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": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from enum import Enum\n",
    "\n",
    "class ColorSpace(Enum):\n",
    "    RGB = 1\n",
    "    grayscale = 2\n",
    "    \n",
    "class WeightsInitialization(Enum):\n",
    "    normal = 1\n",
    "    xavier = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "data_dir = os.path.join('..', 'data')\n",
    "training_file = os.path.join(data_dir, 'train.p')\n",
    "validation_file = os.path.join(data_dir, 'valid.p')\n",
    "testing_file = os.path.join(data_dir, '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 = np.array(train['features']), np.array(train['labels'])\n",
    "X_valid, y_valid = np.array(valid['features']), np.array(valid['labels'])\n",
    "X_test, y_test = np.array(test['features']), np.array(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": 4,
   "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": [
    "import pandas as pd\n",
    "\n",
    "# Number of training examples\n",
    "n_train = y_train.shape[0]\n",
    "\n",
    "# Number of validation examples\n",
    "n_validation = y_valid.shape[0]\n",
    "\n",
    "# Number of testing examples.\n",
    "n_test = y_test.shape[0]\n",
    "\n",
    "# What's the shape of an traffic sign image?\n",
    "image_shape = X_train.shape[1:]\n",
    "\n",
    "# How many unique classes/labels there are in the dataset.\n",
    "train_labels_df = pd.DataFrame(y_train, columns=['class'])\n",
    "n_classes = train_labels_df.nunique()[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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Data exploration visualization code\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_images_as_grid(images, labels=None, images_per_row=5, figsize=(10, 6), cmap=None):\n",
    "    if labels is None:\n",
    "        labels = []\n",
    "\n",
    "    rows = math.ceil(len(images) / images_per_row)\n",
    "    columns = images_per_row\n",
    "\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    for i in range(0, len(images)):\n",
    "        ax = fig.add_subplot(rows, columns, i + 1)\n",
    "        if i < len(labels):\n",
    "            ax.set_title(labels[i])\n",
    "        ax.imshow(images[i], cmap=cmap)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "# Select 5 random images from each of the train, validation and test datasets to check that\n",
    "# the data consists of valid images.\n",
    "random.seed(RANDOM_SEED)\n",
    "num_samples = 5\n",
    "dataset_dict = {'train': X_train, 'validation': X_valid, 'test': X_test}\n",
    "\n",
    "for key in dataset_dict:\n",
    "    dataset = dataset_dict[key]\n",
    "    random_images = random.choices(dataset, k=num_samples)\n",
    "    plot_images_as_grid(random_images, labels=[f'{key} sample {i+1}' for i in range(num_samples)], images_per_row=num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of samples=34799 which should equal n_train=34799\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2acff02e0b8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dist = train_labels_df.groupby('class').size()\n",
    "\n",
    "# If the sum of our class distribution stats and n_train are equal, everything is fine\n",
    "print(f'total number of samples={sum(class_dist)} which should equal n_train={n_train}')\n",
    "\n",
    "class_dist.plot.barh(title='distribution of classes over the training set', figsize=(10, 10), colormap='tab10', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the distribution of the classes in the original training set. Outlined by the chart, it can be seen that the dataset is not balanced across classes. If the model is overfitting or shows some bias towards specific classes, this issue could be addressed. However, I will skip this step first due to time constraints."
