p7gGdO0v414KxJ2gu4ZCDRSZIkSZK+ZzYfIyBJkiRJmkcWcJIkSZLUEhZwkiRJktQSFnCSJEmS1BIWcJIkSZLUEhZwkiRJktQSFnCSJEmS1BIWcJIkSZLUEhZwkiRJktQSFnCSJEmS1BIWcJIkSZLUEocPOwBJkubD8nU3HfL3bl9//gAjkSTp0DkDJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS3RdwGX5LAkn0ny4Wb/SUk+meQLSd6f5Iim/dHN/rbm+PK5CV2SJEmSRstMZuAuBe7q2n8TcHVVnQrsAS5u2i8G9lTVU4Crm36SJEmSpFnqq4BLcjJwPvAXzX6AFwIfaLpsAC5otlc3+zTHz2r6S5IkSZJmod8ZuDcDlwPfbfaPA/ZW1YFmfyewrNleBuwAaI7va/pLkiRJkmYhVdW7Q/Ii4Lyq+vUkK4HfAi4CPtEskyTJKcDNVbUiyZ3A2VW1szn2ReCMqvrahHHXAmsBxsbGTt+4ceNAXtD+/ftZsmRJ3/233rvvkM+1YtnRjtVl7EjY/eDCi2sUxjL35n4hjTVI08XVK/9tyZe5722Yr7GXYV4T5n5h5n7ieLOJa7LYBqWtcc30/baNVq1ataWqxqfr108BdyXwcuAA8BjgccD1wNnAiVV1IMnzgddX1dlJPtJsfyLJ4cCXgeOrx4nGx8dr8+bN/b62njZt2sTKlSv77r983U2HfK7t6893rC6XrTjAVVsPX3BxjcJY5t7cL6SxBmm6uHrlvy35Mve9DfM19jLMa8LcL8zcTxxvNnFNFtugDDKu+Rxrpu+3bZSkrwJu2iWUVfU7VXVyVS0HXgp8tKp+CfgY8OKm2xrghmb7xmaf5vhHexVvkiRJkqT+zOZz4K4AXptkG5173K5p2q8BjmvaXwusm12IkiRJkiSAqechJ1FVm4BNzfY9wBmT9Pk2cOEAYpMkSZIkdZnNDJwkSZIkaR5ZwEmSJElSS8xoCaUkSVq4FuoTByVJg+MMnCRJkiS1hAWcJEmSJLWEBZwkSZIktYQFnCRJkiS1hAWcJEmSJLWEBZwkSZIktYQFnCRJkiS1hAWcJEmSJLWEBZwkSZIktYQFnCRJkiS1hAWcJEmSJLWEBZwkSZIktcThww5AkiRJC9/ydTcd8vduX3/+ACORRpszcJIkSZLUEhZwkiRJktQSLqGUJEmStGjMZrkvLPwlv87ASZIkSVJLWMBJkiRJUku4hFKSJLWGT0KUNOqcgZMkSZKklrCAkyRJkqSWsICTJEmSpJZo3T1w0619v2zFAV7Zo4/r3yVJkiS1lTNwkiRJktQSFnCSJEmS1BKtW0IpSZIkSfNhNh9dAnNz+9a0M3BJHpPkn5J8NsmdSf6gaX9Skk8m+UKS9yc5oml/dLO/rTm+fOBRS5IkSdII6mcJ5UPAC6vqmcCzgHOSnAm8Cbi6qk4F9gAXN/0vBvZU1VOAq5t+kiRJkqRZmraAq479ze6jmj8FvBD4QNO+Abig2V7d7NMcPytJBhaxJEmSJI2ovh5ikuSwJLcD9wO3AF8E9lbVgabLTmBZs70M2AHQHN8HHDfIoCVJkiRpFKWq+u+cLAWuB34feFezTJIkpwA3V9WKJHcCZ1fVzubYF4EzquprE8ZaC6wFGBsbO33jxo19xbD13n09j48dCbsfnPr4imVHz2i8Xhzr4cy9uR/Fscz9I8capNn8zG9Lvto6lrk396M41kx+5s8mrsliG5RBxjWfYw0r9/P597hq1aotVTU+Xb8ZFXAASV4HPABcAZxYVQeSPB94fVWdneQjzfYnkhwOfBk4vnqcaHx8vDZv3tzX+fv5IO+rtk79cM2JT4KZzZNlHOvhzL25H8WxzP3cPGHroNn8zG9Lvto6lrk396M41kx+5i/EpxfCYOOaz7GGlfv5/HtM0lcB189TKI9vZt5IciTw08BdwMeAFzfd1gA3NNs3Nvs0xz/aq3iTJEmSJPWnn8+BOwnYkOQwOgXfdVX14SSfBzYm+UPgM8A1Tf9rgL9Msg34OvDSOYhbkrRALdTZPEmSFoNpC7iqugN49iTt9wBnTNL+beDCgUQnSZIkjbiFuhxTw9HXUyglSZIkScNnASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLTFtAZfklCQfS3JXkjuTXNq0H5vkliRfaL4e07QnyZ8l2ZbkjiTPmesXIUmSJEmjoJ8ZuAPAZVX1NOBM4JIkTwfWAbdW1anArc0+wLnAqc2ftcDbBh61JEmSJI2gaQu4qtpVVZ9utr8J3AUsA1YDG5puG4ALmu3VwLXVcRuwNMlJA49ckiRJkkbMjO6BS7IceDbwSWCsqnZBp8gDTmi6LQN2dH3bzqZNkiRJkjQLqar+OiZLgP8NvLGq/jbJ3qpa2nV8T1Udk+Qm4Mqq+njTfitweVVtmTDeWjpLLBkbGzt948aNfcWx9d59PY+PHQm7H5z6+IplR89ovF4c6+HMvbkfxbHM/XDH6pX/xfIaF+pY5t7cj+JYM/mZP5u4HOuRFkPup7Nq1aotVTU+Xb++CrgkjwI+DHykqv60absbWFlVu5olkpuq6rQkb2+23zex31Tjj4+P1+bNm/t6YcvX3dTz+GUrDnDV1sOnPL59/fkzGq8Xx3o4c2/uR3Escz/csXrlf7G8xoU6lrk396M41kx+5s8mLsd6pMWQ++kk6auA6+cplAGuAe46WLw1bgTWNNtrgBu62l/RPI3yTGBfr+JNkiRJktSfqcvY73sB8HJga5Lbm7bfBdYD1yW5GPgScGFz7GbgPGAb8ABw0UAjliRJkqQRNW0B19zLlikOnzVJ/wIumWVckiRJkqQJZvQUSkmSJEnS8FjASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktYwEmSJElSS1jASZIkSVJLWMBJkiRJUktMW8AleWeS+5N8rqvt2CS3JPlC8/WYpj1J/izJtiR3JHnOXAYvSZIkSaOknxm4dwPnTGhbB9xaVacCtzb7AOcCpzZ/1gJvG0yYkiRJkqRpC7iq+gfg6xOaVwMbmu0NwAVd7ddWx23A0iQnDSpYSZIkSRplh3oP3FhV7QJovp7QtC8DdnT129m0SZIkSZJmKVU1fadkOfDhqnpGs7+3qpZ2Hd9TVcckuQm4sqo+3rTfClxeVVsmGXMtnWWWjI2Nnb5x48a+At56776ex8eOhN0PTn18xbKjZzReL471cObe3I/iWOZ+uGP1yv9ieY0LdSxzb+5HcayZ/MyfTVyO9UiLIffTWbVq1ZaqGp+u36EWcHcDK6tqV7NEclNVnZbk7c32+yb26zX++Ph4bd68edo4AJavu6nn8ctWHOCqrYdPeXz7+vNnNF4vjvVw5t7cj+JY5n64Y/XK/2J5jQt1LHNv7kdxrJn8zJ9NXI71SIsh99NJ0lcBd6hLKG8E1jTba4Abutpf0TyN8kxg33TFmyRJkiSpP1OXsY0k7wNWAo9PshN4HbAeuC7JxcCXgAub7jcD5wHbgAeAi+YgZkmSJEkaSdMWcFX1sikOnTVJ3wIumW1QkiRJkqRHOtQllJIkSZKkeWYBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktYQEnSZIkSS1hASdJkiRJLWEBJ0mSJEktMScFXJJzktydZFuSdXNxDkmSJEkaNQMv4JIcBrwVOBd4OvCyJE8f9HkkSZIkadTMxQzcGcC2qrqnqr4DbARWz8F5JEmSJGmkzEUBtwzY0bW/s2mTJEmSJM1CqmqwAyYXAmdX1a80+y8Hzqiq10zotxZY2+yeBtw9oBAeD3x1QGNpZsz98Jj74TH3w2X+h8fcD4+5Hx5zPzyjkPsnVtXx03U6fA5OvBM4pWv/ZOC+iZ2q6h3AOwZ98iSbq2p80ONqeuZ+eMz98Jj74TL/w2Puh8fcD4+5Hx5z/31zsYTyU8CpSZ6U5AjgpcCNc3AeSZIkSRopA5+Bq6oDSV4NfAQ4DHhnVd056PNIkiRJ0qiZiyWUVNXNwM1zMXYfBr4sU30z98Nj7ofH3A+X+R8ecz885n54zP3wmPvGwB9iIkmSJEmaG3NxD5wkSZIkaQ4sqgIuyTlJ7k6yLcm6YcczSpJsT7I1ye1JNg87nsUsyTuT3J/kc11txya5JckXmq/HDDPGxWqK3L8+yb3NtX97kvOGGeNileSUJB9LcleSO5Nc2rR77c+xHrn32p9jSR6T5J+SfLbJ/R807U9K8snmun9/89A4DVCP3L87yb90XffPGnasi1WSw5J8JsmHm32v+8aiKeCSHAa8FTgXeDrwsiRPH25UI2dVVT3LR7zOuXcD50xoWwfcWlWnArc2+xq8d/PI3ANc3Vz7z2ruAdbgHQAuq6qnAWcClzQ/4732595UuQev/bn2EPDCqnom8CzgnCRnAm+ik/tTgT3AxUOMcbGaKvcAv9113d/O13dvAAADVUlEQVQ+vBAXvUuBu7r2ve4bi6aAA84AtlXVPVX1HWAjsHrIMUkDV1X/AHx9QvNqYEOzvQG4YF6DGhFT5F7zoKp2VdWnm+1v0nlTX4bX/pzrkXvNserY3+w+qvlTwAuBDzTtXvdzoEfuNQ+SnAycD/xFsx+87r9nMRVwy4AdXfs78Q1mPhXw90m2JFk77GBG0FhV7YLOf7aAE4Ycz6h5dZI7miWWLuGbY0mWA88GPonX/ryakHvw2p9zzTKy24H7gVuALwJ7q+pA08X/78yRibmvqoPX/Rub6/7qJI8eYoiL2ZuBy4HvNvvH4XX/PYupgMskbf6mZP68oKqeQ2cJ6yVJfnLYAUnz5G3AD9NZYrMLuGq44SxuSZYAfwP8ZlV9Y9jxjJJJcu+1Pw+q6t+r6lnAyXRWGz1tsm7zG9VomJj7JM8Afgd4KvBc4FjgiiGGuCgleRFwf1Vt6W6epOvIXveLqYDbCZzStX8ycN+QYhk5VXVf8/V+4Ho6bzKaP7uTnATQfL1/yPGMjKra3bzJfxf4c7z250ySR9EpIN5TVX/bNHvtz4PJcu+1P7+qai+wic59iEuTHPwsX/+/M8e6cn9Os6S4quoh4F143c+FFwA/n2Q7nVuiXkhnRs7rvrGYCrhPAac2T6g5AngpcOOQYxoJSR6b5KiD28DPAp/r/V0asBuBNc32GuCGIcYyUg4WD43/iNf+nGjuf7gGuKuq/rTrkNf+HJsq9177cy/J8UmWNttHAj9N5x7EjwEvbrp53c+BKXL/z12/MAqde7C87gesqn6nqk6uquV0/j//0ar6Jbzuv2dRfZB38wjjNwOHAe+sqjcOOaSRkOTJdGbdAA4H3mvu506S9wErgccDu4HXAR8ErgN+CPgScGFV+bCNAZsi9yvpLCErYDvwawfvydLgJPlx4P8AW/n+PRG/S+deLK/9OdQj9y/Da39OJflROg9rOIzOL92vq6o3NO+7G+ks4fsM8MvNjJAGpEfuPwocT2dJ3+3Aq7oedqIBS7IS+K2qepHX/fctqgJOkiRJkhazxbSEUpIkSZIWNQs4SZIkSWoJCzhJkiRJagkLOEmSJElqCQs4SZIkSWoJCzhJkiRJagkLOEmSJElqCQs4SZIkSWqJ/w+I9i/i3QDMjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617d9c77b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=0)\n",
    "\n",
    "print(\"New Dataset Size : {}\".format(X_train.shape[0]))\n",
    "\n",
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "\n",
    "\n",
    "# Plot the histogram\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1,43])\n",
    "#\n",
    "plt.bar(unique, counts)\n",
    "plt.grid()\n",
    "plt.title(\"Train Dataset Sign Counts\")\n",
    "plt.savefig('./images/4.png')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "unique, counts = np.unique(y_test, return_counts=True)\n",
    "\n",
    "# Plot the histogram\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1,43])\n",
    "#\n",
