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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "import numpy as np\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",
    "## 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 validation 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",
    "\n",
    "# TODO: Number of training examples\n",
    "n_train = X_train.shape[0]\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = X_valid.shape[0]\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = X_test.shape[0]\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train[1].shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = np.unique(np.concatenate((y_train, y_valid, y_test))).size\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of validation 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": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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UXUqpvFIqrZT6CTFSnrXy1TgOF6QMldGDfl1E/pfW+kMxl3xERH5ZKbVFKbVZRP6doBz2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\n23gxNWHRxFEJY7Q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7QGWcnsC9DzdYqtNfpJTIcMak08uCwrtuEOWSTyPt3RpR7ucgf3Mj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+LIkitI2oXuGAjNWetlJ3I5CBeZXivyqCAnkixLfVStSxp0UerTzbfdFsddS4kKfviz6CsMkIso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YDAaDwWAwGC44ziShiqLYdWq5UAZHEFK8SH3Lc8icynbKe1xz7CpQeQvQXfOctF5QjungANrq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/p/LheIgFwQLcUnlrBxe+0d33OxA4CI4iAXfYekxRE5TCFo0XaKZ9RfWbaaFLhalDmGUjyo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\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M9kg0FIixP0kg/wqxtzGQnBi0v8uM0tkcFEFuPiwklMUlMOYGkJn1EScolzvVPSqkK+zz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\nqiN+RijncTzH8cU19G+hfmFauNY/kXSutYLnkcoYniPbXSe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caIHQkBbRC2lxua6cIyLq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M/L8vhTXTN7/hPnnHNvvCUH5dv3/1J/Hz8py++/LVfPe9iWY72n8Xexp7HbuwaJqPvJ4jp+tson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\nTCblGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef5cbb14a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some samples\n",
    "plt.figure(figsize=(20,10))\n",
    "for n in range (50):\n",
    "    index = random.randint(0, len(X_train)-1)\n",
    "    image = X_train[index]\n",
    "    plt.subplot(5,10,n+1)\n",
    "    plt.xticks([]), plt.yticks([])\n",
    "    plt.imshow(image)\n",
    "    plt.title('Image: '+ str(index))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AALbDyp59s9Q9uHf3LHUBYFl4jyWxwgkAAACAZgInAAAAAFoJnAAAAABoJXAC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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef511717b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_histogram():\n",
    "    plt.figure(figsize=(20,10))\n",
    "    plt.hist(y_train, bins=n_classes, rwidth=0.9, range=[0,n_classes],align='left')\n",
    "    plt.xticks(np.arange(n_classes))\n",
    "    plt.xlabel('Sign Label')\n",
    "    plt.ylabel('Number')\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "show_histogram()"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "The following pre-processing data steps are used:\n",
    "1. Shuffle the data using sklearn\n",
    "2. Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "\n",
    "import math\n",
    "\n",
    "# Shuffle the data\n",
    "from sklearn.utils import shuffle\n",
    "# X_train, y_train  = shuffle(X_train, y_train)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.2, random_state=0)\n",
    "\n",
    "# Normalize the data\n",
    "def normalize_data(img):\n",
    "    x_mean = float(img.mean())\n",
    "    x_std = float(img.std())\n",
    "    img_normalized = (img - x_mean)/(x_std)\n",
    "    return img_normalized\n",
    "\n",
    "X_train = normalize_data(X_train)\n",
    "X_valid = normalize_data(X_valid)\n",
    "X_test = normalize_data(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "EPOCHS = 20\n",
    "BATCH_SIZE = 128\n",
    "\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 = 28x28x6.\n",
    "    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 3, 6), mean = mu, stddev = sigma))\n",
    "    conv1_b = tf.Variable(tf.zeros(6))\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 = 28x28x6. Output = 14x14x6.\n",
    "    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='convolution-1')\n",
    "\n",
    "    # Layer 2: Convolutional. Output = 10x10x16.\n",
    "    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n",
    "    conv2_b = tf.Variable(tf.zeros(16))\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 = 10x10x16. Output = 5x5x16.\n",
    "    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='convolution-2')\n",
    "\n",
    "    # Flatten. Input = 5x5x16. Output = 400.\n",
