Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "## Deep Learning\n",
    "\n",
    "## Project: Build a Traffic Sign Recognition Classifier\n",
    "\n",
    "In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary. \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission. \n",
    "\n",
    "In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) that can be used to guide the writing process. Completing the code template and writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/481/view) for this project.\n",
    "\n",
    "The [rubric](https://review.udacity.com/#!/rubrics/481/view) contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this Ipython notebook and also discuss the results in the writeup file.\n",
    "\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34799, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = \"dataset\\\\train.p\"\n",
    "validation_file= \"dataset\\\\valid.p\"\n",
    "testing_file = \"dataset\\\\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_rgb, y_train = train['features'], train['labels']\n",
    "X_valid_rgb, y_valid = valid['features'], valid['labels']\n",
    "X_test_rgb, y_test = test['features'], test['labels']\n",
    "print(X_train_rgb.shape)"
   ]
  },
  {
   "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 testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "import numpy as np\n",
    "# TODO: Number of training examples\n",
    "n_train = len(X_train_rgb)\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = len(X_valid_rgb)\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = len(X_test_rgb)\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train_rgb[1].shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = len(np.unique(y_train))\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "signs_names = []\n",
    "df = pd.read_csv('./signnames.csv')\n",
    "for name in df['SignName']:\n",
    "    signs_names.append(name)\n",
    "\n",
    "app_times = np.unique(y_train, return_counts=True)\n",
    "plt.xticks(rotation=90)\n",
    "plt.bar(app_times[0], app_times[1], tick_label=signs_names)    \n",
    "plt.show()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import random\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "fig,axs=plt.subplots(2,5,figsize=(20,7))\n",
    "fig.subplots_adjust(hspace=.2,wspace=.001)\n",
    "axs=axs.ravel()\n",
    "for i in range(10):\n",
    "    index=random.randint(0,len(X_train_rgb))\n",
    "    image=X_train_rgb[index]\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image)\n",
    "    axs[i].set_title(y_train[index])"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include \n",
    "### converting to grayscale, etc.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "X_train=np.sum(X_train_rgb/3, axis=3, keepdims=True)\n",
    "X_test=np.sum(X_test_rgb/3, axis=3, keepdims=True)\n",
    "X_valid=np.sum(X_valid_rgb/3, axis=3, keepdims=True)\n",
    "X_train=(X_train - 128)/128\n",
    "X_test=(X_test - 128)/128\n",
    "X_train,y_train=shuffle(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import tensorflow as tf\n",
    "\n",
    "epochs=30\n",
    "batch_size=150\n",
    "\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "def LeNet(x):\n",
    "    mu=0\n",
    "    sigma=0.1\n",
    "    \n",
    "    conv1_w=tf.Variable(tf.truncated_normal(shape=(5,5,1,6),mean=mu,stddev=sigma))\n",
    "    conv1_b=tf.Variable(tf.zeros(6))\n",
    "    conv1=tf.nn.conv2d(x,conv1_w,strides=[1,1,1,1],padding='VALID')+conv1_b\n",
    "    conv1=tf.nn.relu(conv1)\n",
    "    conv1=tf.nn.max_pool(conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID')\n",
    "    \n",
    "    conv2_w=tf.Variable(tf.truncated_normal(shape=(5,5,6,16),mean=mu,stddev=sigma))\n",
    "    conv2_b=tf.Variable(tf.zeros(16))\n",
    "    conv2=tf.nn.conv2d(conv1,conv2_w,strides=[1,1,1,1],padding='VALID')+conv2_b\n",
    "    conv2=tf.nn.relu(conv2)\n",
    "    conv2=tf.nn.max_pool(conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID')\n",
    "    \n",
    "    conv3_w=tf.Variable(tf.truncated_normal(shape=(5,5,16,400),mean=mu,stddev=sigma))\n",
    "    conv3_b=tf.Variable(tf.zeros(400))\n",
    "    conv3   = tf.nn.conv2d(conv2, conv3_w, strides=[1, 1, 1, 1], padding='VALID') + conv3_b\n",
    "    conv3 = tf.nn.relu(conv3)\n",
    "    \n",
    "    fc0=flatten(conv3)\n",
    "    \n",
    "    fc1_w=tf.Variable(tf.truncated_normal(shape=(400,120),mean=mu,stddev=sigma))\n",
    "    fc1_b=tf.Variable(tf.zeros(120))\n",
    "    fc1=tf.matmul(fc0,fc1_w)+fc1_b\n",