    "\n",
    "plt.bar(unique, counts)\n",
    "plt.grid()\n",
    "plt.title(\"Test Dataset Sign Counts\")\n",
    "plt.savefig('./images/5.png')\n",
    "plt.show()\n",
    "\n",
    "unique, counts = np.unique(y_valid, return_counts=True)\n",
    "\n",
    "# Plot the histogram\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1,43])\n",
    "#\n",
    "\n",
    "plt.bar(unique, counts)\n",
    "plt.grid()\n",
    "plt.title(\"Valid Dataset Sign Counts\")\n",
    "plt.savefig('./images/6.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(im):\n",
    "    return -np.log(1/((1 + im)/257) - 1)\n",
    "\n",
    "X_train_normalized = X_train/127.5-1\n",
    "X_test_normalized = X_test/127.5-1\n",
    "\n",
    "number_to_stop = 8\n",
    "figures = {}\n",
    "count = 0\n",
    "for i in random_signs:\n",
    "    labels[count] = name_values[y_train[i]][1].decode('ascii')\n",
    "    figures[count] = X_train_normalized[i].squeeze()\n",
    "    count += 1;\n",
    "    \n",
    "#plot_figures(figures, int(number_to_stop/2), 2, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get normalized dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train_normalized\n",
    "X_test = X_test_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "My final model consisted of the following layers:\n",
    "\n",
    "|      Layer       \t\t|     Description\t \t\t\t\t\t\t\t|\n",
    "|:---------------------:|:---------------------------------------------:|\n",
    "| Input         \t\t| 32x32x1 grayscale image   \t\t\t\t\t|\n",
    "| Convolution 5x5     \t| 2x2 stride, valid padding, outputs 28x28x6 \t|\n",
    "| RELU\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t|\n",
    "| Max pooling\t      \t| 2x2 stride,  outputs 14x14x6 \t\t\t\t\t|\n",
    "| Convolution 5x5\t    | 2x2 stride, valid padding, outputs 10x10x16\t|\n",
    "| RELU\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t|\n",
    "| Max pooling\t      \t| 2x2 stride,  outputs 5x5x16 \t\t\t\t\t|\n",
    "| Convolution 1x1\t    | 2x2 stride, valid padding, outputs 1x1x412    |\n",
    "| RELU\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t|\n",
    "| Fully connected\t\t| input 412, output 122        \t\t\t\t\t|\n",
    "| RELU\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t|\n",
    "| Dropout\t\t\t\t| 50% keep        \t\t\t\t\t\t\t\t|\n",
    "| Fully connected\t\t| input 122, output 84 \t\t\t\t\t\t\t|\n",
    "| RELU\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t|\n",
    "| Dropout\t\t\t\t| 50% keep \t\t\t\t\t\t\t\t\t\t|\n",
    "| Fully connected\t\t| input 84, output 43\t\t\t\t\t\t\t|\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define basic property of a layer\n",
    "def conv2d(x, W, b, strides=1):\n",
    "    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='VALID')\n",
    "    x = tf.nn.bias_add(x, b)\n",
    "    print(x.shape)\n",
    "    return tf.nn.relu(x)\n",
    "\n",
    "def LeNet(x):\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    W_one = tf.Variable(tf.truncated_normal(shape=(5, 5, image_depth_channels, 6), mean = mu, stddev = sigma))\n",
    "    b_one = tf.Variable(tf.zeros(6))\n",
    "    layer_one = conv2d(x, W_one, b_one, 1)\n",