    "    fc0   = flatten(conv2)\n",
    "    \n",
    "    # Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\n",
    "    fc1_b = tf.Variable(tf.zeros(120))\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 = 120. Output = 84.\n",
    "    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n",
    "    fc2_b  = tf.Variable(tf.zeros(84))\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 = 84. Output = 43.\n",
    "    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\n",
    "    fc3_b  = tf.Variable(tf.zeros(43))\n",
    "    logits = tf.matmul(fc2, fc3_W) + fc3_b\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, (None, 32, 32, 3))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "x_final_test = tf.placeholder(tf.float32, (None, 32, 32, 3))\n",
    "x_final_graph = tf.placeholder(tf.float32, (None, 32, 32, 3))\n",
    "one_hot_y = tf.one_hot(y, n_classes)"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 ...\n",
      "Validation Accuracy = 0.690\n",
      "\n",
      "EPOCH 2 ...\n",
      "Validation Accuracy = 0.813\n",
      "\n",
      "EPOCH 3 ...\n",
      "Validation Accuracy = 0.876\n",
      "\n",
      "EPOCH 4 ...\n",
      "Validation Accuracy = 0.896\n",
      "\n",
      "EPOCH 5 ...\n",
      "Validation Accuracy = 0.911\n",
      "\n",
      "EPOCH 6 ...\n",
      "Validation Accuracy = 0.919\n",
      "\n",
      "EPOCH 7 ...\n",
      "Validation Accuracy = 0.927\n",
      "\n",
      "EPOCH 8 ...\n",
      "Validation Accuracy = 0.935\n",
      "\n",
      "EPOCH 9 ...\n",
      "Validation Accuracy = 0.929\n",
      "\n",
      "EPOCH 10 ...\n",
      "Validation Accuracy = 0.931\n",
      "\n",
      "EPOCH 11 ...\n",
      "Validation Accuracy = 0.936\n",
      "\n",
      "EPOCH 12 ...\n",
      "Validation Accuracy = 0.940\n",
      "\n",
      "EPOCH 13 ...\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 14 ...\n",
      "Validation Accuracy = 0.942\n",
      "\n",
      "EPOCH 15 ...\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 16 ...\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 17 ...\n",
      "Validation Accuracy = 0.964\n",
      "\n",
      "EPOCH 18 ...\n",
      "Validation Accuracy = 0.969\n",
      "\n",
      "EPOCH 19 ...\n",
      "Validation Accuracy = 0.939\n",
      "\n",
      "EPOCH 20 ...\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "Model saved\n",
      "Test Accuracy = 0.905\n"
     ]
    }
   ],
   "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",
    "\n",
    "# Training Pipeline\n",
    "rate = 0.001\n",
    "\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)\n",
    "\n",
    "# Model Evaluation\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})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples\n",
    "\n",
    "# Train 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: batch_x, y: batch_y})\n",
    "            \n",
    "        validation_accuracy = evaluate(X_validation, y_validation)\n",
    "        print(\"EPOCH {} ...\".format(i+1))\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "        \n",
    "    saver.save(sess, './lenet')\n",
    "    print(\"Model saved\")\n",
    "\n",
    "# Test 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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XivgvD96nclpYGzz8/PPPIY+bG5o2bSriDRs2qJz+/fqJuEABXdexsnTpUjVmz7F06dIq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\niIjIZ4xr0WzM7syYPQC2ACgLYG/c7jg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef99a198d0>"
      ]
     },
     "metadata": {},
     "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 os\n",
    "import glob\n",
    "import cv2\n",
    "\n",
    "def load_image(path):\n",
    "    img = cv2.imread(path)\n",
    "    img = cv2.resize(img, (32,32), fx=1.0, fy=1.0, interpolation=cv2.INTER_AREA)\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    return img\n",
    "\n",
    "# Load and plot the images\n",
    "images = [load_image(path) for path in glob.glob('test_signs/*.png')]\n",
    "X_new_images = np.asarray(images)\n",
    "\n",
    "fig = plt.figure(figsize=(15,6))\n",
    "nrows, ncols = 1, 5\n",
    "plt.subplot(nrows, ncols, 1)\n",
    "for i in range(ncols):\n",
    "    ax = plt.subplot(nrows, ncols, i+1)\n",
    "    plt.imshow(X_new_images[i])\n",
    "    \n",
    "### Load and plot the images\n",
    "# import matplotlib.pyplot as plt\n",
    "# import matplotlib.image as mpimg\n",
    "\n",
    "# image1=mpimg.imread('test_signs/germany-end-speed-limit-sign-60.png')\n",
    "# image1 = cv2.resize(image1, (32,32), fx=1.0, fy=1.0, interpolation=cv2.INTER_AREA)\n",
    "# image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)\n",
    "# plt.subplot(3,3,1)\n",
    "# plt.axis('off')\n",
    "# plt.imshow(image1)\n",
    "\n",
    "# image2=mpimg.imread('test_signs/germany-road-signs-pedestrians-crossing.png')\n",