    "    fc1=tf.nn.relu(fc1)\n",
    "    fc1=tf.nn.dropout(fc1,keep_prob)\n",
    "    \n",
    "    fc2_w=tf.Variable(tf.truncated_normal(shape=(120,84),mean=mu,stddev=sigma))\n",
    "    fc2_b=tf.Variable(tf.zeros(84))\n",
    "    fc2=tf.matmul(fc1,fc2_w)+fc2_b\n",
    "    fc2=tf.nn.relu(fc2)\n",
    "    fc2=tf.nn.dropout(fc2,keep_prob)\n",
    "    \n",
    "    fc3_w=tf.Variable(tf.truncated_normal(shape=(84,n_classes),mean=mu,stddev=sigma))\n",
    "    fc3_b=tf.Variable(tf.zeros(n_classes))\n",
    "    logits=tf.matmul(fc2,fc3_w)+fc3_b\n",
    "    \n",
    "    return logits\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-8-2610c6b441ab>:11: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed.\n",
    "x=tf.placeholder(tf.float32,(None,32,32,1))\n",
    "y=tf.placeholder(tf.int32,(None))\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "one_hot_y=tf.one_hot(y,n_classes)\n",
    "logits=LeNet(x)\n",
    "cross_entropy=tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y,logits=logits)\n",
    "loss=tf.reduce_mean(cross_entropy)\n",
    "optimizer=tf.train.AdamOptimizer(learning_rate=0.00097)\n",
    "training=optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-9-3d2ee4eea275>:1: arg_max (from tensorflow.python.ops.gen_math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `argmax` instead\n"
     ]
    }
   ],
   "source": [
    "correct_prediction=tf.equal(tf.arg_max(logits,1),tf.arg_max(one_hot_y,1))\n",
    "accuracy_operation=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "saver=tf.train.Saver()\n",
    "\n",
    "def evaluate(X_data,y_data):\n",
    "    num_examples=len(X_data)\n",
    "    total_accuracy=0\n",
    "    sess=tf.get_default_session()\n",
    "    for offset in range(0,num_examples,batch_size):\n",
    "        batch_x,batch_y=X_data[offset:offset+batch_size],y_data[offset:offset+batch_size]\n",
    "        accuracy=sess.run(accuracy_operation,feed_dict={x:batch_x,y:batch_y,keep_prob:1.0})\n",
    "        total_accuracy+=(accuracy*len(batch_x))\n",
    "        \n",
    "    return total_accuracy/num_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 4563718365486527491\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 3162085785\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 6800002227496049498\n",
      "physical_device_desc: \"device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib \n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 ...\n",
      "Training Accuracy = 0.703\n",
      "Validation Accuracy = 0.651\n",
      "\n",
      "EPOCH 2 ...\n",
      "Training Accuracy = 0.883\n",
      "Validation Accuracy = 0.822\n",
      "\n",
      "EPOCH 3 ...\n",
      "Training Accuracy = 0.931\n",
      "Validation Accuracy = 0.867\n",
      "\n",
      "EPOCH 4 ...\n",
      "Training Accuracy = 0.946\n",
      "Validation Accuracy = 0.895\n",
      "\n",
      "EPOCH 5 ...\n",
      "Training Accuracy = 0.964\n",
      "Validation Accuracy = 0.889\n",
      "\n",
      "EPOCH 6 ...\n",
      "Training Accuracy = 0.974\n",
      "Validation Accuracy = 0.918\n",
      "\n",
      "EPOCH 7 ...\n",
      "Training Accuracy = 0.984\n",
      "Validation Accuracy = 0.934\n",
      "\n",
      "EPOCH 8 ...\n",
      "Training Accuracy = 0.988\n",
      "Validation Accuracy = 0.933\n",
      "\n",
      "EPOCH 9 ...\n",
      "Training Accuracy = 0.985\n",
      "Validation Accuracy = 0.934\n",
      "\n",
      "EPOCH 10 ...\n",
      "Training Accuracy = 0.992\n",
      "Validation Accuracy = 0.926\n",
      "\n",
      "EPOCH 11 ...\n",
      "Training Accuracy = 0.993\n",
      "Validation Accuracy = 0.934\n",
      "\n",
      "EPOCH 12 ...\n",
      "Training Accuracy = 0.993\n",
      "Validation Accuracy = 0.925\n",
      "\n",
      "EPOCH 13 ...\n",
      "Training Accuracy = 0.996\n",
      "Validation Accuracy = 0.938\n",
      "\n",
      "EPOCH 14 ...\n",
      "Training Accuracy = 0.996\n",
      "Validation Accuracy = 0.941\n",
      "\n",
      "EPOCH 15 ...\n",
      "Training Accuracy = 0.993\n",
      "Validation Accuracy = 0.927\n",
      "\n",
      "EPOCH 16 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.942\n",
      "\n",
      "EPOCH 17 ...\n",
      "Training Accuracy = 0.998\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 18 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.944\n",
      "\n",
      "EPOCH 19 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.937\n",
      "\n",
      "EPOCH 20 ...\n",
      "Training Accuracy = 0.999\n",
