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",
    "\n",
    "training_file = \"/Users/laszlofriedmann/Downloads/traffic-signs-data/train.p\"\n",
    "validation_file= \"/Users/laszlofriedmann/Downloads/traffic-signs-data/valid.p\"\n",
    "testing_file = \"/Users/laszlofriedmann/Downloads/traffic-signs-data/test.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\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 = 4410\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Number of training examples\n",
    "n_train = X_train.shape[0]\n",
    "\n",
    "# Number of validation examples\n",
    "n_validation = X_valid.shape[0]\n",
    "\n",
    "# Number of testing examples.\n",
    "n_test = X_valid.shape[0]\n",
    "\n",
    "# shape of traffic sign\n",
    "image_shape = X_train.shape[1:4]\n",
    "\n",
    "# unique classes/labels\n",
    "n_classes = np.unique(y_train).shape[0]\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": {},
   "outputs": [
    {
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PTZ/BNtbvx559Q0O2fvLk88nag5/9G9s2NEdt/dt+dHuyNrZr3Lat1Tf1VuO6E5UZ3805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\nJH1OKz9nPRJCqLVLpbpGr7LSjk+MTetTpuuM8Wl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113302208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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To/leNUij2h+DHB+DfC9p26j2x3QLGRv2x8JNj2tiYpKLLx4byddwkPaXv51R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\nHWMiKEmSJEkdYyIoSZIkSR3z/wN0e3Z2az5FuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133021d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from skimage.color import rgb2gray, gray2rgb\n",
    "%matplotlib inline \n",
    "import random\n",
    "       \n",
    "# plot 3 random example of trainingset with label\n",
    "rand_i = np.random.randint(n_train, size=(3,))\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "plt.figure()\n",
    "\n",
    "for i, _ in enumerate(rand_i):\n",
    "\n",
    "    example_img = X_train[rand_i[i],:,:,:]\n",
    "    classId = y_train[rand_i[i]]\n",
    "    \n",
    "    plt.subplot(1, rand_i.shape[0], i+1)\n",
    "    plt.imshow(example_img)\n",
    "    plt.title('Label: %d' % classId)\n",
    "    \n",
    "# the histogram of the data\n",
    "plt.figure()\n",
    "\n",
    "plt.hist(y_train, 134, normed=1, facecolor='b', alpha=0.75)\n",
    "plt.xlabel('Classes')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of Classes')\n",
    "plt.axis([0, 42, 0, 0.15])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Preprocess of data:\n",
    "- Normalize images from 0 to 1 channelwise\n",
    "- Augment images with some factor:\n",
    "    * Random scaling +/- 12.5%\n",
    "    * Rotating +/- 10°\n",
    "    * Translating +/- 1 in x- and y-direction\n",
    "\"\"\"\n",
    "import scipy.ndimage\n",
    "\n",
    "# Cast data to type float32\n",
    "X_train = X_train.astype('float32')\n",
    "X_valid = X_valid.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# normalize and stretch data from 0 to 1\n",
    "def normalize(f):\n",
    "    \n",
    "    lmin = f.min()\n",
    "    lmax = f.max()\n",
    "    \n",
    "    return (f-lmin)/(lmax-lmin)\n",
    "\n",
    "# normalize each color channel separatly\n",
    "def normChannelwise(img):\n",
    "    \n",
    "    img[:,:,0] = normalize(img[:,:,0])\n",
    "    img[:,:,1] = normalize(img[:,:,1])\n",
    "    img[:,:,2] = normalize(img[:,:,2])\n",
    "    \n",
    "    return img  \n",
    "\n",
    "# data augmentation\n",
    "def augment(img, aug_factor):\n",
