Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "## Deep Learning\n",
    "\n",
    "## Project: Build a Traffic Sign Recognition Classifier\n",
    "\n",
    "In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary. \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission. \n",
    "\n",
    "In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) that can be used to guide the writing process. Completing the code template and writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/481/view) for this project.\n",
    "\n",
    "The [rubric](https://review.udacity.com/#!/rubrics/481/view) contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this Ipython notebook and also discuss the results in the writeup file.\n",
    "\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "# DONE: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "data_path = str(Path(os.getcwd()).parent) + \"/data/\"\n",
    "training_file = data_path + \"train.p\"\n",
    "validation_file = data_path + \"valid.p\"\n",
    "testing_file = data_path + \"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",
    "## Step 1: 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. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**\n",
    "\n",
    "Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the [pandas shape method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html) might be useful for calculating some of the summary results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of valid examples = 4410\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "import numpy as np\n",
    "\n",
    "# Done: Number of training examples\n",
    "n_train = len(X_train)\n",
    "\n",
    "# Done: Number of validation examples\n",
    "n_validation = len(X_valid)\n",
    "\n",
    "# Done: Number of testing examples.\n",
    "n_test = len(X_test)\n",
    "\n",
    "# Done: What's the shape of an traffic sign image?\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# Done: 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 valid examples =\", n_validation)\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": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc. \n",
    "\n",
    "The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
    "\n",
    "**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the traffic signs labels from CSV file and print the first 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)', 'No passing']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "labels = []\n",
    "with open('signnames.csv', 'r') as file:\n",
    "    reader = csv.reader(file)\n",
    "    for label in reader:\n",
    "        labels.append(label[1])\n",
    "labels = labels[1:] # remove header\n",
    "\n",
    "print(labels[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5bdebd9e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "index = random.randint(0, len(X_train))\n",
    "image = X_train[index].squeeze()\n",
    "\n",
    "rows = 7\n",
    "cols = 7\n",
    "\n",
    "fig, axs = plt.subplots(rows, cols)\n",
    "fig.set_size_inches(25, 25)\n",
    "\n",
    "for i in range(n_classes):\n",
    "    class_images = X_train[y_train == i]\n",
    "    index = index = random.randint(0, len(class_images))\n",
    "    image = class_images[index]\n",
    "    \n",
    "    axs[int(i / rows)][i % cols].imshow(image)\n",
    "    axs[int(i / rows)][i % cols].set_title(labels[i], fontsize=12)  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram Distribution\n",
    "This section explores the distribution of the classes in the training, test and validation dataset.\n",
    "\n",
    "From the graphs, we see that the distribution in all datasets are almost the same, there is a variation in the distribution from one class to another is large.\n",
    "\n",
    "Data normalisation would be used to mitigate this challenge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum class labels in train data 2010.0\n",
      "Minimum class labels in test data 180.0 \n",
      "\n",
      "Maximum class labels in validation data 240.0\n",
      "Mininum class labels in validation data 30.0 \n",
      "\n",
      "Maximum class labels in test data 750.0\n",
      "Minimum class labels in test data 60.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py:1999: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5bcf782240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset visualisation\n",
    "fig, axs = plt.subplots(1, 3, sharey=False, tight_layout=True, figsize=[15,5])\n",
    "\n",
    "axs[0].set_title('Train classes distribution', fontsize=11)\n",
    "axs[1].set_title('Validation classes distribution', fontsize=11)\n",
    "axs[2].set_title('Test classes distribution', fontsize=11)\n",
    "\n",
    "x_train_hist, y_train_hist, _ = axs[0].hist(y_train, bins=n_classes)\n",
    "x_valid_hist, y_valid_hist, _ = axs[1].hist(y_valid, bins=n_classes)\n",
    "x_test_hist, y_test_hist, _ = axs[2].hist(y_test, bins=n_classes)\n",