      "Validation Accuracy = 0.945\n",
      "\n",
      "EPOCH 21 ...\n",
      "Training Accuracy = 0.998\n",
      "Validation Accuracy = 0.943\n",
      "\n",
      "EPOCH 22 ...\n",
      "Training Accuracy = 0.998\n",
      "Validation Accuracy = 0.939\n",
      "\n",
      "EPOCH 23 ...\n",
      "Training Accuracy = 0.999\n",
      "Validation Accuracy = 0.947\n",
      "\n",
      "EPOCH 24 ...\n",
      "Training Accuracy = 1.000\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 25 ...\n",
      "Training Accuracy = 0.999\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 26 ...\n",
      "Training Accuracy = 0.998\n",
      "Validation Accuracy = 0.939\n",
      "\n",
      "EPOCH 27 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.949\n",
      "\n",
      "EPOCH 28 ...\n",
      "Training Accuracy = 0.999\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 29 ...\n",
      "Training Accuracy = 1.000\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 30 ...\n",
      "Training Accuracy = 1.000\n",
      "Validation Accuracy = 0.949\n",
      "\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples=len(X_train)\n",
    "    \n",
    "    print(\"Training...\")\n",
    "    print()\n",
    "    validation_accuracy_figure = []\n",
    "    test_accuracy_figure = []\n",
    "    for i in range(epochs):\n",
    "        X_train,y_train=shuffle(X_train,y_train)\n",
    "        for offset in range(0,num_examples,batch_size):\n",
    "            end=offset+batch_size\n",
    "            batch_x,batch_y=X_train[offset:end],y_train[offset:end]\n",
    "            sess.run(training,feed_dict={x:batch_x,y:batch_y,keep_prob:0.5})\n",
    "            \n",
    "        validation_accuracy=evaluate(X_valid,y_valid)\n",
    "        validation_accuracy_figure.append(validation_accuracy)\n",
    "        \n",
    "        train_accuracy = evaluate(X_train, y_train)\n",
    "        test_accuracy_figure.append(train_accuracy)\n",
    "        print(\"EPOCH {} ...\".format(i+1))\n",
    "        print(\"Training Accuracy = {:.3f}\".format(train_accuracy))\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "        \n",
    "    saver.save(sess,'./lenet.ckpt')\n",
    "    print('Model saved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dE4+9uZXfv7qZ8ppGjhs9iA9PG8OTbxezurSKY0cO5GsXHcMHjhl+WNuO65paWLp1D29uqqBfeipXn3rw1x0Ot6aWMPe+sJ7ddU18+fxJDBsQn7/CeiKFviS8t7fu5rOPLKamsYUPHDOcyaMGMmV0JORHDurbYfD9Z305n39sMakpxv2fOJnTYqwRh8LOHfNW8PhbW/nvglzu+vDxB31f+KFoaA7x1NJiHvz3Rt4tq2X8sEy+fP4kLj9xdELWYCU2Cn05KJH7iluobwrRHHZGD+44PLsqHHbWbK9mYk7/Q64dP1G4jTv+toIRgzP4zbUFHbZzd2RTeS2femgRW3fVMXvm8Vx96oEfCmxoDnHL3CU8t3IHX/jAUXzlgmMOa206FuGws35nDRNz+ifFPf9yYLGGfo8bI1e6btHmCj736NtU1TeTnpZCRloK6cFr7/vU1s+RsK1vClHX3EJdU4j6phD1zSHqmkI0Be3frU4aO5gvX3AMZ+VnH3TIuTv/Xl/O/z23hhXFVeQO7ce3LpnChceN6PI2W0Jh7lqwmt+/upkZRw/jl7PeR1b/9C6XKS+7P3/7/AxufnwJ3/zbctbtqOaOSye3W3OvamjmMw8V8uamCr5z+RRumJHX5f0dDikpxjEjB3Z3MaSXUU2/l3ttQzmfeqiQUYP7csFxI2lqCdMUCtHYHKYpFI58bgnT2PpvKAzu9EtPJTM9jX7pqfTrk0pmempkWp+0ve8bmkP8/tXNFO+pp2B8FrddcAzvP6prFwcXb9nNj/+xhjc3VTA2qx/XnDaeJ98uYt2OGs44OpvvXD6F/BGxBdfu2ia+8Me3ee3dXXxyRh7fvOTYQ25eCYWdHy5YzW//s4kzjs7mvqvft8895TurG7huziLW76jmJ1eexMypYw5pfyKHi5p3ksC/1pVx48OFjB+WyaOfPnWfx9PjpaklzJ8Kt/HLFyO3AZ5+1DBuu2ASJ48fesD11myv4u7n1vLP1TvJHpDBFz94NFdNzyUjLZWWUJhH34g8+l7bFOLa94/nS+dNOuCdH2u2V/GZhwvZUdnIXR8+no8V5Mb1OJ8o3Ma3/racsVmZ/ObaAo4ePoAtu2q55ndvUVbdyK+vOZmzJ+XEdZ8i8aTQT3ALV+3gC4+9zdHDB/Dop09l6EE0cXRFQ3OIx97cyv0vb6C8pomzJ+Vw2wWTOHHskH2W27Krlp8tXMdT75QwICONz559FDfMmEBm+v4tiRW1Tdz9/Foef2srWZnpfPXCY7iyIHe/e+j/vryU2/78DgMy0njgmpOZNi7rsBxj4eYKPvvoYhpbwnzlgmP4xYsbCIXDzLn+lMO2T5F4UegnsAXLS7n58SUcN3oQD3/y1HYfcT9c6ppaePj1LTzwr3fZXdfM+VNGRG6F65/OvS+uZ+5b20hLNa4/PY/Pnj2RIZmdn4xWFFfy3adXsmjzbo4bPYjvfug4CiYMJRx27nlhPfe+sJ6puUN44JqTGTEo/n/NRCvaXcdnHl7M6tIqRg/uy8OfOpWjhw84rPsUiQeFfoKat6SYLz+xlPeNy+L3N5zSbY+QVzc084dXN/PgKxupbmghPS2FcNi5anouN38wn+FdDGd35+llpfzg2dVsr2pg5tTR1DWFWLhqBx89eSzfv+L4I3Y/fG1jC4+/tZVLTxx10D0eihxpCv0E9MSibXz9r8s4LW8Yv72ugP6H8ERpvFTWNTPn1U1U1Dbx6TPzutxJVlt1TS3c//K7PPDvjZH74i+dzPWnT+j22yNFejqFfoJ55I0t/M+8FZw1KYcHrzn5iNV6u0vxnnqq6puZPKpr99+LJCvdp59AfvvKRr7/7GrOmzycX179voQPfIAxQ/oxppM+1EWk6xT6PdyvXt7Aj/+xlouPH8nPr5rW5f7NRUSiKfR7sN+/uokf/2MtM6eO5icfO6lb+nkRkcSi0O+hdtU08pPn13H2pBx+euXUuPT/LiKiqmMP9YsXN1DfHOJ/LpuiwBeRuFHo90Cby2t59I0tXFmQqweDRCSuFPo90P89v5Y+qSncel5+dxdFRBKMQr+HeWfbHp5dVspnzszr8lOtIiKdUej3IO7OD/++mmH907nx7KO6uzgikoAU+j3Iy2vLeGNjBTefm39Ig3aLiHREod9DhMLOj/6+hgnDMpk1/cBD94mIHCyFfg/x5NtFrN1RzVcvPFZP3YrIYRNTupjZRWa21sw2mNnt7cwfb2YvmNkyM3vZzMZGzbvOzNYHr+viWfhE0dAc4qfPr+Ok3CFccsLI7i6OiCSwTkPfzFKB+4CLgSnALDOb0maxu4GH3f1EYDbww2DdocB3gFOB6cB3zExDELUx59VNbK9q4BsXH6suhEXksIqlpj8d2ODuG929CZgLzGyzzBTgheD9S1HzLwQWunuFu+8GFgIXHXqxE8fu2ibuf/ldzj12OKdN7Nqg4yIiXRVL6I8BtkV9LgqmRXsH+Ejw/sPAQDMbFuO6mNmNZlZoZoVlZWWxlj0h/PKlDdQ2tvD1i4/t7qKISBKIJfTba29oO/LKV4CzzWwJcDZQDLTEuC7u/qC7F7h7QU5OTgxFSgzbKup45PUtfOzkXCaNGNjdxRGRJBBL6BcBuVGfxwIl0Qu4e4m7/5e7TwO+FUyrjGXdRFHd0My6HdV0ZSSyu59fS0oK3Hr+pMNYMhGR98TyBNAiIN/M8ojU4K8Cro5ewMyygQp3DwPfAOYEs54DfhB18faCYH5CcXc+/9jbvLK+nIk5/bli6hiumDqGccMyO1xnRXElTy0t4fPnHMXIwepuQUSOjE5r+u7eAtxEJMBXA0+4+0ozm21mHwoWOwdYa2brgBHAXcG6FcD3iJw4FgGzg2kJ5aW1O3llfTkfnjaGnAEZ/HThOs76v5f4r1+9ysOvb2ZXTeN+6/zo72vIyuzDZ89RdwsicuRoYPRD1BwKc+HP/g0Gz33pLPqkplC8p575S0t4amkxa7ZXk5ZinJmfzRXTxnD+lBEUbt7NtXPe4tuXTeGTZ+R19yGISALQwOhHyMOvb2FjeS1zri+gTzCc4Zgh/fjcOUfxuXOOYnVpFfOWFjN/aQm3zF1KZnoqmemp5A7tx8dPU3cLInJkKfQPQUVtEz//5zrOzM/mA8cMb3eZyaMGMXnUIL5+4bG8tbmCp5YW89KaMv7n0ilkpKUe4RKLSLJT6B+Ce/65jprGFv7nsimdPkmbkmKcNnGYHsASkW6lnr0O0rod1Tz25lY+fup43WMvIr2GQv8guDvfe2YV/dNTdY+9iPQqCv2D8PLaMl5ZX84t501iaP/07i6OiEjMFPpd1BwK871nVzExuz/XnDa+u4sjItIlCv0uevSNLWwsq+Vbl07WYCci0usotbpgd20T9/xzPWfmZ/PBY9u/RVNEpCdT6HfBz19YT3VDM3dc2vktmiIiPZFCP0brd1TzyBtbuPrUcRwzUrdoikjvpNCP0fefXU1meiq3nqdbNEWk91Lox+CltTv517oybjk3n2EDMrq7OCIiB02h34nmUJjvP7OKCcMyufb9E7q7OCIih0Sh34nH3tjCu2W1fOvSKbpFU0R6PaXYAdQ0tvCzf67njKOzOW+ybtEUkd5PoX8Az63YTmV9M7ecl69bNEUkISj0D2De0mLGZvWjYHxW5wuLiPQCCv0O7Kxq4NUNkXFvVcsXkUSh0O/A/HdKCDvMnDqmu4siIhI3Cv0OzFtazAljBnP08AHdXRQRkbhR6Ldjw85qVhRXccU01fJFJLEo9Nsxb0kJKQaXnzSqu4siIhJXCv023J15S4uZcXQ2wwf27e7iiIjEVUyhb2YXmdlaM9tgZre3M3+cmb1kZkvMbJmZXRJMn2Bm9Wa2NHj9Ot4HEG+Lt+ymaHc9H1bTjogkoLTOFjCzVOA+4HygCFhkZvPdfVXUYncAT7j7/WY2BVgATAjmvevuU+Nb7MPnb0uK6dcnlQuPG9ndRRERibtYavrTgQ3uvtHdm4C5wMw2yzgwKHg/GCiJXxGPnKaWMM8uL+X8KSPon9Hp+VBEpNeJJfTHANuiPhcF06LdCXzCzIqI1PK/GDUvL2j2+ZeZndneDszsRjMrNLPCsrKy2EsfZ/9aV8aeumY17YhIwool9Nt7HNXbfJ4F/MHdxwKXAI+YWQpQCoxz92nAl4E/mtmgNuvi7g+6e4G7F+Tk5HTtCOJo3pJihvVP54z87G4rg4jI4RRL6BcBuVGfx7J/882ngCcA3P11oC+Q7e6N7r4rmL4YeBfokUNPVTU0s3D1Di47cRR9UnVTk4gkpljSbRGQb2Z5ZpYOXAXMb7PMVuBcADObTCT0y8wsJ7gQjJlNBPKBjfEqfDz9Y8V2mlrCeiBLRBJap1cr3b3FzG4CngNSgTnuvtLMZgOF7j4fuA34jZndSqTp53p3dzM7C5htZi1ACPisu1cctqM5BPOWFDNhWCZTc4d0d1FERA6bmG5RcfcFRC7QRk/7dtT7VcCMdtZ7EnjyEMt42JVW1vP6xl3c/EH1my8iiU2N18D8pSW4o6YdEUl4Cn1g3tISpuYOIS+7f3cXRUTksEr60F+zvYrVpVVcMXV0dxdFROSwS/rQn7ekhNQU47KTFPoikviSOvTDYWf+0mLOys8me0BGdxdHROSwS+rQf2tzBSWVDbqAKyJJI6lDf96SYjLTUzl/yojuLoqIyBGRtKHf0Bzi2eWlXHTcSDLT1aOmiCSHpA39l9fupLqhRU07IpJUkjb0/7akmOwBGZx+1LDuLoqIyBGTlKFfWdfMS2vK+NBJo0lTj5oikkSSMvEWrCilKRTWYCkiknSSMvQXb9lNzsAMjh+z33guIiIJLSlDv6y6kVGD+6pHTRFJOkkb+jl6AldEklBShn55TSM5AxX6IpJ8ki70Q2FnV22TQl9EklLShf7uuiZCYVfoi0hSSrrQL6tuBFCvmiKSlJI29FXTF5FklLyhr5q+iCSh5Av9GtX0RSR5JV3ol1c3kpmeSv8MdacsIskn6UK/TPfoi0gSiyn0zewiM1trZhvM7PZ25o8zs5fMbImZLTOzS6LmfSNYb62ZXRjPwh+MsupG3bkjIkmr09A3s1TgPuBiYAowy8ymtFnsDuAJd58GXAX8Klh3SvD5OOAi4FfB9rqNumAQkWQWS01/OrDB3Te6exMwF5jZZhkHWrusHAyUBO9nAnPdvdHdNwEbgu11GzXviEgyiyX0xwDboj4XBdOi3Ql8wsyKgAXAF7uwLmZ2o5kVmllhWVlZjEXvuqaWMHvqmhX6IpK0Ygn99vof9jafZwF/cPexwCXAI2aWEuO6uPuD7l7g7gU5OTkxFOng7KrV7ZoiktxiuW+xCMiN+jyW95pvWn2KSJs97v66mfUFsmNc94jRg1kikuxiqekvAvLNLM/M0olcmJ3fZpmtwLkAZjYZ6AuUBctdZWYZZpYH5ANvxavwXbW33x3V9EUkSXVa03f3FjO7CXgOSAXmuPtKM5sNFLr7fOA24DdmdiuR5pvr3d2BlWb2BLAKaAG+4O6hw3UwnVG/OyKS7GJ6LNXdFxC5QBs97dtR71cBMzpY9y7grkMoY9y818NmejeXRESkeyTVE7llNY0M7teHjLRufVRARKTbJFXoa5hEEUl2SRX6ehpXRJJd0oW+7twRkWSWdKGvmr6IJLOkCf3axhZqm0Jq0xeRpJY0oV+uEbNERBT6IiLJJGlCX/3uiIgkYehnD9TTuCKSvJIq9FMMhvVXTV9EklfyhH5NI0P7Z5Ca0l4X/yIiySF5Qr9aXTCIiCRP6Nc0KfRFJOklTeiX62lcEZHkCH13D/rd0Z07IpLckiL0q+pbaAqFVdMXkaSXFKFfVtMA6GlcEZGkCP2dGhtXRARIktAvr2kCYLhCX0SSXFKE/nv97vTt5pKIiHSvpAn99NQUBvVL6+6iiIh0q6QJ/ewB6ZipCwYRSW7JEfo16oJBRARiDH0zu8jM1prZBjO7vZ35PzOzpcFrnZntiZoXipo3P56Fj5X63RERiei0kdvMUoH7gPOBImCRmc1391Wty7j7rVHLfxGYFrWJenefGr8id115TSNTcwd3ZxFERHqEWGr604EN7r7R3ZuAucDMAyw/C3g8HoWLh1DY2VWjfndERCC20B8DbIsRx7LJAAAKLklEQVT6XBRM24+ZjQfygBejJvc1s0Ize8PMruhgvRuDZQrLyspiLHpsKmqbCDtkq3lHRCSm0G/vlhfvYNmrgL+4eyhq2jh3LwCuBu4xs6P225j7g+5e4O4FOTk5MRQpdhobV0TkPbGEfhGQG/V5LFDSwbJX0aZpx91Lgn83Ai+zb3v/YVdWoy4YRERaxRL6i4B8M8szs3Qiwb7fXThmdgyQBbweNS3LzDKC99nADGBV23UPpzL1uyMislend++4e4uZ3QQ8B6QCc9x9pZnNBgrdvfUEMAuY6+7RTT+TgQfMLEzkBPOj6Lt+joTyoKafreYdEZHOQx/A3RcAC9pM+3abz3e2s95rwAmHUL5DVlbdSP/0VPpnqAsGEZGEfyI3MmKWavkiIpAkoa87d0REIhI/9NXvjojIXokf+up3R0Rkr4QO/caWEJX1zWreEREJJHTo7wqGSVRNX0QkIqFDXw9miYjsKylCXw9miYhEJHboq98dEZF9JHboBzX9YQPSu7kkIiI9Q0KHfnlNI0My+5CRltrdRRER6RESOvT1NK6IyL4SP/TVni8isldih35No+7cERGJktihr5q+iMg+Ejb0axtbqGsKKfRFRKIkbOi3jpilC7kiIu9J2NBXFwwiIvtT6IuIJJHEDX0NiC4isp/EDf3qRlIMhvZXFwwiIq0SOvSHDcggNcW6uygiIj1GwoZ+eY26YBARaSthQ18PZomI7C+m0Dezi8xsrZltMLPb25n/MzNbGrzWmdmeqHnXmdn64HVdPAt/IAp9EZH9pXW2gJmlAvcB5wNFwCIzm+/uq1qXcfdbo5b/IjAteD8U+A5QADiwOFh3d1yPog13V787IiLtiKWmPx3Y4O4b3b0JmAvMPMDys4DHg/cXAgvdvSII+oXARYdS4FhU1jfTHHLV9EVE2ogl9McA26I+FwXT9mNm44E84MWurGtmN5pZoZkVlpWVxVLuA9KDWSIi7Ysl9Nu759E7WPYq4C/uHurKuu7+oLsXuHtBTk5ODEU6sDL1uyMi0q5YQr8IyI36PBYo6WDZq3ivaaer68aNavoiIu2LJfQXAflmlmdm6USCfX7bhczsGCALeD1q8nPABWaWZWZZwAXBtMNKoS8i0r5O795x9xYzu4lIWKcCc9x9pZnNBgrdvfUEMAuY6+4etW6FmX2PyIkDYLa7V8T3EPZXVtNIemoKg/p2engiIkklplR09wXAgjbTvt3m850drDsHmHOQ5Tsorffom6kLBhGRaAn5RG5ZdSPZatoREdlPQoZ+eU2T7twREWlHQoa+umAQEWlfwoV+KOxU1Cr0RUTak3Chv6u2kbBDzgANniIi0lbChb7u0RcR6ZhCX0QkiSRc6JfXNAGQM6BvN5dERKTnSbjQb63pZw9Um76ISFsJGfoDMtLITFcXDCIibSVe6Nc0kq07d0RE2pV4oV/doIu4IiIdSMDQ14NZIiIdSbjQV787IiIdS6jQb2wJUVnfrJq+iEgHEir0996jr9AXEWlXQoX+3nv01bwjItKuhAx91fRFRNqn0BcRSSIJGfrD+iv0RUTak1ChX17TSFZmH9LTEuqwRETiJqHSUQ9miYgcWGKFfk2j7twRETmAxAp91fRFRA4optA3s4vMbK2ZbTCz2ztY5kozW2VmK83sj1HTQ2a2NHjNj1fB23L3SOirpi8i0qFOO503s1TgPuB8oAhYZGbz3X1V1DL5wDeAGe6+28yGR22i3t2nxrnc+6ltClHfHFJNX0TkAGKp6U8HNrj7RndvAuYCM9ss8xngPnffDeDuO+NbzM41t4S5/KTRTB416EjvWkSk14gl9McA26I+FwXTok0CJpnZq2b2hpldFDWvr5kVBtOvaG8HZnZjsExhWVlZlw6gVVb/dH4xaxpnTco5qPVFRJJBLGMKWjvTvJ3t5APnAGOBV8zseHffA4xz9xIzmwi8aGbL3f3dfTbm/iDwIEBBQUHbbYuISJzEUtMvAnKjPo8FStpZ5il3b3b3TcBaIicB3L0k+Hcj8DIw7RDLLCIiBymW0F8E5JtZnpmlA1cBbe/CmQd8AMDMsok092w0sywzy4iaPgNYhYiIdItOm3fcvcXMbgKeA1KBOe6+0sxmA4XuPj+Yd4GZrQJCwFfdfZeZnQ48YGZhIieYH0Xf9SMiIkeWufesJvSCggIvLCzs7mKIiPQqZrbY3Qs6Wy6hnsgVEZEDU+iLiCQRhb6ISBLpcW36ZlYGbDmETWQD5XEqTk+QaMcDiXdMiXY8kHjHlGjHA/sf03h37/Tp1B4X+ofKzApjuZjRWyTa8UDiHVOiHQ8k3jEl2vHAwR+TmndERJKIQl9EJIkkYug/2N0FiLNEOx5IvGNKtOOBxDumRDseOMhjSrg2fRER6Vgi1vRFRKQDCn0RkSSSMKEfyzi+vY2ZbTaz5cH4wr2uQyIzm2NmO81sRdS0oWa20MzWB/9mdWcZu6qDY7rTzIqjxoK+pDvL2BVmlmtmL5nZ6mB861uC6b3ydzrA8fTm36ivmb1lZu8Ex/TdYHqemb0Z/EZ/CnpB7nx7idCmH4zju46ocXyBWb29R08z2wwUuHuvfKjEzM4CaoCH3f34YNqPgQp3/1Fwcs5y9693Zzm7ooNjuhOocfe7u7NsB8PMRgGj3P1tMxsILAauAK6nF/5OBzieK+m9v5EB/d29xsz6AP8BbgG+DPzV3eea2a+Bd9z9/s62lyg1/VjG8ZUjzN3/DVS0mTwTeCh4/xCR/5C9RgfH1Gu5e6m7vx28rwZWExkOtVf+Tgc4nl7LI2qCj32ClwMfBP4STI/5N0qU0I9lHN/eyIHnzWyxmd3Y3YWJkxHuXgqR/6DA8G4uT7zcZGbLguafXtEU0paZTSAyst2bJMDv1OZ4oBf/RmaWamZLgZ3AQuBdYI+7twSLxJx5iRL6sYzj2xvNcPf3ARcDXwiaFqTnuR84CpgKlAI/6d7idJ2ZDQCeBL7k7lXdXZ5D1c7x9OrfyN1D7j6VyHC104HJ7S0Wy7YSJfRjGce314kaX3gn8DciP3ZvtyNod21tf93ZzeU5ZO6+I/hPGQZ+Qy/7nYJ24ieBx9z9r8HkXvs7tXc8vf03auXue4iMNX4aMMTMWkc/jDnzEiX0YxnHt1cxs/7BhSjMrD9wAbDiwGv1CvOB64L31wFPdWNZ4qI1HAMfphf9TsFFwt8Bq939p1GzeuXv1NHx9PLfKMfMhgTv+wHnEblW8RLw0WCxmH+jhLh7ByC4Bese3hvH965uLtIhMbOJRGr3EBnL+I+97ZjM7HHgHCJdwO4AvgPMA54AxgFbgY+5e6+5MNrBMZ1DpNnAgc3A/2ttD+/pzOwM4BVgORAOJn+TSDt4r/udDnA8s+i9v9GJRC7UphKpqD/h7rODjJgLDAWWAJ9w98ZOt5cooS8iIp1LlOYdERGJgUJfRCSJKPRFRJKIQl9EJIko9EVEkohCX0QkiSj0RUSSyP8Hjp0Iv3eJ6mMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(validation_accuracy_figure)\n",
    "plt.title(\"Test Accuracy\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(validation_accuracy_figure)\n",
    "plt.title(\"Validation Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import cv2\n",
    "\n",
    "\n",
    "# load test images\n",
    "test_img_paths = [img_path for img_path in glob.glob(\"my_images/*\")]\n",
    "num_test_img = len(test_img_paths)\n",
    "test_img=np.uint8(np.zeros((num_test_img,32,32,3)))\n",
    "for i, imp in enumerate(test_img_paths):\n",
    "    img = cv2.imread(imp)\n",
    "    img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "    img=cv2.resize(img,(32,32))\n",
    "    test_img[i]=img\n",
    "\n",
    "# plot images\n",
    "figsize = (16, 16)\n",
    "plt.figure(figsize=figsize)\n",
    "for i in range(num_test_img):\n",
    "    plt.subplot(2, 3, i+1)\n",
    "    plt.imshow(test_img[i]) \n",
    "    plt.title(i+1)\n",
    "    plt.axis('off')\n",
    "plt.show\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Run the predictions here and use the model to output the prediction for each image.\n",
    "### Make sure to pre-process the images with the same pre-processing pipeline used earlier.\n",
    "### Feel free to use as many code cells as needed.\n",
    "test_data=np.uint8(np.zeros((6,32,32,3)))\n",
    "for i in range(num_test_img):\n",
    "    test_data[i] = test_img[i]\n",
    "test_data=np.sum(test_data/3, axis=3, keepdims=True)\n",
    "test_data=(test_data - 128)/128\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess,'./lenet.ckpt')\n",
    "    signs_classes=sess.run(tf.arg_max(logits,1),feed_dict={x:test_data,keep_prob:1.0})\n",
    "    \n",
    "figsize=(16,16)\n",