    "\n",
    "    # factorize number of augmented images (e.g. if factor is 5, then 4 augmented images on 1 normalized original)\n",
    "    rand = random.randint(0, aug_factor-1)\n",
    "    \n",
    "    if(rand > 0):\n",
    "        new_img_40 = np.zeros((40,40,3))\n",
    "\n",
    "        # scale original img size 32x32px randomly between 28x28px and 36x36px\n",
    "        scale = random.randint(-2, 2)\n",
    "        new_img_x = np.zeros((32+2*scale,32+2*scale,3))\n",
    "\n",
    "        # scale img\n",
    "        new_img_x[:,:,0] = scipy.ndimage.interpolation.zoom(img[:,:,0],(32+2*scale)/32)\n",
    "        new_img_x[:,:,1] = scipy.ndimage.interpolation.zoom(img[:,:,1],(32+2*scale)/32)\n",
    "        new_img_x[:,:,2] = scipy.ndimage.interpolation.zoom(img[:,:,2],(32+2*scale)/32)\n",
    "\n",
    "        # add border to be 40x40px\n",
    "        new_img_40[:,:,0] = np.lib.pad(new_img_x[:,:,0], 4-scale, 'reflect')\n",
    "        new_img_40[:,:,1] = np.lib.pad(new_img_x[:,:,1], 4-scale, 'reflect')\n",
    "        new_img_40[:,:,2] = np.lib.pad(new_img_x[:,:,2], 4-scale, 'reflect')\n",
    "\n",
    "        # rotate img randomly between -10 and 10 degrees\n",
    "        angle = random.randint(-10,10)\n",
    "        new_img_40 = scipy.ndimage.rotate(new_img_40, angle, reshape=False) \n",
    "\n",
    "        # translate img randomly from -1 to 1px in x and y direction and crop\n",
    "        transy = random.randint(-1, 1)\n",
    "        transx = random.randint(-1, 1)  \n",
    "        aug_img = new_img_40[(4+transy):(4+transy)+32,(4+transx):(4+transx)+32,:]\n",
    "    \n",
    "    else:\n",
    "        aug_img = img\n",
    "    \n",
    "    return aug_img\n",
    "\n",
    "# batch generator for training with augmentation and normalization\n",
    "def batchGenTrain(batch_x):\n",
    "    \n",
    "    bs = batch_x.shape[0]\n",
    "    \n",
    "    for i in range(0, bs):\n",
    "        batch_x[i,:,:,:] = normChannelwise(augment(batch_x[i,:,:,:], 5))\n",
    "    \n",
    "    return batch_x\n",
    "\n",
    "# batch generator for testing without augmentation, but with normalization\n",
    "def batchGenTest(batch_x):\n",
    "    \n",
    "    bs = batch_x.shape[0]\n",
    "    \n",
    "    for i in range(0, bs):\n",
    "        batch_x[i,:,:,:] = normChannelwise(batch_x[i,:,:,:])\n",
    "    \n",
    "    return batch_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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T2z95Ik5JfWra3+5SO36+9WBeWSY2RXOjkYkwUYH1XMRrR2mlM6mwL36mqlPe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\n4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7asPln3gQEAAAAAW6ySYGgAAAAAAAsyAAAAAKgMCzIAAAAAqAgLMgAAAACoCAsyAAAA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\nC7KUUsvMflTSf1bnr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h328zIOHdoTzh0LsiXuOxBnDJ3f7ufMDQ3EOVBFkA1j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\nZ6Z/jBWZY9ePAZFarWY4N+rwWzhfy3UWlP5GphS3g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J8A9CNqE4CbkW17L+mQpA+bWU2d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\nKYkbMkmSJElK4oZMkiRJkpL8AL2LQLU58Ti2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be84860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test augmentation on first validation example\n",
    "img = X_valid[0,:,:,:]\n",
    "new_img = img.copy()\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 15]\n",
    "plt.subplot(3, 3, 1)\n",
    "plt.imshow(new_img/255.)\n",
    "plt.title(\"Original unnormalized\")\n",
    "\n",