    "# image2 = cv2.resize(image2, (32,32), fx=1.0, fy=1.0, interpolation=cv2.INTER_AREA)\n",
    "# image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)\n",
    "# plt.subplot(3,3,2)\n",
    "# plt.axis('off')\n",
    "# plt.imshow(image2)\n",
    "\n",
    "# image3=mpimg.imread('test_signs/germany-road-signs-slippery.png')\n",
    "# image3 = cv2.resize(image3, (32,32), fx=1.0, fy=1.0, interpolation=cv2.INTER_AREA)\n",
    "# image3 = cv2.cvtColor(image3, cv2.COLOR_BGR2RGB)\n",
    "# plt.subplot(3,3,3)\n",
    "# plt.axis('off')\n",
    "# plt.imshow(image3)\n",
    "\n",
    "# image4=mpimg.imread('test_signs/germany-road-signs-wild-animals.png')\n",
    "# image4 = cv2.resize(image4, (32,32), fx=1.0, fy=1.0, interpolation=cv2.INTER_AREA)\n",
    "# image4 = cv2.cvtColor(image4, cv2.COLOR_BGR2RGB)\n",
    "# plt.subplot(3,3,4)\n",
    "# plt.axis('off')\n",
    "# plt.imshow(image4)\n",
    "\n",
    "# image5=mpimg.imread('test_signs/germany-speed-limit-sign-60.png')\n",
    "# image5 = cv2.resize(image5, (32,32), fx=1.0, fy=1.0, interpolation=cv2.INTER_AREA)\n",
    "# image5 = cv2.cvtColor(image5, cv2.COLOR_BGR2RGB)\n",
    "# plt.subplot(3,3,5)\n",
    "# plt.axis('off')\n",
    "# plt.imshow(image5)\n",
    "\n",
    "# X_new_images = np.array([image1, image2, image3, image4, image5])\n",
    "# y_test_labels=np.array([6,27,23,31,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SB7tJaJlMZeqA5z7n3nabr2tt6Jjuuc8fek31da03u57gq99ZH63y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\n9iQr9jwW8NIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef500685f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "# Pre-processing new images\n",
    "# X_final_test, y_test_labels  = shuffle(X_final_test, y_test_labels)\n",
    "X_final_test = normalize_data(X_new_images)\n",
    "y_test_labels=np.array([27, 23, 3, 6, 31])\n",
    "# y_test_labels = np.array([27,23,3,16,1])\n",
    "\n",
    "# Plot preprocessed images\n",
    "images = np.resize(X_new_images, (len(X_final_test), 32,32))\n",
    "fig = plt.figure(figsize=(15,6))\n",
    "nrows, ncols = 1, 5\n",
    "plt.subplot(nrows, ncols, 1)\n",
    "for i in range(ncols):\n",
    "    ax = plt.subplot(nrows, ncols, i+1)\n",
    "    plt.imshow(images[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with images from the internet = 0.600\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",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "\n",
    "    test_accuracy = evaluate(X_final_test, y_test_labels)\n",
    "    print(\"Accuracy with images from the internet = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[ 1.01231352  0.52451998  0.55438489]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 1.01231352  0.52451998  0.55438489]]\n",
      "\n",
      " [[ 1.03222346  0.55438489  0.5842498 ]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  ..., \n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 1.03222346  0.55438489  0.5842498 ]]\n",
      "\n",
      " [[-0.28183259 -0.29178756 -0.29178756]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  ..., \n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [-0.28183259 -0.28183259 -0.28183259]]] - Prediction: Traffic signals\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[ 0.96253867  0.61411471  0.63402465]\n",
      "  [ 0.94262873 -0.76962612 -0.67007642]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.94262873 -0.76962612 -0.67007642]\n",
      "  [ 0.97249364  0.63402465  0.65393459]]\n",
      "\n",
      " [[ 1.13177316  0.52451998  0.55438489]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 1.1516831   0.54442992  0.57429483]]\n",
      "\n",
      " [[ 0.2856007   0.24578082  0.24578082]\n",
      "  [ 1.16163807  0.61411471  0.64397962]\n",
      "  [ 1.16163807  0.55438489  0.5842498 ]\n",
      "  ..., \n",
      "  [ 1.16163807  0.55438489  0.5842498 ]\n",
      "  [ 1.16163807  0.61411471  0.64397962]\n",
      "  [ 0.32542058  0.2856007   0.2856007 ]]] - Prediction: Bumpy road\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]] - Prediction: Turn left ahead\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]] - Prediction: Ahead only\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[ 1.01231352  0.52451998  0.55438489]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 1.01231352  0.52451998  0.55438489]]\n",
      "\n",
      " [[ 1.03222346  0.55438489  0.5842498 ]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  ..., \n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 1.03222346  0.55438489  0.5842498 ]]\n",
      "\n",
      " [[-0.28183259 -0.29178756 -0.29178756]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  ..., \n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [-0.28183259 -0.28183259 -0.28183259]]] - Prediction: Beware of ice/snow\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[ 1.01231352  0.52451998  0.55438489]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 1.01231352  0.52451998  0.55438489]]\n",