    "plt.figure(figsize=figsize)\n",
    "for i in range(num_test_img):\n",
    "    plt.subplot(2,3,i+1)\n",
    "    plt.imshow(test_img[i])\n",
    "    plt.title(signs_names[signs_classes[i]])\n",
    "    plt.axis('off')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "correct_class=[1,22,12,14,17,13]\n",
    "correct=0\n",
    "for (corr,pred) in zip(correct_class,signs_classes):\n",
    "    if corr==pred:\n",
    "        correct+=1\n",
    "        \n",
    "print(\"Accuracy: \",round(correct/6,3))"
   ]
  },
  {
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yQMPrFsWPeu+h2OS8V+InFNl1NBMAZ2JFzCQxa4klUKbEBp8aXjn41PBK4srWVTpw5V9iZ4SfiJ02fBe5XV3nM6YRE10HcMWuiSW7aJ4A24BurqOkO1X2baR7+dOxcXum15499EPy7Z49kywUyK0qLd7nOkiiWRFLdtG8o4EK1zHMp6miu8ipeCU+snpa7bn9lurQI11nMmnv6KrS4rddh0g0O52Y/I5zHcB8Vt11tAvD87kwPJ8DGn7/DT1y9cO153R7OX5iUYyw/W6ZRDsMsCJmkk5f1wFM0zIlNnC0VAwcnVWBKjXvav/lf42dEXosNmH4TjrZqWCTCGk5y4MVseRn4wAGjAh5w+TDsT8JzeDHGTP2V5P3xjOxsbumx84Z+r4W9Hedz6Ssw1wHcMGKWPKzm3ADTISs3tSceF3GP7ku45/s0uyKf8dHbnyw9ty+b+qwo1znMynFWmImKVlLLIV0ln2F54dfLzw//Dq1GvpgiQ579+HayV1fip9YVEtGput8JtCsJWaSkrXEUlSGxAecLG8PODnrbVSpWaP9VvwldgaPxSYM30HnPNf5TOD0cB3ABetin+yieWtI009Y6UqVA5vpVv5s7NSdD8UmH7Feew9wnckEwodVpcVp93/FWmLJL9t1AJNYImTms/2EazJe4JqMF9it2W+/Fj/+o2m15/ZerEcVus5nklZano62Ipb89roOYNzqJPuOOi+88Kjzwgup1dCG6Rkjli55Z1J4T9djwprZ1X6HDQCK7nGdwQX7BUh+aTeMjGmcgv66V7dVj3arHn/Lor8uOnmRTtzVqc+66vwR6zflF8V3dh7QNx7KPAKRkOusxolNrgO4YEUs+VlLzLA6M6Pqyn59tm8Ph08D+ON5oZEnvROr6bz748Gd1704OLLuRQAOhHNrtvQ65t2N+SN3but+RNcDmV2HIWKDFqeHA64DuGBFLPlZEUtjMYjdlt9zzpNdOp+MSKRu+a5cyZt/tMw8tVIn1F8/M7Ynr8/GN07ss/ENABSJb+865J3qghEfbe51bMbu3N6DNBQelNA3YRJlv+sALlgRS35WxNLUyqzMd6/p12ffrlBoQmOv/+nc0MgxlbFtAt0Ptg9BQ3k7qo7M21F15NA1TwOwLytvY3X+ce9tyj9+7/ZuQ3rWhnOPQiSrg96GSZwdrgO4YEUs+e12HcAkVi3U3lrQa+7znTuNOVRx2ZMj3WYPl5mnrdBGi9zBZO+v6T3ww9m9B344G4C4ZOzb2n1Y+caCkVu29jgqZ29Oz8ORUEEb34ZJvPdcB3DBiljyW+s6gEmcpdlZb3+tb2/2HqT11dADk0MnjlsR2xKCnq09Zkhrs3ttrSjqtfWTGX925fZZV11w/PpN+cdZh5HgsCJmklLaTa2QjvbD/lt658//d6fcsYg0+/dyb5Z0mXmczDx9WctaY03pvOfjwZ3XvTQ4su4lAGrDOds39zzm3eqCkTu2dR/aZX9m1yOtw0jSWeM6gAtWxJLfO64DmI61MCd7xTf7FGTva2brq6FpZ4VGTSiPVYeUDjsFmBHb261P9ZIT+lQvAT7VYeTjzb2Gh7wOIxmDO+r4plmsJWaSkrXEUtRekT039c5fOD83Zxwi4dbuZ1+WdH7leFl81lvt2xo7lMY7jHSr3tTruNXVBSP2be82pFdtOHcYIjbiTOKkZRGzsROTXTQvhNe5w/4YpJDZuTnLvtOnoNuBet3m2yLrgO555Nex7SFNngGjvQ4jQ1dVF4zcvKXH0dl7c3oeYR1GOlSXG6dO2uU6RKJZEQuCaN5yYLjrGKbtdovs+kbfgjeWZGePR0Tac99XvRybdd5iPa0999ne/tNhpFdRfGeXgX3iocyh1mGkXXx049RJ/VyHcMFOJwaDFbEU8HKn3CU/6J1fUCvSIYXm0dNDp5zzRmxDWEnaP2ZNdBjpuj+z61BEujmOGUTzW7ORiChwt6re7H9/C9BFVaPtmK3hMW9V1V+01/6aLGIiEgPKAQFiwLdUdZ6I9Af+V1W/0B5BROQqYJSqfqs99tfRROSfwFdUdVsCDjcLuDQBxzEdYIfI9uv69V66Ijt7fEcepzZDsstOklUXLNSkLWINNdZhZEfXwas2FozcsLnX8LB1GGm22a3cbh9wsYj8UlUTNfbircBniph4ZyZEVeMt2VlzWmJ7VHWEf5BzgF8CE1T1Q6BdClgiiUiGqta2dT+qel575Gmm1xJ4LNOOnu3SadGP83sNjIt0aAGr838TQ6OLF8U+CCuBnFdK0FC3HWuHdduxdliDDiNrqguO37u9a6RnbUbukdZh5DNmtXK7WuA+4HvAj+q/ICJDgGlAAVANXK2q6xqs0xm4FyjCqydRVX3Gb5RcAHQCjgCeUtUfiEgpkCsibwEr/GM+D/wbGAM8LSLdVfV7/v6vAwpV9f8d7A209Fx0N2Crv/OIiCz3n4dF5C4RKReRZSLybRE5Q0SeqvdmzxKRJ/3nk0VkiYgsFZFXGh5ERApE5O8issh/jPWXTxCRt/zHm9LIfSoicqWfYamIzPCXPSwid4vIv4E7RKSniDztr/e6iBx3sP2LSD8RmeUvWy7+HyMRqRKRfP/nUCEi94vIChF5SURy/XVO8o8xX0TurPt5tVi0ZiXwcau2NU7UhELbLh7Qd+6PCvJPioskrGVUG5asZ0dLSt0vlL1/e8GADXNOGbHs9xNOm/v9oomzvsvxS+9d3v/DOTNz9mxagMarXWd0bBvwVhu2/z1wuYg0nE38d8Ajqnoc8GfgfxvZ9kfAq6p6EnA6cKdf2ABG4J1BKgIuFZFBqlqC3zBS1cv99Y7yjzMSuAu4QETq5ka7GnjoUOGb0xKrq5o5QD9gUiPrXI83+/BIVa0VkZ54xe73IlKgqtV1YUSkALgfOE1V3/PXbei3wD2qOkdEBgMvAoXALcCNqjpXRLrQYFxBERmO90Mdq6qbGuz7SOBMVY2JyL3Am6p6kYhMAh7B+4E3tv/rgRdV9efidYPu1EjeYcCXVfU6EXkMuAR4FO+Hf71/+rX04D/iZnkRuLKN+zAJ8FjXLq//vFePw+P+h6+EH398aPTnFsTWZ8RJyYF+vRFGKo/ttbXyP8t25/ZeX51//Prq/ONqd3YZ2DfNOoy8dOPUSbHWbqyq20XkEeAmoP6cZGOAi/3nM4BfNbL52XhF5xb/+xyg7vTvK6paAyAiK4EhwPpG9rFWVV/3s+wSkVeB80WkAshU1fJD5W/p6cQxwCMicmyDdc4EptadplPVLf76M4ArROQhvB/IlcC5wCxVfa/+uo3s75h6nbe6+a2uucDdIvJn4ElVfb/BdpOAJ+rO7TbY9+OqWvcPPQ6v0KCqr4pIL/9TyGf2LyKLgGn+J4OnVbWxTzzv1Vv+BhARke5AV1Wd5y//C3B+I9s2VxlWxJLa5lBo05X9+6xal5k5xmWOWFgynzpV1n5xjqZkEWtMpz0bBw1Z//KgIetfBqA2nL1jc8/hq6oLRuzYlje0y/6sbsNSuMPIC+2wj98ASzh0q6exruwCXKKqn7qfVURO4dNzIcY4eL1peFvAA3jXzSqbyAOH2GmjVHW+iOTDZ0YGEBp/gw8Bz+G1aB73W2kHW7e+EDBG9TMzlZaKSBlwHvC6iJypqpX1Xj/Uvnc1WK8hVdXG9j9LvN5kxcAMEblTVR9psG3Df6zcgxyjLV7EO39tPUqT0MPdus67u2f3o9T7oOfc38eGRn9+XmxtRpwhrrO4kBHb17XxDiMjPtrcc3hod6c+qdJhRGmHIqaqW/yzSNfiXQcDmAdchtcKuxyY08imLwLfFpFvq6qKyEhVfbOJwx0QkUxVbXT+M1VdICKDgBOA45rK3qLmtogcDYSBzQ1eegm4Qfwx3+pO4/mdPz4E/ht42F93PjBBRA6rv24j+/tPL0URqWsJHqGq5ap6B7AYOLrBdq8AXxKRXofYN3gXQS/315kIbPKb1J/Zv3gXNzeq6v3Ag3g/2Cap6lZgh4iM9hdd1pztDipaU0P7fOIy7ejjcPjjswf2X/DrXj1OVf//XTKIhyTjsfGhhmcq0lZdh5Gha54Zf8riX4w9fdZ3Bo+d91/VR7391wU9t6ycmXFgdzmqQZxF/eUbp07a0E77+jWQX+/7m4CrRWQZ8FXgO41sczuQCSzzr/nf3ozj3Oev/+dDrPMYMNf/O3pILbkmBl7rYop/Xan+Og/gXXNaJiIH8K55/c5/7c9AgaquBFDVahG5HnhSvHPWG4GzGhzzJrzracv8jLOAG4DvisjpeK2dlXi9Wv5DVVeIyM+BmeLdGvAmcFUj7ymKd31uGd5oGFP85Y3t/zLg+/772knLTuldC9wvIrvwehjWtGDbxtxP205Jmnb0x+7d5vyhe14R3qmTpPPsaBn9xTmsyYxxuOssycjvMFIwYIPXwIhLeP+27sOWbywYsXlLj8Jsf0qa3o5jNuX+tmysql3qPf+Yetf8VbWKxvtA1N9+D/D1RpY/zCcNF1T1/HrPfwj8sN7qDS9PgXfJ554m4gMJGLFDRH6H14niwQ49UBISkS6qutN/XgL0U9XGPs00TzQvjDc1SyC7T6eK9zPCH3y1X98NmzLCo1xnacr5C+Lzrnw1fqrrHEH16Q4jA/rGQ1nJ1GFkIzDwxqmTGj0tF0R+X4KFwFJV/WKztunIIiYib+BdizpLg9lUbxMRuRT4L7zW5FrgKr+nZutF824Dftz2dKalFPSeHt1nP5TXdWRQpiER1fiMu2JrsmoZ6jpLKqgNZ+/Y0vOYVRsLRtZ1GBnKZ7umJ8qvbpw66YdNr5babOzEoInmDcGbNyhZPg2mhaqMjHVX9u+zeWs4PNJ1lpaavDj++jUvx0c3vaZpKa/DyKDV1fkjPtrU61jZ3anP4AR1GFHgyBunTno3AcdKalbEgiia9zww2XWMdBCH+C979Zjzf127jEKksXsEk5+qzrgrtiq7liNdR0kH+zK7Vm/KP+696vzj92zvdljP2ozcYYjktPNh/n3j1EmHvF6VLqy7djD9AStiHe6dzMz3rurXZ+eOcCipR4ZvkohMPzO09foXWjQknWml7AM7CgZsmFswYMNcwO8wkjd0+cbedVPS9GqPDiP3tT1parCWWFBF8xYCJ7mOkYpiEIvm95z9dJfOozvgE7Qzj9xVW5FzgELXOQzszi14vzr/+LXV+cfH/A4jR7RgYtRVQGFbRulIJVbEgiqadxbe/XSmHa3Iylp1Tb/eB3aHQse4ztLeJi6LL/xmWfxk1znMZ/kdRt7dmD9i+7buw5rqMHLFjVMnHeoeq7RiRSzIonmv4g26adroABz4r4Je817s3GkMIlmu83SUh39du6LTfpubLtk16DAS2t2pz0ANZQzBG/n9uBunTrJzwz4rYkEWzTsFeN11jKBbkp1V8fW+vcN7Q6GU7/gwdkX8je88Gz/RdQ7Tcvszu276qM+oKef86w//dJ0lmVgRC7po3jN48/aYFtoP+77Xp+D1Wbk5Y/GHTEsHD91dW955H0Wuc5gWm11YWRHsTkYdwO41Cr4fAXZqoYVez8lePmbIoPdndcqdkE4FDOBP54XaPCmsSbg4cLPrEMnIiljQRWuW402jYJphr8ier/XtPfO6vr2P2R+SI1znceH1o0Mjd+Sw1HUO0yJ/LKysWOQ6RDKyIpYa/huv2605hNc65b41ZsjAjQtycyYk0fh3Tvzh/JBdRwiO9/GGrzONSOtf5JQRrdkDXIOdVmzULpGdV/TrM+vbvfOPr/Wm1kl7bwwLjdie26Yp7U3ifLOwsmKH6xDJyopYqojWzAHudR0j2bzQudMbY4cM3LY0J/s0GswflO7u/VzIfv+T32OFlRXPuQ6RzOw/cWq5FVjtOkQy2B6Smi/17zvn+73zT4yJDHSdJxktPSJ03NbOvOE6hzmoj4Bvuw6R7KyIpZJozW6804ppfb3jqS6dF44fPHBPRXbWONdZkt29F4RSZlitFBMHLi+srNjoOkiysyKWaqI1s2jeFOEpZ2sotOXCAf3m/aSg18lxkb6u8wTB8kho+OauWK+35HN7YWXFq65DBIEVsdQUBZ5xHSKR/tK1y/wJgwfE1mRl2izGLfTbC8Ndml7LJNC/gdtchwgKG7EjVUWFzi2vAAAO2ElEQVTzuuINSZVyA9nWVx0OVU/p12f1+sxMm/SxDX7/+9qFBduxwYHd+xgYWVhZscF1kKCwlliqitbsAC4CtrmO0lEezOs274xBA8JWwNruNxeF8zTNr6UmgT3ABVbAWsaKWCqL1qwCvkKK3T+2IRzecOag/gt/07P7qSrS03WeVLBqgBy1sTsLXOdIYwpcUVhZsdB1kKCxIpbqojXP43W9Twn3ds+bc/ag/p0+zsiwU1/t7J6Lwr00xT7wBMgPCysrnnQdIoisiKWDaM0dwG9dx2iL9RkZ708cNOCN+3rkjTvEZIGmDdb0k2EbelprzIH7Cisr7nQdIqisiKWLaM13gT+5jtFSCnpnz+6zzhvYr/vmjLDNg9XB7v58uI+1xhLqceBG1yGCLK2moDB8A8gBprgO0hxrMjPWXtmvz7aacNjmUEqQdb3l8PfzmTdoE3arQsd7AvhKYWWFTY3TBtYSSyfRGgWuBf7mOsqhxCF+W68eMy8c0K93TTh8vOs86eaei8L9FWKuc6S4J4AvWwFrOyti6SZaEwOuAJ52HaUxlVmZq8cOGbjy8W5dJyCS6zpPOnq/QCJre/O66xwpzApYO7Iilo6iNbXApUDSjI5dC7UlBb1mfrF/34E7Q6FjXedJd/dcFB6kYH9k29/fsQLWrqyIpatozX7g88B9rqMsy856+9QhA1eVdek8AZFs13kMbOglg9f0tdZYO3sSuMwKWPuyYacMRPNuBX4GJHS+rf2w/4e98+f/q1PuqYhkJvLYpml9tur7/zs11kfA/m3a7mHgOitg7c+KmPFE864AppGgP1iLcrJXfqNPQea+UGhYIo5nWudn02tnHfkh1ju09WqB7xVWVvzOdZBUZUXMfCKaNwnvlEeH3Uy8T9j7nd4FC+bm5oxDJNxRxzHtI79GN/z+D7GeAnaat+WqgS8WVlbMdB0kldk1MfOJaM2rwDhgfUfsfm5uTvmYIYM2zO2UO8EKWDBsypN+lYNsFI9WWAKMsgLW8awlZj4rmtcbeBQ4qz12t0dk9zf7FCxenJM9DhH74BQwPbfrx3/8fSxPvBvlTdMeBa4vrKzY4zpIOrA/KOazojUbgcnAT2jjTa+vdMp989QhAzctzs05zQpYMG3pJn1WDBFrjTUtBtxcWFnxVStgiWMtMXNo0byJwF+Afi3ZbIfI9uv79l66PDtrHCIJ7fVo2l/3nVr9p3tjnQU6uc6SpDYDlxZWVrziOki6sU/G5tCiNa8BI4CXm7tJWedOi8cPGbhzeU72eCtgqWFbFylYepgscp0jSS3Fu/5lBcwBa4mZ5onmhYAfAT8FGu2UURMKbbumb+8V72RnjU1oNpMQ3Xbp5vv/N5Yt0MV1liQRx5vi6Ed2+tAdK2KmZaJ5Y/DuJzu6/uInunRecHt+z0hcpI+bYCYRfvB47LVR7+pE1zmSwNvANYWVFfNcB0l3VsRMy0XzsoEo8P0todC2Kf36vFOVlTnGcSqTAF1269YHfxsLC3RzncWROHA38OPCyoq9rsMYK2KmDXb8T/cTJg4Z+Ov9IhNdZzGJc/OTsZmnvK0TXOdwYCVwbWFlhY0pmUSsY4dpta4/3bZkv8hZwPeB3a7zmMSYem5ohEKN6xwJtBsoAUZYAUs+1hIz7aJoetEQvIvcF7rOYjred56OzRxbkRatsWeBmworK9a6DmIaZy0x0y7Kp5SvLZ9SfhEwEW/IHZPC7pscGqmw1XWODrQOuLCwsuLC1hQwEdlZ7/l5IrJKRAa3a8JDH/8BETmmiXUeFpEvNLI8IiJf6bh07cuKmGlX5VPKZwKjgCnA+47jmA6yJ0e6zTpWlrnO0QE2ArcARxdWVjzb1p2JyBnAvcBkVV3X1v0185hhVf2aqq5s5S4igBUxk77Kp5Rr+ZTyR4AjgR8DO5vYxATQg+eETox7I1Wkgk3AD4HDCysrft0e932JyHjgfqBYVVf7ywpE5O8issh/jPWXdxaRaf6yN0XkQn/5VSLyjIi8ICJvi8hPD3KsnSJym4gsAMaIyGsiMsp/7VoRecdfdr+I1J8W5jQRmScia+q1ykqB8SLyloh8r60/h45mRcx0mPIp5XvKp5T/DBgG/B6wLskpZG+WdHntOFnuOkcbbQFuBQ4rrKz4VWFlxa522m828AxwkapW1lv+W+AeVT0JuAR4wF/+I+BVf/npwJ0i0tl/7WTgcryRc75YV5wa6AwsV9VTVHVO3UIR6Y/3QXI03oDeRzfYrh/ezBXn4xUv8DqxzFbVEap6T8vfemJZETMdrnxK+UflU8q/BRyOd4+N9WRMEdPODp0UF6pd52iFbXgDXEcKKyt+WVhZ0d5nCw4A84BrGyw/E/idiLyF12mkm4h0Bc4GSvzlr+HNGFB3De1lVd2sqnvw5vsb18jxYsDfG1l+MjBTVbeo6gHg