    "for i in range(0,8):\n",
    "    plt.subplot(3, 3, i+2)\n",
    "    plt.imshow(normChannelwise(augment(new_img, 5)))\n",
    "    plt.title(\"Augmented and normalized\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "\"\"\"\n",
    "Network architecture was adapted from given LeNet, with \n",
    "6 conv layers and relus, \n",
    "3 max pooling operations and \n",
    "2 fully connected layers with dropout:\n",
    "           \n",
    "          feature map  op size\n",
    "           \n",
    "Input:     32x32x3     -\n",
    "1 Conv:    32x32x32    3x3x32\n",
    "2 Conv:    32x32x32    3x3x32\n",
    "1 Maxpool: 16x16x32    2x2/2\n",
    "3 Conv:    16x16x64    3x3x64\n",
    "4 Conv:    16x16x64    3x3x64\n",
    "2 Maxpool: 8x8x64      2x2/2\n",
    "5 Conv:    8x8x128     3x3x128\n",
    "6 Conv:    8x8x128     3x3x128\n",
    "3 Maxpool: 4x4x128     2x2/2\n",
    "1 FC+DO:   1024        1024\n",
    "2 FC+DO:   1024        1024\n",
    "Output:    43          -\n",
    "\"\"\"\n",
    "\n",
    "def myNet(x, keep_prob):    \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 = 32x32x3. Output = 32x32x32.\n",
    "    conv1_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 3, 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='SAME') + conv1_b\n",
    "    conv1   = tf.nn.relu(conv1)\n",
    "    \n",
    "    # Layer 2: Convolutional. Input = 32x32x32. Output = 32x32x32.\n",
    "    conv1_1_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 32, 32), mean = mu, stddev = sigma))\n",
    "    conv1_1_b = tf.Variable(tf.zeros(32))\n",
    "    conv1_1   = tf.nn.conv2d(conv1, conv1_1_W, strides=[1, 1, 1, 1], padding='SAME') + conv1_1_b\n",
    "    conv1_1   = tf.nn.relu(conv1_1)\n",
    "\n",
    "    # Max-Pooling 1: Input = 32x32x32. Output = 16x16x32.\n",
    "    conv1_1 = tf.nn.max_pool(conv1_1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "    # Layer 3: Convolutional. Input = 16x16x32. Output = 16x16x64.\n",
    "    conv2_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 32, 64), mean = mu, stddev = sigma))\n",
    "    conv2_b = tf.Variable(tf.zeros(64))\n",
    "    conv2   = tf.nn.conv2d(conv1_1, conv2_W, strides=[1, 1, 1, 1], padding='SAME') + conv2_b\n",
    "    conv2   = tf.nn.relu(conv2)\n",
    "    \n",
    "    # Layer 4: Convolutional. Input = 16x16x64. Output = 16x16x64.\n",
    "    conv2_1_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 64, 64), mean = mu, stddev = sigma))\n",
    "    conv2_1_b = tf.Variable(tf.zeros(64))\n",
    "    conv2_1   = tf.nn.conv2d(conv2, conv2_1_W, strides=[1, 1, 1, 1], padding='SAME') + conv2_1_b\n",
    "    conv2_1   = tf.nn.relu(conv2_1)\n",
    "\n",
    "    # Max-Pooling 2: Input = 16x16x64. Output = 8x8x64.\n",
    "    conv2_1 = tf.nn.max_pool(conv2_1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "    \n",
    "    # Layer 5: Convolutional. Input = 8x8x64. Output = 8x8x128.\n",
    "    conv3_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 64, 128), mean = mu, stddev = sigma))\n",
    "    conv3_b = tf.Variable(tf.zeros(128))\n",
    "    conv3   = tf.nn.conv2d(conv2_1, conv3_W, strides=[1, 1, 1, 1], padding='SAME') + conv3_b\n",
    "    conv3   = tf.nn.relu(conv3)\n",
    "    \n",
    "    # Layer 6: Convolutional. Input = 8x8x128. Output = 8x8x128.\n",
    "    conv3_1_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 128, 128), mean = mu, stddev = sigma))\n",
    "    conv3_1_b = tf.Variable(tf.zeros(128))\n",
    "    conv3_1   = tf.nn.conv2d(conv3, conv3_1_W, strides=[1, 1, 1, 1], padding='SAME') + conv3_1_b\n",