    "\n",
    "print(\"Maximum class labels in train data\",x_train_hist.max())\n",
    "print(\"Minimum class labels in test data\",x_train_hist.min(),'\\n')\n",
    "\n",
    "print(\"Maximum class labels in validation data\",x_valid_hist.max())\n",
    "print(\"Mininum class labels in validation data\",x_valid_hist.min(),'\\n')\n",
    "\n",
    "print(\"Maximum class labels in test data\",x_test_hist.max())\n",
    "print(\"Minimum class labels in test data\",x_test_hist.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "The LeNet-5 implementation shown in the [classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play! \n",
    "\n",
    "With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission. \n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture (is the network over or underfitting?)\n",
    "- Play around preprocessing techniques (normalization, rgb to grayscale, etc)\n",
    "- Number of examples per label (some have more than others).\n",
    "- Generate fake data.\n",
    "\n",
    "Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data Set (normalization, grayscale, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, `(pixel - 128)/ 128` is a quick way to approximately normalize the data and can be used in this project. \n",
    "\n",
    "Other pre-processing steps are optional. You can try different techniques to see if it improves performance. \n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yield\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5bcde07080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5bcde07e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include \n",
    "### converting to grayscale, etc.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import numpy as np\n",
    "import cv2\n",
    "import random\n",
    "\n",
    "def gray_and_equalize(img):\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
    "    img = clahe.apply(img)\n",
    "    \n",
    "    return img\n",
    "\n",
    "\n",
    "index = 22188\n",
    "plt.figure(figsize=(1,1))\n",
    "image = X_train[index].squeeze()\n",
    "plt.imshow(image)\n",
    "\n",
    "X_train = np.stack([gray_and_equalize(img) for img in X_train], axis=0)\n",
    "X_test = np.stack([gray_and_equalize(img) for img in X_test], axis=0)\n",
    "X_valid = np.stack([gray_and_equalize(img) for img in X_valid], axis=0)\n",
    "\n",
    "image = X_train[index].squeeze()\n",
    "\n",
    "plt.figure(figsize=(1,2))\n",
    "plt.imshow(image, cmap='gray')\n",
    "print(labels[y_train[index]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "def LeNet(x):    \n",
    "    # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    # Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x32.\n",
    "    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 32), mean = mu, stddev = sigma))\n",
    "    conv1_b = tf.Variable(tf.zeros(32))\n",
    "    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n",
    "\n",
    "    # Activation.\n",
    "    conv1 = tf.nn.relu(conv1)\n",
    "\n",
    "    # Pooling. Input = 28x28x32. Output = 14x14x32.\n",
    "    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # Layer 2: Convolutional. Output = 10x10x64.\n",
    "    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 32, 64), mean = mu, stddev = sigma))\n",
    "    conv2_b = tf.Variable(tf.zeros(64))\n",
    "    conv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n",
    "    \n",
    "    # Activation.\n",
    "    conv2 = tf.nn.relu(conv2)\n",
    "\n",
    "    # Pooling. Input = 10x10x64. Output = 5x5x64.\n",
    "    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # Flatten. Input = 5x5x64. Output = 1600.\n",
    "    fc0   = flatten(conv2)\n",
    "    \n",
    "    # Layer 3: Fully Connected. Input = 1600. Output = 400.\n",
    "    fc1_W = tf.Variable(tf.truncated_normal(shape=(1600, 400), mean = mu, stddev = sigma))\n",
    "    fc1_b = tf.Variable(tf.zeros(400))\n",
    "    fc1   = tf.matmul(fc0, fc1_W) + fc1_b\n",
    "    \n",
    "    # Activation.\n",
    "    fc1    = tf.nn.relu(fc1)\n",
    "\n",
    "    # Layer 4: Fully Connected. Input = 400. Output = 100.\n",
    "    fc2_W  = tf.Variable(tf.truncated_normal(shape=(400, 100), mean = mu, stddev = sigma))\n",
    "    fc2_b  = tf.Variable(tf.zeros(100))\n",
    "    fc2    = tf.matmul(fc1, fc2_W) + fc2_b\n",
    "    \n",
    "    # Activation.\n",
    "    fc2    = tf.nn.relu(fc2)\n",
    "\n",
    "    # Layer 5: Fully Connected. Input = 100. Output = 43.\n",
    "    fc3_W  = tf.Variable(tf.truncated_normal(shape=(100, 43), mean = mu, stddev = sigma))\n",
    "    fc3_b  = tf.Variable(tf.zeros(n_classes))\n",
    "    logits = tf.matmul(fc2, fc3_W) + fc3_b\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import tensorflow as tf\n",
    "\n",
    "# Hyperparamters\n",
    "EPOCHS = 20\n",
    "BATCH_SIZE = 64\n",
    "rate = 0.001\n",
    "\n",