8QavP62qcf/UYyAHKrAiZhKmfEr5hvIp5TcDQ4BfAtsdRzJttD9TOr08Ulp77cWFGuB/8IrX7YWVFTs66Dhx4EvASSJya73lIWCM38oZoaoDVHUHIMAl9ZYPVtUKf5uGXcgb61K+V1Ubm3GiqbFL97Vg3aRkRcwkXPmU8k3lU8pvxStmP8U7pWMC6pEzQqfEhY9d52jCDuBneKcNo4WVFR1+n5uq7sY7TXe5iNS1yF4CvlW3joiM8J++CHxb/AGzRWRkvV2dJSI9RSQXuAiY24IYC4EJItJDRDLwTmE2ZQfQtQXHcMqKmHGmfEr5tvIp5bfhnTb5OpCKvd1S3oEMyXn+RHnbdY6DqARuxmt5/biwsiKhtwWo6ha8ufn+2++scRMwSkSWichK4AZ/1duBTGCZiCz3v68zB5gBvAX8XVUXt+D4HwC/ABYA/8IbdaSpAr4MqBWRpUHo2GE3O5ukUjS9aDxwI3Ax3i+1CYCMWt03467YlrC2bN65DrIX79rP/YWVFbNdh2kLEbkKGKWq32pq3UPso4uq7vRbYk8B01T1qfbK6Jq1xExSKZ9SPrt8SvlleKcao8CHbhOZ5qjNkOx/nCzvOo5RjtfS6V9YWXFl0AtYO4r6HUaWA+8BTzvO066sJWaSWtH0ogzgc8BXgWIgy20iczAZMd0/467YxnCcgQk87C7gb3itLhvXMA1ZETOBUTS9qAfwReAKvG7GgexNlcoumxmbffE8HZ+AQy3Bu5H4L4WVFdbLNY1ZETOB5A84fDleQSt0HMf4QnGtffTO2IcZcTpinMCVwHPAY4WVFTY+pwGsiJkUUDS9qAi4wH+chLXQnLpkTnzupbPjY9thVweAWXiF67nCyoo17bBPk2KsiJmUUjS9qC/evTkX4I2OkOs2UfoJxTU2487Y+sw4kVZsvhn4J/AP4MVE3M9lgs2KmElZRdOLcvEK2fnAGcARbhOljwvnx+dd/lr81GauXoHf2gLmF1ZWNDbyhDGNsiJm0kbR9KKBePOd1T2sqHUQUY0/emesKjPG4Y28vBFvXMGZeKcJVyc2nUklVsRM2mpQ1MbiTR1j9062k+KF8flTXomPBlbgFa25wLzCygrX95OZFGJFzBhf0fSiLsAJwIn+YxReYbOOIs33AbAIWBSK68L/uyO2uLCyYpvrUCZ1WREz5hCKphd1BUbiFbXj8LrzFwLdXOZKApvxuryvxGtprQRWlE8p/8hpKpN2rIgZ0wpF04sG4BWzYQ0eQ/DmgkoFNcBaYJ3/tRK/YJVPKU/2UetNmrAiZkw7K5pe1BMY0MhjoP+1AOgOdHIQT/Hmcdvc4PE+ny5Y68qnlNtIGCbpWREzxhF/XMjuQJ7/6F7va2e8UfwzGjzqLwvhTWq4t8HX+s/3AFv5pFhtLZ9SXpuQN2hMAlgRM8YYE1jWndgYY0xgWREzjRKRXiLylv/4SEQ+qPd9s6ZDEZGJIrJCRN4UkSwRudv/vlREbhSRy9uY8SEROaot+2hknxkiYl3CjQkIO51omiQiUWCnqt7VYLng/R+KH2S7B4CZqjrDX7cG6KWqBzo6c2v5s99uUtXurrMYY5pmLTHTIiIyVESWi8hUvDmd+onIfSKy2G9l/cRf7wbgYuA2EXkEKMPrrLBIRL4gIj8Tke/66x4pIq+KyFIRWSIikQbH7Coiz/uvLxeRL/jL54jICP/510XkHRF5TUQeEJHf+MsfFZHfisg8EVkjIp/3l3fzj7lERJaJyPmNvNcB/jHe8o/b3LEAjTEJkuE6gAmkY4CrVfUGABEpUdUtfivm3yLyhKpOFZFxwBOq+nS9Fk5d0RlRb39/BaKq+pyI5PDZD1fnAVWqeq6/bV79F0VkEFCCN9rGLuA1YGG9VXrjDStVBDwGPIXXa+9CVd0hIr3xhkT6R4PjXgE8p6p3iEgYGxHfmKRjLTHTGqtVdVG9778sIkvwWmaFeEWuWUSkB5Cvqs8BqOpeVd3dYLVlwGT/WtpYVW04PccpwKuqulVV9wNPNHj9afUsw7tPC7yhpO4QkWXAS8AgEclvsN0i4Gsi8lPgWFXd2dz3ZYxJDCtipjV21T0RkWHAd4BJqnoc8AItH7HikBdmVbUCbxzDFcCdInJrg1WaGttwXyPrXol3T9YJfutwEw1yq+qreIMDbwD+3NaOKMaY9mdFzLRVN2AHsF1E+gHntGRjVd0KbBKRzwGISI6IfGokCxEZgNexZAZwN95pw/oWAKeLSHcRycS7FteUPGCjqtaKyFl80kKrf9whwEeqeh/wMN4YisaYJGLXxExbLcEb/HU5sAbv2lJLXQ78SUR+DuwHLsEb+qjO8UCpiMT912+ov7GqrhORO/Gug32A12JrakbgGcBzIrLYfw+rGlnnDOD/icgBYCfeNTJjTBKxLvYmJYhIF1Xd6bfEngH+WHedzRiTuux0okkVt4vIm3idQN7msz0NjTEpyFpixhhjAstaYsYYYwLLipgxxpjAsiJmjDEmsKyIGWOMCSwrYsYYYwLLipgxxpjAsiJmjDEmsKyIGWOMCSwrYsYYYwLLipgxxpjAsiJmjDEmsKyIGWOMCSwrYsYYYwLLipgxxpjAsiJmjDEmsKyIGWOMCSwrYsYYYwLLipgxxpjAsiJmjDEmsP4/tXWAoDLJ4vcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/EBThB4KylMa2/QagPzDyA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8BLm+ozm62r1kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "### Feel free to use as many code cells as needed.\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, './lenet.ckpt')\n",
    "    top_probs = sess.run(tf.nn.top_k(logits  , k=5)  , feed_dict={x: test_data, keep_prob: 1.0})\n",
    "\n",
    "prob_names = []\n",
    "for prob in top_probs[1]:\n",
    "    names = []\n",
    "    for id in prob:\n",
    "        names.append(signs_names[id])\n",
    "    prob_names.append(names)\n",
    "    \n",
    "for i in range(num_test_img):\n",
    "    plt.imshow(test_img[i]) \n",
    "    plt.title(\"Correct answer: \" + signs_names[correct_class[i]])\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    plt.pie(top_probs[0][i], labels=prob_names[i])\n",
    "    plt.show()"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Visualize your network's feature maps here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# image_input: the test image being fed into the network to produce the feature maps\n",
    "# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer\n",
    "# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output\n",
    "# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry\n",
    "\n",
    "def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):\n",
    "    # Here make sure to preprocess your image_input in a way your network expects\n",
    "    # with size, normalization, ect if needed\n",
    "    # image_input =\n",
    "    # Note: x should be the same name as your network's tensorflow data placeholder variable\n",
    "    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function\n",
    "    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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