    "    conv3_1   = tf.nn.relu(conv3_1)\n",
    "\n",
    "    # Max-Pooling 3: Input = 8x8x128. Output = 4x4x128.\n",
    "    conv3_1 = tf.nn.max_pool(conv3_1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "    # Flatten. Input = 4x4x128. Output = 2048.\n",
    "    fc0   = flatten(conv3_1)\n",
    "    \n",
    "    # Fully Connected Layer 1: Input = 2048. Output = 1024.\n",
    "    fc1_W = tf.Variable(tf.truncated_normal(shape=(2048, 1024), mean = mu, stddev = sigma))\n",
    "    fc1_b = tf.Variable(tf.zeros(1024))\n",
    "    fc1   = tf.matmul(fc0, fc1_W) + fc1_b\n",
    "    fc1   = tf.nn.relu(fc1)\n",
    "    # + DROPOUT\n",
    "    fc1    = tf.nn.dropout(fc1, keep_prob)\n",
    "\n",
    "    # Fully Connected Layer 2: Input = 1024. Output = 1024.\n",
    "    fc2_W  = tf.Variable(tf.truncated_normal(shape=(1024, 1024), mean = mu, stddev = sigma))\n",
    "    fc2_b  = tf.Variable(tf.zeros(1024))\n",
    "    fc2    = tf.matmul(fc1, fc2_W) + fc2_b\n",
    "    fc2    = tf.nn.relu(fc2)\n",
    "    # + DROPOUT\n",
    "    fc2    = tf.nn.dropout(fc2, keep_prob)\n",
    "\n",
    "    # Output Layer: Input = 1024. Output = 43.\n",
    "    fc3_W  = tf.Variable(tf.truncated_normal(shape=(1024, 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": [
    "### 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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 ... 34688\n",
      "Validation Accuracy = 0.706\n",
      "\n",
      "EPOCH 2 ... 34688\n",
      "Validation Accuracy = 0.928\n",
      "\n",
      "EPOCH 3 ... 34688\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 4 ... 34688\n",
      "Validation Accuracy = 0.967\n",
      "\n",
      "EPOCH 5 ... 34688\n",
      "Validation Accuracy = 0.978\n",
      "\n",
      "EPOCH 6 ... 34688\n",
      "Validation Accuracy = 0.972\n",
      "\n",
      "EPOCH 7 ... 34688\n",
      "Validation Accuracy = 0.977\n",
      "\n",
      "EPOCH 8 ... 34688\n",
      "Validation Accuracy = 0.981\n",
      "\n",
      "EPOCH 9 ... 34688\n",
      "Validation Accuracy = 0.974\n",
      "\n",
      "EPOCH 10 ...34688\n",
      "Validation Accuracy = 0.986\n",
      "\n",
      "EPOCH 11 ...34688\n",
      "Validation Accuracy = 0.983\n",
      "\n",
      "EPOCH 12 ...34688\n",
      "Validation Accuracy = 0.984\n",
      "\n",
      "EPOCH 13 ...34688\n",
      "Validation Accuracy = 0.981\n",
      "\n",
      "EPOCH 14 ...34688\n",
      "Validation Accuracy = 0.986\n",
      "\n",
      "EPOCH 15 ...34688\n",
      "Validation Accuracy = 0.979\n",
      "\n",
      "EPOCH 16 ...34688\n",
      "Validation Accuracy = 0.982\n",
      "\n",
      "EPOCH 17 ...34688\n",
      "Validation Accuracy = 0.991\n",
      "\n",
      "EPOCH 18 ...34688\n",
      "Validation Accuracy = 0.985\n",
      "\n",
      "EPOCH 19 ...34688\n",
      "Validation Accuracy = 0.988\n",
      "\n",
      "EPOCH 20 ...34688\n",
      "Validation Accuracy = 0.991\n",
      "\n",
      "EPOCH 21 ...34688\n",
      "Validation Accuracy = 0.991\n",
      "\n",
      "EPOCH 22 ...34688\n",
      "Validation Accuracy = 0.976\n",
      "\n",
      "EPOCH 23 ...34688\n",
      "Validation Accuracy = 0.989\n",
      "\n",
      "EPOCH 24 ...34688\n",
      "Validation Accuracy = 0.992\n",
      "\n",
      "EPOCH 25 ...34688\n",
      "Validation Accuracy = 0.988\n",
      "\n",
      "EPOCH 26 ...34688\n",
      "Validation Accuracy = 0.989\n",
      "\n",
      "EPOCH 27 ...34688\n",
      "Validation Accuracy = 0.993\n",
      "\n",
      "EPOCH 28 ...34688\n",