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean before normalization:  82.6775890369964\n",
      "Mean after normalization:  0.015879638\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include \n",
    "### converting to grayscale, etc.\n",
    "from sklearn.utils import shuffle\n",
    "import cv2\n",
    "\n",
    "def normalize_image(image):\n",
    "    # return (image - 128) / 128\n",
    "    # return (image - 255) / 255\n",
    "    tmp_dst = np.zeros_like(image)\n",
    "    return cv2.normalize(image, tmp_dst, dtype=cv2.CV_32F)  # better normalization with less mean and higher performance\n",
    "\n",
    "def preprocess_data(X, y, color_space=ColorSpace.RGB, normalize=True):\n",
    "    if color_space is ColorSpace.grayscale:\n",
    "        X = np.array([cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) for image in X])\n",
    "    \n",
    "    if normalize is True:\n",
    "        X = np.array([normalize_image(image) for image in X])\n",
    "        \n",
    "    if color_space is ColorSpace.grayscale:\n",
    "        X = X[:, :, :, None]  # 2d convolutions expect third dimension for color even for grayscale images\n",
    "    return X, y\n",
    "\n",
    "print('Mean before normalization: ', np.mean(np.mean(X_train)))\n",
    "X_train_norm, y_train_norm = preprocess_data(X_train, y_train)\n",
    "print('Mean after normalization: ', np.mean(np.mean(X_train_norm)))\n",
    "\n",
    "# Check that grayscale conversion works properly:\n",
    "gray_img, _ = preprocess_data(X_train[:1], y_train[:1], color_space=ColorSpace.grayscale, normalize=False)\n",
    "\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "ax = fig.add_subplot(121)\n",
    "ax.set_title('original color image')\n",
    "ax.imshow(X_train[0])\n",
    "ax = fig.add_subplot(122)\n",
    "ax.set_title('grayscale image')\n",
    "ax.imshow(gray_img[0, :, :, 0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to increase the size of the training set, so that I can overcome overfitting issues, I use data augmentation to create additional transformed images. I carefully chose the ranges for random values to be within a limited range to get images which are still close to realistic samples. Also, a neural net should of course nonetheless be able to derive the meaning of the signs from the augmented images.\n",
    "\n",
    "Horizontal flip is not used as the meaning of traffic sign can depend on the direction where it shows. Not only do written numbers for speed limits look different, but a lot of signs also include arrows pointing into a specific direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bjoer\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "def augment_images(X_images, y_images, num_transforms=5):\n",
    "    gen = ImageDataGenerator(\n",
    "        rotation_range=15,\n",
    "        zoom_range=0.1,\n",
    "        width_shift_range=2, \n",
    "        height_shift_range=2)\n",
    "    \n",
    "    num_samples = len(X_images)\n",
    "    \n",
    "    num_augmentations = 0\n",
    "    augmented_images = []\n",
    "    labels = []\n",
    "    for X_aug, y_aug in gen.flow(X_images, y_images, batch_size=32, seed=RANDOM_SEED):\n",
    "        augmented_images.append(X_aug)\n",
    "        labels.append(y_aug)\n",
    "        num_augmentations += len(X_aug)\n",
    "        if num_augmentations >= num_samples * num_transforms:\n",
    "            break\n",
    "    \n",
    "    augmented_images = np.concatenate(augmented_images)\n",
    "    return np.asarray(augmented_images, dtype=np.uint8), np.concatenate(np.array(labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7296, 1639, 18024, 16049, 14628]\n",
      "Augmented data shape:\n",
      "X shape: (15, 32, 32, 3)\n",
      "y shape: (15,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_samples = 5\n",
    "num_transforms = 3\n",
    "random.seed(RANDOM_SEED)\n",
    "random_image_idcs = random.sample(range(0, n_train - 1), num_samples)\n",
    "print(random_image_idcs)\n",
    "\n",
    "X_samples, y_samples = np.array([X_train[i] for i in random_image_idcs]), np.array([y_train[i] for i in random_image_idcs])\n",
    "\n",
    "X_aug, y_aug = augment_images(X_samples, y_samples, num_transforms)\n",
    "print(f'Augmented data shape:\\nX shape: {X_aug.shape}\\ny shape: {y_aug.shape}')\n",
    "\n",
    "# plot original images\n",
    "fig = plot_images_as_grid(X_samples, \n",
    "                          labels=[f'class: {c}\\ntraining #{random_image_idcs[i]}' for i, c in enumerate(y_samples)])\n",
    "\n",
    "# plot augmented images\n",
    "fig = plot_images_as_grid(X_aug, \n",
    "                          labels=[f'class: {c}\\naugmented #{i+1}' for i, c in enumerate(y_aug)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create augmented training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found augmented training data in \"..\\data\\augmented_train.p\" - loading...\n",