    "# Features and Labels\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y = tf.one_hot(y, n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup the pipeline\n",
    "logits = LeNet(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the Model\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: np.expand_dims(batch_x, axis=3), y: batch_y})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 ...\n",
      "Training Accuracy = 0.438\n",
      "Validation Accuracy = 0.413\n",
      "\n",
      "EPOCH 2 ...\n",
      "Training Accuracy = 0.653\n",
      "Validation Accuracy = 0.603\n",
      "\n",
      "EPOCH 3 ...\n",
      "Training Accuracy = 0.794\n",
      "Validation Accuracy = 0.746\n",
      "\n",
      "EPOCH 4 ...\n",
      "Training Accuracy = 0.871\n",
      "Validation Accuracy = 0.800\n",
      "\n",
      "EPOCH 5 ...\n",
      "Training Accuracy = 0.921\n",
      "Validation Accuracy = 0.855\n",
      "\n",
      "EPOCH 6 ...\n",
      "Training Accuracy = 0.961\n",
      "Validation Accuracy = 0.889\n",
      "\n",
      "EPOCH 7 ...\n",
      "Training Accuracy = 0.968\n",
      "Validation Accuracy = 0.908\n",
      "\n",
      "EPOCH 8 ...\n",
      "Training Accuracy = 0.969\n",
      "Validation Accuracy = 0.916\n",
      "\n",
      "EPOCH 9 ...\n",
      "Training Accuracy = 0.971\n",
      "Validation Accuracy = 0.919\n",
      "\n",
      "EPOCH 10 ...\n",
      "Training Accuracy = 0.978\n",
      "Validation Accuracy = 0.926\n",
      "\n",
      "EPOCH 11 ...\n",
      "Training Accuracy = 0.963\n",
      "Validation Accuracy = 0.908\n",
      "\n",
      "EPOCH 12 ...\n",
      "Training Accuracy = 0.988\n",
      "Validation Accuracy = 0.943\n",
      "\n",
      "EPOCH 13 ...\n",
      "Training Accuracy = 0.979\n",
      "Validation Accuracy = 0.928\n",
      "\n",
      "EPOCH 14 ...\n",
      "Training Accuracy = 0.979\n",
      "Validation Accuracy = 0.927\n",
      "\n",
      "EPOCH 15 ...\n",
      "Training Accuracy = 0.988\n",
      "Validation Accuracy = 0.941\n",
      "\n",
      "EPOCH 16 ...\n",
      "Training Accuracy = 0.992\n",
      "Validation Accuracy = 0.945\n",
      "\n",
      "EPOCH 17 ...\n",
      "Training Accuracy = 0.990\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 18 ...\n",
      "Training Accuracy = 0.990\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 19 ...\n",
      "Training Accuracy = 0.989\n",
      "Validation Accuracy = 0.947\n",
      "\n",
      "EPOCH 20 ...\n",
      "Training Accuracy = 0.994\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "model_file = './lenet'\n",
    "\n",
    "# Training the model\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",
    "    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: np.expand_dims(batch_x, axis=3), y: batch_y})\n",
    "            \n",
    "        validation_accuracy = evaluate(X_valid, y_valid)\n",
    "        training_accuracy = evaluate(X_train, y_train)\n",
    "        print(\"EPOCH {} ...\".format(i+1))\n",
    "        print(\"Training Accuracy = {:.3f}\".format(training_accuracy))\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "        \n",
    "    saver.save(sess, model_file)\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Test Accuracy = 0.943\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "\n",
    "    test_accuracy = evaluate(X_test, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.\n",
    "\n",
    "You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b53f07518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import glob\n",
    "\n",
    "image_files = glob.glob('test_images/*.jpg')\n",
    "\n",
    "images = []\n",
    "my_labels = []\n",
    "for index, filename in enumerate(image_files):\n",
    "    img = plt.imread(filename)\n",
    "    img = cv2.resize(img, (32, 32))\n",
    "    \n",
    "    images.append(img)\n",
    "    my_labels.append(int(filename.split(\".\")[0].split(\"/\")[1]))\n",
    "    \n",
    "rows = 5\n",
    "cols = 5\n",
    "\n",
    "fig, axs = plt.subplots(rows, cols)\n",
    "fig.set_size_inches(25, 25)\n",
    "\n",
    "num_of_images = len(images)\n",
    "for i in range(num_of_images):\n",
    "    axs[int(i / rows)][i % cols].imshow(images[i])\n",
    "    axs[int(i / rows)][i % cols].set_title(labels[my_labels[i]], fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "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",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "preprocessed_images = np.stack([gray_and_equalize(img) for img in images], axis=0)\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "def predict(features):\n",
    "    with tf.Session() as sess:\n",
    "        saver.restore(sess, model_file)\n",
    "        results = sess.run(tf.argmax(logits, 1), {x : np.expand_dims(features, axis=3), keep_prob:1.0})\n",
    "        return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Prediction: Go straight or left | Actual: Go straight or left \n",
      "\n",