    "    \n",
    "    layer_one = tf.nn.max_pool(layer_one, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    print(layer_one.shape)\n",
    "    print()\n",
    "    \n",
    "    W_two = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n",
    "    b_two = tf.Variable(tf.zeros(16))\n",
    "    layer_two = conv2d(layer_one, W_two, b_two, 1)\n",
    "\n",
    "    layer_two = tf.nn.max_pool(layer_two, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    print(layer_two.shape)\n",
    "    print()\n",
    "    \n",
    "    W_two_a = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 412), mean = mu, stddev = sigma))\n",
    "    b_two_a = tf.Variable(tf.zeros(412))\n",
    "    layer_two_a = conv2d(layer_two, W_two_a, b_two_a, 1)\n",
    "    print(layer_two_a.shape)\n",
    "    print()\n",
    "    \n",
    "    flat = flatten(layer_two_a)\n",
    "\n",
    "    W_three = tf.Variable(tf.truncated_normal(shape=(412, 122), mean = mu, stddev = sigma))\n",
    "    b_three = tf.Variable(tf.zeros(122))\n",
    "    layer_three = tf.nn.relu(tf.nn.bias_add(tf.matmul(flat, W_three), b_three))\n",
    "    layer_three = tf.nn.dropout(layer_three, keep_prob)\n",
    "    \n",
    "    W_four = tf.Variable(tf.truncated_normal(shape=(122, 84), mean = mu, stddev = sigma))\n",
    "    b_four = tf.Variable(tf.zeros(84))\n",
    "    layer_four = tf.nn.relu(tf.nn.bias_add(tf.matmul(layer_three, W_four), b_four))\n",
    "    layer_four = tf.nn.dropout(layer_four, keep_prob)\n",
    "    \n",
    "    W_five = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\n",
    "    b_five = tf.Variable(tf.zeros(43))\n",
    "    layer_five = tf.nn.bias_add(tf.matmul(layer_four, W_five), b_five)\n",
    "    \n",
    "    return layer_five\n",
    "\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, image_depth_channels))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y = tf.one_hot(y, 43)\n",
    "\n",
    "keep_prob = tf.placeholder(tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN Structure details \n",
      "(?, 28, 28, 6)\n",
      "(?, 14, 14, 6)\n",
      "\n",
      "(?, 10, 10, 16)\n",
      "(?, 5, 5, 16)\n",
      "\n",
      "(?, 1, 1, 412)\n",
      "(?, 1, 1, 412)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "\n",
    "EPOCHS = 40\n",
    "BATCH_SIZE = 256\n",
    "train=0\n",
    "rate = 0.00097\n",
    "print(\"CNN Structure details \")\n",
    "\n",
    "##CALL CNN##\n",
    "logits = LeNet(x)\n",
    "############\n",
    "\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=one_hot_y)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "def evaluate(X_data, y_data):\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(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if train==1:\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        num_examples = len(X_train)\n",
    "\n",
    "        print(\"Training...\")\n",
    "        print()\n",
    "        validation_accuracy_figure = []\n",
    "        test_accuracy_figure = []\n",
    "        for i in range(EPOCHS):\n",
    "            X_train, y_train = shuffle(X_train, y_train)\n",
    "            for offset in range(0, num_examples, BATCH_SIZE):\n",