      "\n",
      "Original training size: 34799\n",
      "Augmented training size: 208794\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_transforms = 5\n",
    "\n",
    "# Load augmented data from pickle file if it has already been created to save time.\n",
    "# Otherwise, create augmented data from training set.\n",
    "augmented_training_file = os.path.join(data_dir, 'augmented_train.p')\n",
    "if os.path.isfile(augmented_training_file):\n",
    "    print(f'Found augmented training data in \"{augmented_training_file}\" - loading...\\n')\n",
    "    \n",
    "    with open(augmented_training_file, mode='rb') as f:\n",
    "        augmented_train = pickle.load(f)\n",
    "    \n",
    "    X_augmented_train, y_augmented_train = np.array(augmented_train['features']), np.array(augmented_train['labels'])\n",
    "else:\n",
    "    print('No augmented data for training set found.\\nCreating augmented data (this can take a while)...\\n')\n",
    "    \n",
    "    X_augmented_train, y_augmented_train = augment_images(X_train, y_train, num_transforms=num_transforms)\n",
    "    # Include original training data\n",
    "    X_augmented_train = np.concatenate([X_train, X_augmented_train])\n",
    "    y_augmented_train = np.concatenate([y_train, y_augmented_train])\n",
    "    \n",
    "    print(f'Saving augmented dataset to \"{augmented_training_file}\"...')\n",
    "    with open(augmented_training_file, mode='wb') as f:\n",
    "        pickle.dump({\n",
    "            'features': X_augmented_train,\n",
    "            'labels': y_augmented_train\n",
    "        }, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "n_augmented_train = y_augmented_train.shape[0]\n",
    "print(f'Original training size: {n_train}\\nAugmented training size: {n_augmented_train}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I created some helper functions which are used to create the layers of my model and add summaries to every layer for TensorBoard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\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",
    "def add_layer_summary(W, b, activations):\n",
    "    tf.summary.histogram('weights', W)\n",
    "    tf.summary.histogram('biases', b)\n",
    "    tf.summary.histogram('activations', activations)\n",
    "    \n",
    "def get_init_weights(scope, shape, method=WeightsInitialization.normal):\n",
    "    with tf.variable_scope(scope):\n",
    "        if method is WeightsInitialization.xavier:\n",
    "            return tf.get_variable('W', shape=shape, initializer=tf.contrib.layers.xavier_initializer())\n",
    "        return tf.get_variable('W', shape=shape, initializer=tf.truncated_normal_initializer(mean=mu, stddev=sigma))\n",
    "    \n",
    "def conv_layer(X, size_in, size_out, name='conv', padding='VALID',\n",
    "               filter_size=5, stride=[1, 1, 1, 1], \n",
    "               pooling_size=[1, 2, 2, 1], pooling_stride=[1, 2, 2, 1], \n",
    "               init_method=WeightsInitialization.normal):\n",
    "    with tf.name_scope(name):\n",
    "        W = get_init_weights(name, shape=[filter_size, filter_size, size_in, size_out], method=init_method)\n",
    "        b = tf.Variable(tf.zeros(size_out), name='b')\n",
    "        conv = tf.nn.conv2d(X, W, strides=stride, padding=padding) + b\n",
    "        act = tf.nn.relu(conv)\n",
    "        add_layer_summary(W, b, act)\n",
    "        return tf.nn.max_pool(act, ksize=pooling_size, strides=pooling_stride, padding=padding)\n",
    "\n",
    "def fc_layer(X, size_in, size_out, name='fc', init_method=WeightsInitialization.normal):\n",
    "    with tf.name_scope(name):\n",
    "        W = get_init_weights(name, shape=[size_in, size_out], method=init_method)\n",
    "        b = tf.Variable(tf.zeros(size_out), name='b')\n",
    "        fc = tf.matmul(X, W) + b\n",
    "        act = tf.nn.relu(fc)\n",
    "        add_layer_summary(W, b, act)\n",
    "        return act\n",
    "\n",
    "def output_layer(X, size_in, size_out, name='output', init_method=WeightsInitialization.normal):\n",
    "    with tf.name_scope(name):\n",
    "        W = get_init_weights(name, shape=[size_in, size_out], method=init_method)\n",
    "        b = tf.Variable(tf.zeros(size_out), name='b')\n",
    "        out = tf.matmul(X, W) + b\n",
    "        tf.summary.histogram('weights', W)\n",
    "        tf.summary.histogram('biases', b)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, I created a LeNet-architecture by building each of the layers using the helper functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LeNet_model(input, dropout_keep_prob, n_channels=3, init_method=WeightsInitialization.normal):\n",
    "    # Layer 1: Convolutional. Input = 32x32xC. Output = 28x28x6.\n",
    "    # Pooling. Input = 28x28x6. Output = 14x14x6.\n",
    "    conv1 = conv_layer(input, size_in=n_channels, size_out=6, name='conv1', init_method=init_method)\n",
    "\n",
    "    # Layer 2: Convolutional. Output = 10x10x16.\n",
    "    # Pooling. Input = 10x10x16. Output = 5x5x16.\n",
    "    conv2 = conv_layer(conv1, size_in=6, size_out=16, name='conv2', init_method=init_method)\n",
    "\n",
    "    # Flatten. Input = 5x5x16. Output = 400.\n",
    "    fc0 = flatten(conv2)\n",
    "\n",
    "    # Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "    fc1 = fc_layer(fc0, size_in=400, size_out=120, name='fc1', init_method=init_method)\n",
    "    fc1_out = tf.nn.dropout(fc1, dropout_keep_prob, name='fc1_dropout')\n",
    "\n",
    "    # Layer 4: Fully Connected. Input = 120. Output = 84.\n",
    "    fc2 = fc_layer(fc1_out, size_in=120, size_out=84, name='fc2', init_method=init_method)\n",
    "    fc2_out = tf.nn.dropout(fc2, dropout_keep_prob, name='fc2_dropout')\n",
    "\n",
    "    # Layer 5: Fully Connected. Input = 84. Output = n_classes.\n",
    "    logits = output_layer(fc2_out, size_in=84, size_out=n_classes, name='output', init_method=init_method)\n",
    "    return logits, conv1, conv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is then used inside a class that takes care of feeding data to the model to train, validate and test the neural net. The class provides support for different values for the hyperparameters to tune them to our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG_DIR = 'logs/'\n",
    "MODEL_DIR = 'models/'\n",
    "\n",
    "class TrafficSignNetwork:\n",
    "    def __init__(self, model_name, batch_size=128, learning_rate=1e-2, color_space=ColorSpace.RGB, init_method=WeightsInitialization.normal):\n",
    "        self.model_name = model_name\n",
    "        self.batch_size = batch_size\n",
    "        self.learning_rate = learning_rate\n",
    "        \n",
    "        tf.reset_default_graph()\n",
    "        tf.set_random_seed(RANDOM_SEED)\n",
    "        \n",
    "        n_channels = 1 if color_space is ColorSpace.grayscale else 3\n",
    "        \n",
    "        self.x = tf.placeholder(tf.float32, [None, 32, 32, n_channels], name='features')\n",
    "        self.y = tf.placeholder(tf.int32, [None], name='labels')\n",
    "        one_hot_y = tf.one_hot(self.y, n_classes)\n",
    "        \n",
    "        self.keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "        image_summary = tf.summary.image('input', self.x, 3)\n",
    "        self.logits, self.conv1, self.conv2 = LeNet_model(self.x, self.keep_prob, n_channels=n_channels, init_method=init_method)\n",
    "\n",
    "        cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=one_hot_y, logits=self.logits)\n",
    "        ordinary_loss = tf.reduce_mean(cross_entropy)\n",
    "        self.loss = ordinary_loss + tf.reduce_sum(tf.losses.get_regularization_losses())\n",
    "\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "        self.loss_minimize = optimizer.minimize(self.loss)\n",
    "\n",
    "        correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(one_hot_y, 1))\n",
    "        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "        self.model_summary = tf.summary.merge_all()\n",
    "\n",
    "        with tf.name_scope('performance'):\n",
    "            self.loss_ph = tf.placeholder(tf.float32, shape=None, name='loss_summary')\n",
    "            loss_summary = tf.summary.scalar('loss', self.loss_ph)\n",
    "\n",
    "            self.accuracy_ph = tf.placeholder(tf.float32, shape=None, name='accuracy_summary')\n",
    "            accuracy_summary = tf.summary.scalar('accuracy', self.accuracy_ph)\n",
    "\n",
    "        self.performance_summaries = tf.summary.merge([loss_summary, accuracy_summary])\n",
    "        \n",
    "        self.saver = tf.train.Saver()\n",
    "    \n",
    "    def train_and_validate(self, X_train, y_train, X_valid, y_valid, epochs=10):\n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            \n",
    "            log_base_dir = f'{LOG_DIR}/{self.model_name}'\n",
    "            train_writer = tf.summary.FileWriter(f'{log_base_dir}/train', sess.graph)\n",
    "            valid_writer = tf.summary.FileWriter(f'{log_base_dir}/valid', sess.graph)\n",
    "\n",
    "            print(f'Training model \"{self.model_name}\"...')\n",
    "\n",
    "            for epoch in range(epochs):\n",
    "                avg_train_acc, train_summ = self.train_epoch(sess, X_train, y_train)\n",
    "                for s in train_summ:\n",
    "                    if s is not None:\n",
    "                        train_writer.add_summary(s, epoch)\n",
    "                \n",