      "\n",
      " [[ 1.03222346  0.55438489  0.5842498 ]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  ..., \n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 1.03222346  0.55438489  0.5842498 ]]\n",
      "\n",
      " [[-0.28183259 -0.29178756 -0.29178756]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  ..., \n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [-0.28183259 -0.28183259 -0.28183259]]]:\n",
      "Traffic signals: 99.70%\n",
      "No entry: 0.30%\n",
      "Slippery road: 0.00%\n",
      "No passing for vehicles over 3.5 metric tons: 0.00%\n",
      "Pedestrians: 0.00%\n",
      "\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[ 0.96253867  0.61411471  0.63402465]\n",
      "  [ 0.94262873 -0.76962612 -0.67007642]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.94262873 -0.76962612 -0.67007642]\n",
      "  [ 0.97249364  0.63402465  0.65393459]]\n",
      "\n",
      " [[ 1.13177316  0.52451998  0.55438489]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 1.1516831   0.54442992  0.57429483]]\n",
      "\n",
      " [[ 0.2856007   0.24578082  0.24578082]\n",
      "  [ 1.16163807  0.61411471  0.64397962]\n",
      "  [ 1.16163807  0.55438489  0.5842498 ]\n",
      "  ..., \n",
      "  [ 1.16163807  0.55438489  0.5842498 ]\n",
      "  [ 1.16163807  0.61411471  0.64397962]\n",
      "  [ 0.32542058  0.2856007   0.2856007 ]]]:\n",
      "Bumpy road: 100.00%\n",
      "No passing: 0.00%\n",
      "Dangerous curve to the left: 0.00%\n",
      "Speed limit (120km/h): 0.00%\n",
      "General caution: 0.00%\n",
      "\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]]:\n",
      "Turn left ahead: 71.71%\n",
      "Speed limit (80km/h): 28.28%\n",
      "Speed limit (20km/h): 0.01%\n",
      "Ahead only: 0.00%\n",
      "Speed limit (50km/h): 0.00%\n",
      "\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]]:\n",
      "Ahead only: 86.14%\n",
      "Speed limit (80km/h): 9.52%\n",
      "Turn left ahead: 2.58%\n",
      "Go straight or left: 1.33%\n",
      "Wild animals crossing: 0.38%\n",
      "\n",
      "[[[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " [[-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  ..., \n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]\n",
      "  [-0.98863547 -0.98863547 -0.98863547]]\n",
      "\n",
      " ..., \n",
      " [[ 1.01231352  0.52451998  0.55438489]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  ..., \n",
      "  [ 0.93267376 -0.809446   -0.7098963 ]\n",
      "  [ 0.93267376 -0.79949103 -0.69994133]\n",
      "  [ 1.01231352  0.52451998  0.55438489]]\n",
      "\n",
      " [[ 1.03222346  0.55438489  0.5842498 ]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  ..., \n",
      "  [ 0.94262873 -0.77958109 -0.68003139]\n",
      "  [ 0.9525837  -0.71985127 -0.63025654]\n",
      "  [ 1.03222346  0.55438489  0.5842498 ]]\n",
      "\n",
      " [[-0.28183259 -0.29178756 -0.29178756]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  ..., \n",
      "  [ 0.79330418  0.62406968  0.63402465]\n",
      "  [ 0.70370945  0.55438489  0.56433986]\n",
      "  [-0.28183259 -0.28183259 -0.28183259]]]:\n",
      "Beware of ice/snow: 100.00%\n",
      "Road narrows on the right: 0.00%\n",
      "Dangerous curve to the right: 0.00%\n",
      "Bumpy road: 0.00%\n",
      "No passing: 0.00%\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",
    "import csv\n",
    "\n",
    "# Example data\n",
    "signs = []\n",
    "\n",
    "with open('signnames.csv', 'r') as csvfile:\n",
    "    readCSV = csv.reader(csvfile, delimiter=',')\n",
    "    for row in readCSV:\n",
    "        signs += [row[1]]\n",
    "        \n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('.'))  \n",
    "    predicted_logits = sess.run(logits, feed_dict={x: X_final_test})\n",
    "    predicted_labels = np.argmax(predicted_logits, axis=1)\n",
    "    for i in range(len(X_final_test)):\n",
    "        print(\"{0} - Prediction: {1}\".format(X_final_test[i],signs[predicted_labels[i]]))\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    softmax = tf.nn.softmax(predicted_logits)\n",
    "    top5 = sess.run(tf.nn.top_k(softmax, k=5))\n",
    "    for x in range(len(X_final_test)):\n",
    "        print(\"{0}:\".format(X_final_test[x]))\n",
    "        for y in range(5):\n",
    "            print(\"{:s}: {:.2f}%\".format(signs[top5[1][x][y]], top5[0][x][y]*100))\n",
    "        print()"
   ]
  },
  {
   "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": 14,
   "metadata": {
    "collapsed": true
   },
   "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\")\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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