      "Prediction: Bumpy road | Actual: Bumpy road \n",
      "\n",
      "Prediction: Yield | Actual: Yield \n",
      "\n",
      "Prediction: Speed limit (50km/h) | Actual: Speed limit (30km/h) \n",
      "\n",
      "Prediction: Go straight or right | Actual: Go straight or right \n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(preprocessed_images)\n",
    "\n",
    "count = 0\n",
    "for prediction in predictions:\n",
    "    print(\"Prediction: {} | Actual: {} \\n\".format(labels[prediction], labels[my_labels[count]]))\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "accuracy is 0.800\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",
    "import tensorflow as tf\n",
    "\n",
    "model_file = './lenet'\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, model_file)\n",
    "    acc = evaluate(preprocessed_images, my_labels)\n",
    "    print(\"accuracy is {:.3f}\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Top Five Softmax Probabilities for Images from the Web\n",
      "Go straight or left\n",
      "Go straight or left: 1.000 \n",
      "Turn left ahead: 0.000 \n",
      "Keep left: 0.000 \n",
      "Ahead only: 0.000 \n",
      "Dangerous curve to the right: 0.000 \n",
      "\n",
      "\n",
      "Bumpy road\n",
      "Bumpy road: 1.000 \n",
      "Bicycles crossing: 0.000 \n",
      "Children crossing: 0.000 \n",
      "Slippery road: 0.000 \n",
      "Beware of ice/snow: 0.000 \n",
      "\n",
      "\n",
      "Yield\n",
      "Yield: 0.838 \n",
      "Traffic signals: 0.158 \n",
      "Priority road: 0.004 \n",
      "General caution: 0.000 \n",
      "Speed limit (80km/h): 0.000 \n",
      "\n",
      "\n",
      "Speed limit (30km/h)\n",
      "Speed limit (50km/h): 0.807 \n",
      "Speed limit (30km/h): 0.129 \n",
      "Wild animals crossing: 0.040 \n",
      "Speed limit (80km/h): 0.017 \n",
      "Speed limit (60km/h): 0.002 \n",
      "\n",
      "\n",
      "Go straight or right\n",
      "Go straight or right: 1.000 \n",
      "Dangerous curve to the right: 0.000 \n",
      "Keep right: 0.000 \n",
      "Road work: 0.000 \n",
      "End of speed limit (80km/h): 0.000 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "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",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, model_file)\n",
    "    pred = tf.nn.softmax(logits)\n",
    "    results = sess.run(tf.nn.top_k(pred, k=5), {x : np.expand_dims(preprocessed_images, axis=3), keep_prob:1.0})\n",
    "    \n",
    "    print(\"Top Five Softmax Probabilities for Images from the Web\")\n",
    "    count = 0\n",
    "    \n",
    "    for i in range(len(images)):\n",
    "        print(labels[my_labels[count]])\n",
    "        accuracy = results[0][i]\n",
    "        classes = results[1][i]\n",
    "        for j in range(5):\n",
    "            print(\"{}: {:.3f} \".format(labels[classes[j]], accuracy[j]))\n",
    "        print(\"\\n\")\n",
    "        count += 1\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Writeup\n",
    "\n",
    "Once you have completed the code implementation, document your results in a project writeup using this [template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) as a guide. The writeup can be in a markdown or pdf file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4 (Optional): Visualize the Neural Network's State with Test Images\n",
    "\n",
    " This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.\n",
    "\n",
    " Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the [LeNet lab's](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.\n",
    "\n",
    "For an example of what feature map outputs look like, check out NVIDIA's results in their paper [End-to-End Deep Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/) in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.\n",
    "\n",
    "<figure>\n",
    " <img src=\"visualize_cnn.png\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above)</p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Visualize your network's feature maps here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# image_input: the test image being fed into the network to produce the feature maps\n",
    "# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer\n",
    "# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output\n",
    "# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry\n",
    "\n",
    "def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):\n",
    "    # Here make sure to preprocess your image_input in a way your network expects\n",
    "    # with size, normalization, ect if needed\n",
    "    # image_input =\n",
    "    # Note: x should be the same name as your network's tensorflow data placeholder variable\n",
    "    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function\n",
    "    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  }
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