    "                avg_valid_acc, valid_summ = self.validate_epoch(sess, X_valid, y_valid)\n",
    "                valid_writer.add_summary(valid_summ, epoch)\n",
    "                \n",
    "                print(f'EPOCH {epoch}: avg training accuracy: {avg_train_acc:.3f} - avg validation accuracy: {avg_valid_acc:.3f}')\n",
    "\n",
    "            print()\n",
    "            self.saver.save(sess, os.path.join(MODEL_DIR, f'{self.model_name}.ckpt'))\n",
    "            print('Model saved...')\n",
    "    \n",
    "    def train_epoch(self, session, X_train, y_train):\n",
    "        train_loss = []\n",
    "        train_acc = []\n",
    "        train_batch_weights = []\n",
    "        model_summ = None\n",
    "        \n",
    "        X_train, y_train = shuffle(X_train, y_train, random_state=RANDOM_SEED)\n",
    "        \n",
    "        num_examples = len(X_train)\n",
    "        total_batches = num_examples // self.batch_size\n",
    "        for offset in range(0, num_examples, self.batch_size):\n",
    "            end = offset + self.batch_size\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "\n",
    "            # get distributions and input from just the first batch of the epoch\n",
    "            if offset == 0:\n",
    "                l, acc, model_summ, _ = session.run([\n",
    "                    self.loss, \n",
    "                    self.accuracy, \n",
    "                    self.model_summary, \n",
    "                    self.loss_minimize\n",
    "                ], feed_dict={self.x: batch_x, self.y: batch_y, self.keep_prob: 0.5})\n",
    "            else:\n",
    "                l, acc, _ = session.run([\n",
    "                    self.loss, \n",
    "                    self.accuracy, \n",
    "                    self.loss_minimize\n",
    "                ], feed_dict={self.x: batch_x, self.y: batch_y, self.keep_prob: 0.5})\n",
    "\n",
    "            train_loss.append(l)\n",
    "            train_acc.append(acc)\n",
    "            train_batch_weights.append(len(batch_x))\n",
    "\n",
    "        avg_train_loss = np.average(train_loss, weights=train_batch_weights)\n",
    "        avg_train_acc = np.average(train_acc, weights=train_batch_weights)\n",
    "        \n",
    "        performance_summary = session.run(self.performance_summaries, \n",
    "                                          feed_dict={self.loss_ph: avg_train_loss, self.accuracy_ph: avg_train_acc})\n",
    "        return avg_train_acc, [performance_summary, model_summ]\n",
    "    \n",
    "    def validate_epoch(self, session, X_valid, y_valid):\n",
    "        valid_loss = []\n",
    "        valid_acc = []\n",
    "        valid_batch_weights = []\n",
    "        \n",
    "        valid_len = len(X_valid)\n",
    "        for offset in range(0, valid_len, self.batch_size):\n",
    "            end = offset + self.batch_size\n",
    "            batch_x, batch_y = X_valid[offset:end], y_valid[offset:end]\n",
    "\n",
    "            l, acc = session.run([\n",
    "                self.loss, \n",
    "                self.accuracy\n",
    "            ], feed_dict={self.x: batch_x, self.y: batch_y, self.keep_prob: 1.0})\n",
    "\n",
    "            valid_loss.append(l)\n",
    "            valid_acc.append(acc)\n",
    "            valid_batch_weights.append(len(batch_x))\n",
    "\n",
    "        avg_valid_loss = np.average(valid_loss, weights=valid_batch_weights)\n",
    "        avg_valid_acc = np.average(valid_acc, weights=valid_batch_weights)\n",
    "        \n",
    "        performance_summary = session.run(self.performance_summaries, \n",
    "                                          feed_dict={self.loss_ph: avg_valid_loss, self.accuracy_ph: avg_valid_acc})\n",
    "        return avg_valid_acc, performance_summary\n",
    "    \n",
    "    def test(self, X_test, y_test):\n",
    "        with tf.Session() as sess:\n",
    "            self.saver.restore(sess, tf.train.latest_checkpoint(os.path.join(MODEL_DIR)))\n",
    "            \n",
    "            total_acc = 0\n",
    "            num_samples = len(X_test)\n",
    "            for offset in range(0, num_samples, self.batch_size):\n",
    "                end = offset + self.batch_size\n",
    "                batch_x, batch_y = X_test[offset:end], y_test[offset:end]\n",
    "\n",
    "                acc = sess.run(self.accuracy, feed_dict={self.x: batch_x, self.y: batch_y, self.keep_prob: 1.0})\n",
    "\n",
    "                total_acc += (acc * len(batch_x))\n",
    "            \n",
    "            test_acc = total_acc/num_samples\n",
    "            print(\"Test Accuracy = {:.3f}\".format(test_acc))\n",
    "            \n",
    "    def predict(self, X):\n",
    "        with tf.Session() as sess:\n",
    "            self.saver.restore(sess, tf.train.latest_checkpoint(os.path.join(MODEL_DIR)))\n",