      "Validation Accuracy = 0.991\n",
      "\n",
      "EPOCH 29 ...34688\n",
      "Validation Accuracy = 0.986\n",
      "\n",
      "EPOCH 30 ...34688\n",
      "Validation Accuracy = 0.991\n",
      "\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Adapted training procedure of LeNet.\n",
    "- Epochs:            30\n",
    "- Batch size:        128\n",
    "- Learning rate:     0.001\n",
    "- Optimizer:         Adam (Momentum & adaptation of learning rate)\n",
    "- Data Augmentation: Yes, factorized with 5\n",
    "- Preprocessing:     Yes, normalization of data to 0 and 1\n",
    "\"\"\"\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "## HYPERPARAMETER\n",
    "EPOCHS     = 30\n",
    "BATCH_SIZE = 128\n",
    "rate       = 0.001\n",
    "##\n",
    "\n",
    "# tf placeholders\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 3))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y = tf.one_hot(y, 43)\n",
    "keep_prob = tf.placeholder(tf.float32, (None))\n",
    "\n",
    "# ops\n",
    "logits = myNet(x, keep_prob)\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",
    "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",
    "# evaluation\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",
    "        \n",
    "        # preprocess for testing\n",
    "        batch_pre = batch_x.copy()\n",
    "        batch_pre = batchGenTest(batch_pre)\n",
    "        \n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_pre, y: batch_y, keep_prob: 1.0})\n",
    "        total_accuracy += (accuracy * len(batch_pre))\n",
    "    return total_accuracy / num_examples\n",
    "\n",
    "# training session\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",
    "        \n",
    "        # shuffle data each epoch\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        \n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            print(\"Iterations:\", offset, end=\"\\r\")\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "\n",
    "            # preprocess\n",
    "            batch_pre = batch_x.copy()\n",
    "            batch_pre = batchGenTrain(batch_pre)\n",
    "            \n",
    "            sess.run(training_operation, feed_dict={x: batch_pre, y: batch_y, keep_prob: 0.5})\n",
    "            \n",
    "        validation_accuracy = evaluate(X_valid, y_valid)\n",
    "        print(\"EPOCH {} ...\".format(i+1))\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "        saver.save(sess, \"./models/model-{}\".format(i+1))\n",
    "        \n",
    "    saver.save(sess, './model')\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oIh1pYN9zUp8Hncw6FtUHi4jz4ZEvIya0n5689pX7SYEACF1gjPiY2JvhuiQA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\n1R2CtkzvA5Tr42BpiZkHbo9MoBkO61PdIPQD617epmxWL9d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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a100588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "list_signs = os.listdir('./example_german_signs')\n",
    "img_list = []\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "\n",
    "for img_name in list_signs:\n",
    "    img = Image.open('./example_german_signs/'+img_name)\n",
    "    img = img.convert('RGB')\n",
    "    img = img.resize((32, 32), Image.ANTIALIAS)\n",
    "    img = np.reshape(img, (32, 32, 3))\n",
    "\n",
    "    img_list.append(img)\n",
    "\n",
    "for i, img in enumerate(img_list):\n",
    "    plt.subplot(1, 5, i+1)\n",
    "    plt.imshow(img)\n",
    "    plt.title(list_signs[i])\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set earlier model: 0.9806\n",