    "                end = offset + BATCH_SIZE\n",
    "                batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "                sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})\n",
    "\n",
    "            validation_accuracy = evaluate(X_valid, y_valid)\n",
    "            validation_accuracy_figure.append(validation_accuracy)\n",
    "\n",
    "            test_accuracy = evaluate(X_train, y_train)\n",
    "            test_accuracy_figure.append(test_accuracy)\n",
    "            print(\"EPOCH {} ...\".format(i+1))\n",
    "            print(\"Test Accuracy = {:.3f}\".format(test_accuracy))\n",
    "            print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "            print()\n",
    "\n",
    "        saver.save(sess, './lenet')\n",
    "        print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "if train==1:\n",
    "    plt.plot(test_accuracy_figure)\n",
    "    plt.title(\"Test Accuracy\")\n",
    "    plt.savefig('./images/8.png')\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(validation_accuracy_figure)\n",
    "    plt.title(\"Validation Accuracy\")\n",
    "    plt.savefig('./images/9.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display Accuracy on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Train Accuracy = 1.000\n",
      "Valid Accuracy = 0.941\n",
      "Test Accuracy = 0.943\n"
     ]
    }
   ],
   "source": [
    "if train==0:\n",
    "    with tf.Session() as sess:\n",
    "        saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "\n",
    "        train_accuracy = evaluate(X_train, y_train)\n",
    "        print(\"Train Accuracy = {:.3f}\".format(train_accuracy))\n",
    "\n",
    "        valid_accuracy = evaluate(X_valid, y_valid)\n",
    "        print(\"Valid Accuracy = {:.3f}\".format(valid_accuracy))    \n",
    "\n",
    "        test_accuracy = evaluate(X_test, y_test)\n",
    "        print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import cv2\n",
    "\n",
    "my_images = sorted(glob.glob('./mysigns/*.png'))\n",
    "my_labels = np.array([1, 22, 35, 15, 37, 18,40,17])\n",
    "\n",
    "figures = {}\n",
    "labels = {}\n",
    "my_signs = []\n",
    "index = 0\n",
    "#count=0\n",
    "for my_image in my_images:\n",
    "    #count=count+1\n",
    "    img = cv2.cvtColor(cv2.imread(my_image), cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "    my_signs.append(img)\n",
    "    figures[index] = img\n",
    "    labels[index] = name_values[my_labels[index]][1].decode('ascii')\n",
    "    index += 1\n",
    "\n",
    "#plot_figures(figures, 4, 2, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_signs = np.array(my_signs)\n",
    "my_signs_gray = np.sum(my_signs/3, axis=3, keepdims=True)\n",
    "my_signs_normalized = my_signs_gray/127.5-1\n",
    "\n",
    "number_to_stop = 8\n",
    "figures = {}\n",
    "labels = {}\n",
    "for i in range(number_to_stop):\n",
    "    labels[i] = name_values[my_labels[i]][1].decode('ascii')\n",
    "    figures[i] = my_signs_gray[i].squeeze()\n",
    "    \n",
    "#plot_figures(figures,int(number_to_stop/2), 2, labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "My Data Set Accuracy = 1.000\n"