    "            # sess.run(tf.global_variables_initializer())\n",
    "            \n",
    "            return sess.run(tf.nn.top_k(\n",
    "                tf.nn.softmax(self.logits), \n",
    "                k=5), feed_dict={self.x: X, self.keep_prob: 1.0})\n",
    "        \n",
    "    def outputFeatureMap(self, image_input, tf_activation, activation_min=-1, activation_max=-1, plt_num=1, restore=True):\n",
    "        with tf.Session() as sess:\n",
    "            if restore is True:\n",
    "                self.saver.restore(sess, tf.train.latest_checkpoint(os.path.join(MODEL_DIR)))\n",
    "            else:\n",
    "                sess.run(tf.global_variables_initializer())\n",
    "            \n",
    "            # Here make sure to preprocess your image_input in a way your network expects\n",
    "            # with size, normalization, etc if needed\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={self.x : image_input})\n",
    "            featuremaps = activation.shape[3]\n",
    "            plt.figure(plt_num, figsize=(16, 16))\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 and 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\")"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "Calculate and report the accuracy on the training and validation set.\n",
    "Once a final model architecture is selected, the accuracy on the test set should be calculated and reported as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model \"LeNet_grayscale_R-0.001_normal_1539007551\"...\n",
      "EPOCH 0: avg training accuracy: 0.504 - avg validation accuracy: 0.852\n",
      "EPOCH 1: avg training accuracy: 0.749 - avg validation accuracy: 0.911\n",
      "EPOCH 2: avg training accuracy: 0.819 - avg validation accuracy: 0.937\n",
      "EPOCH 3: avg training accuracy: 0.851 - avg validation accuracy: 0.950\n",
      "EPOCH 4: avg training accuracy: 0.869 - avg validation accuracy: 0.959\n",
      "EPOCH 5: avg training accuracy: 0.881 - avg validation accuracy: 0.964\n",
      "EPOCH 6: avg training accuracy: 0.891 - avg validation accuracy: 0.962\n",
      "EPOCH 7: avg training accuracy: 0.897 - avg validation accuracy: 0.964\n",
      "EPOCH 8: avg training accuracy: 0.903 - avg validation accuracy: 0.971\n",
      "EPOCH 9: avg training accuracy: 0.907 - avg validation accuracy: 0.971\n",
      "\n",
      "Model saved...\n",
      "Training model \"LeNet_grayscale_R-0.001_xavier_1539007841\"...\n",
      "EPOCH 0: avg training accuracy: 0.493 - avg validation accuracy: 0.847\n",
      "EPOCH 1: avg training accuracy: 0.742 - avg validation accuracy: 0.904\n",
      "EPOCH 2: avg training accuracy: 0.797 - avg validation accuracy: 0.933\n",
      "EPOCH 3: avg training accuracy: 0.826 - avg validation accuracy: 0.938\n",
      "EPOCH 4: avg training accuracy: 0.841 - avg validation accuracy: 0.948\n",
      "EPOCH 5: avg training accuracy: 0.851 - avg validation accuracy: 0.958\n",
      "EPOCH 6: avg training accuracy: 0.859 - avg validation accuracy: 0.951\n",
      "EPOCH 7: avg training accuracy: 0.866 - avg validation accuracy: 0.959\n",
      "EPOCH 8: avg training accuracy: 0.872 - avg validation accuracy: 0.963\n",
      "EPOCH 9: avg training accuracy: 0.876 - avg validation accuracy: 0.955\n",
      "\n",
      "Model saved...\n",
      "Training model \"LeNet_RGB_R-0.001_normal_1539008189\"...\n",
      "EPOCH 0: avg training accuracy: 0.516 - avg validation accuracy: 0.822\n",
      "EPOCH 1: avg training accuracy: 0.739 - avg validation accuracy: 0.888\n",
      "EPOCH 2: avg training accuracy: 0.799 - avg validation accuracy: 0.913\n",
      "EPOCH 3: avg training accuracy: 0.830 - avg validation accuracy: 0.927\n",
      "EPOCH 4: avg training accuracy: 0.849 - avg validation accuracy: 0.930\n",
      "EPOCH 5: avg training accuracy: 0.863 - avg validation accuracy: 0.937\n",
      "EPOCH 6: avg training accuracy: 0.873 - avg validation accuracy: 0.944\n",
      "EPOCH 7: avg training accuracy: 0.881 - avg validation accuracy: 0.944\n",
      "EPOCH 8: avg training accuracy: 0.888 - avg validation accuracy: 0.950\n",
      "EPOCH 9: avg training accuracy: 0.893 - avg validation accuracy: 0.953\n",
      "\n",
      "Model saved...\n",
      "Training model \"LeNet_RGB_R-0.001_xavier_1539008738\"...\n",
      "EPOCH 0: avg training accuracy: 0.523 - avg validation accuracy: 0.865\n",
      "EPOCH 1: avg training accuracy: 0.771 - avg validation accuracy: 0.916\n",
      "EPOCH 2: avg training accuracy: 0.825 - avg validation accuracy: 0.926\n",
      "EPOCH 3: avg training accuracy: 0.854 - avg validation accuracy: 0.937\n",
      "EPOCH 4: avg training accuracy: 0.870 - avg validation accuracy: 0.941\n",
      "EPOCH 5: avg training accuracy: 0.879 - avg validation accuracy: 0.943\n",