      "Accuracy on test set: 0.9762\n"
     ]
    },
    {
     "data": {
      "image/png": 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ejTqr6nkVkXoReUREHkvq/Pfk9oNFZEXyfP5ARGpTO6iqlv0PgCyKO1IdAqAW\nwGMAjqhELQG1bgAwtdJ1jFDbPwI4FsCaIbd9AcCVycdXAri2Suu8GsCHKl3bMLXOBHBs8nELgD8C\nOKLanlejzqp6XlFcPrQ5+bgGwAoU9zi5HcD5ye2LAfxrWses1JXacQCeUtX1WtzV/TYAZ1eolglL\nVZcD2LrXzWejuA8rUCX7sY5QZ1VS1U5VXZl8vBPF/ThmocqeV6POqqJFZd03uFJNbRaA54Z8Xs37\nhiqAX4jI70RkUaWLCTBDVTuB4okPYHqF67FcJiKrkx9PK/5j8t5E5CAUl7JfgSp+XveqE6iy57Xc\n+wZXqqkNt+h5tb4Ne4KqHgvgjQAuFZF/rHRBkfgOgLkAjgHQCeDLlS3nr4lIM4A7AVyhqjsqXc9I\nhqmz6p5XLWHf4LGoVFPrADBnyOcj7htaaaq6Mfl7M4C7MZbNZsqrS0RmAkDy9+YK1zMsVe1KTvYC\ngO+iip5XEalBsVHcrKp3JTdX3fM6XJ3V/LzqGPYNHotKNbVHAcxL3gGpBXA+gHsrVMuIRKRJRFr2\nfAzgdABr7K+quHtR3IcVqOL9WPc0iMQ5qJLnVYrbTV0PYJ2qfmVIqKqe15HqrLbntRL7Blds+DZ5\nq/lrKL4TulRVP1+RQgwicgiKV2dAcTvBW6qpThG5FcDJKK540AXg0wB+hOI7SwcAeBbAeapa0Rfp\nR6jzZBR/RFIU32G+ZM9rVpUkIicCeAjA4wAKyc0fR/H1qqp5Xo06L0AVPa8icjSKbwQM3Tf4M8n3\n1m0AWlHcN/ifVNXZWDDwmJVqakRE44G/UUBEUWFTI6KosKkRUVTY1IgoKmxqRBQVNjUiigqbGhFF\nhU2NiKLyf66En4nM/4mDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136b7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: Speed limit (70km/h)\n",
      " with 0.3881 softmax probability\n",
      "Prediction: Speed limit (60km/h)\n",
      " with 0.3830 softmax probability\n",
      "Prediction: Double curve\n",
      " with 0.1213 softmax probability\n",
      "Prediction: General caution\n",
      " with 0.0309 softmax probability\n",
      "Prediction: Traffic signals\n",
      " with 0.0307 softmax probability\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x123985208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: Right-of-way at the next intersection\n",
      " with 1.0000 softmax probability\n",
      "Prediction: Pedestrians\n",
      " with 0.0000 softmax probability\n",
      "Prediction: Double curve\n",
      " with 0.0000 softmax probability\n",
      "Prediction: Ahead only\n",
      " with 0.0000 softmax probability\n",
      "Prediction: Road work\n",
      " with 0.0000 softmax probability\n"
     ]
    },
    {
     "data": {
      "image/png": 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8+DlWiniuh4bDAgDUpyYiMjgVNRFJioqaiCRFRU1EkqKiJiJJUVETkaSoqIlI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\nSVFRE5GkqKiJSFJU1EQkKf8f+8nPJTOviJ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b821198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: Road work\n",
      " with 1.0000 softmax probability\n",
      "Prediction: Road narrows on the right\n",
      " with 0.0000 softmax probability\n",
      "Prediction: Dangerous curve to the right\n",
      " with 0.0000 softmax probability\n",
      "Prediction: Bumpy road\n",
      " with 0.0000 softmax probability\n",
      "Prediction: Bicycles crossing\n",
      " with 0.0000 softmax probability\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b9f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: Stop\n",
      " with 0.5582 softmax probability\n",
      "Prediction: Priority road\n",
      " with 0.4386 softmax probability\n",
      "Prediction: No entry\n",
      " with 0.0022 softmax probability\n",
      "Prediction: Roundabout mandatory\n",
      " with 0.0004 softmax probability\n",
      "Prediction: Right-of-way at the next intersection\n",
      " with 0.0001 softmax probability\n"
     ]
    },
    {
     "data": {