     ]
    }
   ],
   "source": [
    "### Run the predictions here and use the model to output the prediction for each image.\n",
    "### Make sure to pre-process the images with the same pre-processing pipeline used earlier.\n",
    "\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    saver.restore(sess, \"./lenet\")\n",
    "    my_accuracy = evaluate(my_signs_normalized, my_labels)\n",
    "    print(\"My Data Set Accuracy = {:.3f}\".format(my_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 1\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 2\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 3\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 4\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 5\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 6\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 7\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 8\n",
      "Image Accuracy = 1.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "my_single_item_array = []\n",
    "my_single_item_label_array = []\n",
    "\n",
    "for i in range(number_to_stop):\n",
    "    my_single_item_array.append(my_signs_normalized[i])\n",
    "    my_single_item_label_array.append(my_labels[i])\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "\n",
    "        saver.restore(sess, \"./lenet\")\n",
    "        my_accuracy = evaluate(my_single_item_array, my_single_item_label_array)\n",
    "        print('Image {}'.format(i+1))\n",
    "        print(\"Image Accuracy = {:.3f}\".format(my_accuracy))\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617d8934e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617d4cb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617c6e2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6178432a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6171e4ed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f616ffb5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617d959d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f615627cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "### Feel free to use as many code cells as needed.\n",
    "k_size = 5\n",
    "softmax_logits = tf.nn.softmax(logits)\n",
    "top_k = tf.nn.top_k(softmax_logits, k=k_size)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    saver.restore(sess, \"./lenet\")\n",
    "    my_softmax_logits = sess.run(softmax_logits, feed_dict={x: my_signs_normalized, keep_prob: 1.0})\n",
    "    my_top_k = sess.run(top_k, feed_dict={x: my_signs_normalized, keep_prob: 1.0})\n",
    "\n",
    "\n",
    "    for i in range(number_to_stop):\n",
    "        figures = {}\n",
    "        labels = {}\n",
    "        \n",
    "        figures[0] = my_signs[i]\n",
    "        labels[0] = \"Original\"\n",
    "        \n",
    "        for j in range(k_size):\n",
    "\n",
    "            labels[j+1] = 'Guess {} : ({:.0f}%)'.format(j+1, 100*my_top_k[0][i][j])\n",
    "            figures[j+1] = X_valid[np.argwhere(y_valid == my_top_k[1][i][j])[0]].squeeze()\n",
    "            \n",
    "\n",
    "        plot_figures(figures, 1, 6, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}