      "EPOCH 6: avg training accuracy: 0.888 - avg validation accuracy: 0.946\n",
      "EPOCH 7: avg training accuracy: 0.893 - avg validation accuracy: 0.950\n",
      "EPOCH 8: avg training accuracy: 0.897 - avg validation accuracy: 0.953\n",
      "EPOCH 9: avg training accuracy: 0.900 - avg validation accuracy: 0.957\n",
      "\n",
      "Model saved...\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# Evaluating different hyperparameters and network structures:\n",
    "for learning_rate in [1e-3]:\n",
    "    for color_space in [ColorSpace.grayscale, ColorSpace.RGB]:\n",
    "        for weights_init in [WeightsInitialization.normal, WeightsInitialization.xavier]:\n",
    "            X_train_pre, y_train_pre = preprocess_data(X_augmented_train, y_augmented_train, color_space=color_space)\n",
    "            X_valid_pre, y_valid_pre = preprocess_data(X_valid, y_valid, color_space=color_space)\n",
    "\n",
    "            model_name = f'LeNet_{color_space.name}_R-{learning_rate}_{weights_init.name}_{int(time.time())}'\n",
    "\n",
    "            net = TrafficSignNetwork(model_name, batch_size=32, learning_rate=learning_rate, color_space=color_space, init_method=weights_init)\n",
    "            net.train_and_validate(X_train_pre, y_train_pre, X_valid_pre, y_valid_pre, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bit surprising for me, grayscale images with a learning rate of 1e-3 and normal initialization show better accuracy than RGB images with xavier initialization. Training for more epochs leads to overfitting, so I just tested the model with training stopped at 9 epochs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model \"LeNet_grayscale_R-0.001_normal_1539014937\"...\n",
      "EPOCH 0: avg training accuracy: 0.501 - avg validation accuracy: 0.837\n",
      "EPOCH 1: avg training accuracy: 0.751 - avg validation accuracy: 0.918\n",
      "EPOCH 2: avg training accuracy: 0.822 - avg validation accuracy: 0.937\n",
      "EPOCH 3: avg training accuracy: 0.855 - avg validation accuracy: 0.956\n",
      "EPOCH 4: avg training accuracy: 0.875 - avg validation accuracy: 0.955\n",
      "EPOCH 5: avg training accuracy: 0.888 - avg validation accuracy: 0.967\n",
      "EPOCH 6: avg training accuracy: 0.897 - avg validation accuracy: 0.963\n",
      "EPOCH 7: avg training accuracy: 0.905 - avg validation accuracy: 0.963\n",
      "EPOCH 8: avg training accuracy: 0.910 - avg validation accuracy: 0.966\n",
      "\n",
      "Model saved...\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "learning_rate = 1e-3\n",
    "color_space = ColorSpace.grayscale\n",
    "weights_init = WeightsInitialization.normal\n",
    "\n",
    "X_train_pre, y_train_pre = preprocess_data(X_augmented_train, y_augmented_train, color_space=color_space)\n",
    "X_valid_pre, y_valid_pre = preprocess_data(X_valid, y_valid, color_space=color_space)\n",
    "\n",
    "model_name = f'LeNet_{color_space.name}_R-{learning_rate}_{weights_init.name}_{int(time.time())}'\n",
    "\n",
    "net = TrafficSignNetwork(model_name, batch_size=32, learning_rate=learning_rate, color_space=color_space, init_method=weights_init)\n",
    "net.train_and_validate(X_train_pre, y_train_pre, X_valid_pre, y_valid_pre, epochs=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/LeNet_grayscale_R-0.001_normal_1539014937.ckpt\n",
      "Test Accuracy = 0.947\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "learning_rate = 1e-3\n",
    "color_space = ColorSpace.grayscale\n",
    "weights_init = WeightsInitialization.normal\n",
    "\n",
    "net = TrafficSignNetwork(f'LeNet_final_{color_space.name}_R-{learning_rate}_{weights_init.name}_{int(time.time())}', \n",
    "                         batch_size=32, learning_rate=learning_rate, color_space=color_space, init_method=weights_init)\n",
    "X_test_pre, y_test_pre = preprocess_data(X_test, y_test, color_space=color_space)\n",
    "net.test(X_test_pre, y_test_pre)"
   ]
  },
  {
   "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": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_signs_dir = os.path.join('test_images')\n",
    "test_signs_csv = pd.read_csv(os.path.join(test_signs_dir, 'test_images.csv'))\n",
    "\n",
    "test_signs = []\n",
    "test_signs_labels = []\n",
    "for [class_id, image_file] in test_signs_csv.values:\n",
    "    test_signs.append(\n",
    "        plt.imread(os.path.join(test_signs_dir, image_file))\n",
    "    )\n",
    "    test_signs_labels.append(class_id)\n",
    "    \n",
    "plot_labels = [f'image #{i+1}\\nclass {l}' for i, l in enumerate(test_signs_labels)]\n",
    "fig = plot_images_as_grid(test_signs, labels=plot_labels)\n",
    "fig.savefig('writeup_images/test_images.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "sign_labels_csv = pd.read_csv('signnames.csv')\n",