      "image/png": 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jc3aTQABQtYsJDzztx+g9Yze0rG/yVwJ//ued5viGqxvdGGc6e8zxkXP+e6EX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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b45da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: Yield\n",
      " with 0.9998 softmax probability\n",
      "Prediction: Priority road\n",
      " with 0.0002 softmax probability\n",
      "Prediction: No vehicles\n",
      " with 0.0000 softmax probability\n",
      "Prediction: No passing\n",
      " with 0.0000 softmax probability\n",
      "Prediction: Keep right\n",
      " with 0.0000 softmax probability\n"
     ]
    }
   ],
   "source": [
    "# reload original validation data and cast\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_valid = X_valid.astype('float32')\n",
    "\n",
    "# load img list from web as np.array & preprocess\n",
    "web_imgs = np.asarray(img_list)\n",
    "web_imgs = web_imgs.astype('float32')\n",
    "test_batch = web_imgs.copy()\n",
    "test_batch = batchGenTest(test_batch)\n",
    "\n",
    "# map label numbers to sign names \n",
    "label_map={}\n",
    "cnt = 0\n",
    "with open('signnames.csv') as f:\n",
    "    for line in f:\n",
    "        \n",
    "        if (cnt > 0):\n",
    "            label_num, label_name = line.split(',')\n",
    "            label_num = int(label_num)\n",
    "            label_map[label_num] = label_name\n",
    "            \n",
    "        cnt+=1\n",
    "\n",
    "# load model and predict\n",
    "with tf.Session() as sess:\n",
    "    \n",
    "    # Accuracy on test set of older model\n",
    "    saver.restore(sess, './models/model-29')\n",
    "    print('Accuracy on test set earlier model:', '%.4f' % evaluate(X_test, y_test))\n",
    "    \n",
    "    # Restore model of latest training\n",
    "    saver = tf.train.import_meta_graph('./models/model-30.meta')\n",
    "    saver.restore(sess,tf.train.latest_checkpoint('./models'))\n",
    "    \n",
    "    # Accuracy on test set\n",
    "    print('Accuracy on test set:', '%.4f' % evaluate(X_test, y_test))\n",
    "    \n",
    "    softmax = tf.nn.softmax(logits)\n",
    "    top_5 = tf.nn.top_k(softmax, k=5)\n",
    "    softmaxes_topk, classes_topk = sess.run(top_5, feed_dict={x: test_batch, keep_prob: 1.0})\n",
    "    \n",
    "    count = 0\n",
    "    for softmax_topk, class_topk in zip(softmaxes_topk, classes_topk):\n",
    "        plt.imshow(img_list[count])\n",
    "        plt.title(list_signs[count])\n",
    "        plt.show()\n",
    "        count = count+1\n",
    "        \n",
    "        for softmax_single, class_single in zip(softmax_topk, class_topk):\n",
    "            print('Prediction:', label_map[class_single], 'with', '%.4f' % softmax_single, 'softmax probability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "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",
    "\n",
    "# Accuracy on the 5 images from web is 5 out of 5, so 100%. \n",
    "# It is close to mistake the 70 km/h sign for a 60s sign, but it still makes the correct decision."
   ]
  },
  {
   "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. `tk.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."
   ]
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   "cell_type": "code",
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   "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."
   ]
  },
  {
   "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": 12,
   "metadata": {
    "collapsed": true
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   "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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