    "\n",
    "def get_sign_name(class_id):\n",
    "    result_df = sign_labels_csv.query(f'ClassId == {class_id}')\n",
    "    return result_df.iloc[0]['SignName']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/LeNet_grayscale_R-0.001_normal_1539014937.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_signs_pre, _ = preprocess_data(test_signs, None, color_space=color_space)\n",
    "predictions = np.array(\n",
    "    net.predict(test_signs_pre))\n",
    "\n",
    "# get top prediction for each image\n",
    "prediction_labels = []\n",
    "prediction_names = []\n",
    "for [prob, label] in predictions.T[0]:\n",
    "    prediction_labels.append(label)\n",
    "    prediction_names.append(get_sign_name(label))\n",
    "\n",
    "plot_labels = [\n",
    "    f'image #{i+1}\\nlabel: {get_sign_name(l)}\\npredicted: {prediction_names[i]}' for i, l in enumerate(test_signs_labels)]\n",
    "fig = plot_images_as_grid(test_signs, labels=plot_labels, figsize=(20, 10))\n",
    "fig.savefig('writeup_images/test_images_predictions.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for new test images is: 80.0%\n"
     ]
    }
   ],
   "source": [
    "# Compare predicted labels to the ground-truth data\n",
    "correct_predictions = 0\n",
    "for i, prediction in enumerate(prediction_labels):\n",
    "    if prediction == test_signs_labels[i]:\n",
    "        correct_predictions += 1\n",
    "accuracy = correct_predictions / len(prediction_labels) * 100\n",
    "\n",
    "print(f'Accuracy for new test images is: {accuracy:.1f}%')"
   ]
  },
  {
   "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": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2016x864 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from textwrap import fill\n",
    "\n",
    "probs = predictions[0]*100\n",
    "labels = predictions[1].astype(int)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(28, 12))\n",
    "for i in range(len(probs)):\n",
    "    chart_probs = probs[i].round(1)\n",
    "    chart_labels = labels[i]\n",
    "    \n",
    "    label_sign_names = [fill(get_sign_name(label), 20) for label in chart_labels]\n",
    "    chart_df = pd.DataFrame({ 'probability': chart_probs, 'labels': label_sign_names })\n",
    "    \n",
    "    axes[0, i].imshow(test_signs[i])\n",
    "    chart = chart_df.plot.bar(x='labels', \n",
    "                              yticks=np.linspace(0, 100, 11), \n",
    "                              ylim=(0, 105), \n",
    "                              rot=35, \n",
    "                              grid=True, \n",
    "                              ax=axes[1, i])\n",
    "    \n",
    "    true_label = get_sign_name(test_signs_labels[i])\n",
    "    ax_title = f'image #{i+1}\\ntrue label: {true_label}'\n",
    "    axes[0, i].set_title(ax_title)\n",
    "    axes[1, i].set_title(ax_title)\n",
    "    axes[1, i].set_xlabel('')  # Remove labels for x axis as xticks are self-explanatory\n",
    "    \n",
    "fig.savefig('writeup_images/test_images_softmax_charts.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the chart image in a new tab to watch in detail."
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "I have added the function `outputFeatureMap` to my class above. In the following, I examine the feature maps of the first conv layer of an untrained neural net and then of my trained network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_image = 1  # index of one of the 5 test signs on which to examine the feature maps\n",
    "\n",
    "feature_net = TrafficSignNetwork(f'LeNet_final_{color_space.name}_R-{learning_rate}_{weights_init.name}_{int(time.time())}', \n",
    "                         batch_size=32, learning_rate=learning_rate, color_space=color_space, init_method=weights_init)\n",
    "feature_net.outputFeatureMap(test_signs_pre[feature_image, None], getattr(feature_net, 'conv1'), restore=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models/LeNet_grayscale_R-0.001_normal_1539014937.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_net.outputFeatureMap(test_signs_pre[feature_image, None], getattr(feature_net, 'conv1'), restore=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my opinion, it shows some interesting results. The trained network picks up edges and specific details of the sign. In almost all feature maps the triangular shape of the sign is outlined. Moreover, it seems that especially feature map 0 and 2 recognized the indicator for a bumpy road at the bottom of the sign.\n",
    "However, two filters (FeatureMap 1 and FeatureMap 4) do not activate at all which we could examine further and find out for which types of images it would activate. But, that is beyond the scope of this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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