Traffic_Sign_Classifier.ipynb

{
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   "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."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   "outputs": [],
   "source": [
    "#code cell number 0\n",
    "# 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 = 'train.p'\n",
    "validation_file= 'valid.p'\n",
    "testing_file = 'test.p'\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
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   "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 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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",
      "Number of validation examples =  4410\n"
     ]
    }
   ],
   "source": [
    "#code cell number 1\n",
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "import random\n",
    "\n",
    "# TODO: Number of training examples\n",
    "n_train = len(X_train)\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = len(X_test)\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "index = random.randint(0,n_train)\n",
    "image = X_train[index]\n",
    "image_shape = image.shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "uniqueClasses = set(y_train)\n",
    "n_classes = len(uniqueClasses)\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)\n",
    "\n",
    "#validation example\n",
    "n_validation = len(X_valid)\n",
    "print(\"Number of validation examples = \", n_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8d4549d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 2\n",
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import matplotlib.pyplot as plt\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(image)\n",
    "print(y_train[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1X0gRQWb27HHRb7DiytnJNUBdtqphwR8DvgncMSJ+ERHPBt4MPDwizgUeWu+T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8d455de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 3\n",
    "#load the proper labes\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "colNames = ['ClassId', 'SignName']\n",
    "data = pd.read_csv('signnames.csv', names=colNames)\n",
    "sign_names = data.SignName.tolist()\n",
    "sign_names.pop(0)\n",
    "\n",
    "train = list(y_train)\n",
    "my_dictionary = dict((x,train.count(x)) for x in set(train))\n",
    "#print (my_dictionary)\n",
    "#plt.bar(my_dictionary.keys(),my_dictionary.values())\n",
    "\n",
    "my_keys = list(my_dictionary.keys())\n",
    "#print (my_keys)\n",
    "my_counts = list(my_dictionary.values())\n",
    "#print (my_counts)\n",
    "\n",
    "count_series = pd.Series.from_array(my_counts)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "ax = count_series.plot(kind='bar', title=\"Details for the training set\")\n",
    "ax.set_ylabel(\"Count of trafic signs\")\n",
    "ax.set_xlabel(\"Trafic signs\")\n",
    "ax.set_xticklabels(sign_names)\n",
    "\n",
    "plt.show(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture\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.\n",
    "\n",
    "**NOTE:** 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!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Pre-process the Data Set (normalization, grayscale, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "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": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#code cell number 4\n",
    "### Preprocess the data here. Preprocessing steps could include normalization, converting to grayscale, etc.\n",
    "### Feel free to use as many code cells as needed.\n",
    "# suffle the data\n",
    "from sklearn.utils import shuffle\n",
    "X_train, y_train = shuffle(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### epochs count and batch size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#code cell number 5\n",
    "import tensorflow as tf\n",
    "\n",
    "EPOCHS = 10\n",
    "BATCH_SIZE = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### LeNet \n",
    "simple implementation of lenet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#code cell number 6\n",
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "from tensorflow.contrib.layers import flatten\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "def LeNet(x):    \n",
    "    # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    # SOLUTION: Layer 1: Convolutional. Input = 32x32x3. Output = 32x32x6.\n",
    "    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 3, 6), mean = mu, stddev = sigma))\n",
    "    conv1_b = tf.Variable(tf.zeros(6))\n",
    "    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n",
    "\n",
    "    # SOLUTION: Activation.\n",
    "    conv1 = tf.nn.relu(conv1)\n",
    "    \n",
    "    #dropout 1\n",
    "    hidden_drop1 = tf.nn.dropout(conv1, keep_prob)\n",
    "\n",
    "    # SOLUTION: Pooling. Input = 32x32x6. Output = 14x14x6.\n",
    "    conv1 = tf.nn.max_pool(hidden_drop1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # SOLUTION: Layer 2: Convolutional. Output = 10x10x16.\n",
    "    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n",
    "    conv2_b = tf.Variable(tf.zeros(16))\n",
    "    conv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n",
    "    \n",
    "    # SOLUTION: Activation.\n",
    "    conv2 = tf.nn.relu(conv2)\n",
    "\n",
    "    # SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.\n",
    "    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # SOLUTION: Flatten. Input = 5x5x16. Output = 400.\n",
    "    fc0   = flatten(conv2)\n",
    "    \n",
    "    # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\n",
    "    fc1_b = tf.Variable(tf.zeros(120))\n",
    "    fc1   = tf.matmul(fc0, fc1_W) + fc1_b\n",
    "    \n",
    "    # SOLUTION: Activation.\n",
    "    fc1    = tf.nn.relu(fc1)\n",
    "\n",
    "    # SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.\n",
    "    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n",
    "    fc2_b  = tf.Variable(tf.zeros(84))\n",
    "    fc2    = tf.matmul(fc1, fc2_W) + fc2_b\n",
    "    \n",
    "    # SOLUTION: Activation.\n",
    "    fc2    = tf.nn.relu(fc2)\n",
    "\n",
    "    # SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43 (trafic signs).\n",
    "    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\n",
    "    fc3_b  = tf.Variable(tf.zeros(43))\n",
    "    logits = tf.matmul(fc2, fc3_W) + fc3_b\n",
    "    \n",
    "    return logits\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Features, Labels, One hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#code cell number 7\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 3))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y = tf.one_hot(y, 43)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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 test set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Training Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#code cell number 8\n",
    "rate = 0.001\n",
    "\n",
    "logits = LeNet(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Evaluation Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#code cell number 9\n",
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "def evaluate(X_data, y_data):\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "    for offset in range(0, num_examples, BATCH_SIZE):\n",
    "        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Start training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "Cicle  10 Validation Accuracy = 0.844\n",
      "Cicle  20 Validation Accuracy = 0.898\n",
      "Cicle  30 Validation Accuracy = 0.913\n",
      "Cicle  40 Validation Accuracy = 0.916\n",
      "Cicle  50 Validation Accuracy = 0.923\n",
      "Cicle  60 Validation Accuracy = 0.920\n",
      "Cicle  70 Validation Accuracy = 0.936\n",
      "Cicle  80 Validation Accuracy = 0.926\n",
      "Cicle  90 Validation Accuracy = 0.931\n",
      "Cicle  100 Validation Accuracy = 0.907\n",
      "Cicle  110 Validation Accuracy = 0.908\n",
      "Cicle  120 Validation Accuracy = 0.927\n",
      "time to stop\n",
      "Cicle  127 Validation Accuracy = 0.943\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "#code cell number 10\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(X_train)\n",
    "    \n",
    "    print(\"Training...\")\n",
    "    print()\n",
    "    #for i in range(EPOCHS):\n",
    "    k=0;\n",
    "    running = True\n",
    "    while running:\n",
    "        k+=1\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob:0.5})\n",
    "            #sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})\n",
    "            \n",
    "        validation_accuracy = evaluate(X_valid, y_valid)\n",
    "        if k % 10 == 0 :\n",
    "            #print(\"EPOCH {} ...\".format(i+1))\n",
    "            #print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "            print(\"Cicle \", k, \"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        if (validation_accuracy > 0.940):\n",
    "            print (\"time to stop\")\n",
    "            print(\"Cicle \", k, \"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "            running = False\n",
    "        \n",
    "    saver.save(sess, './lenet')\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Test one time only on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy = 0.924\n"
     ]
    }
   ],
   "source": [
    "#code cell number 11\n",
    "with tf.Session() as sess:\n",
    "    #saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "    saver.restore(sess, './lenet')\n",
    "\n",
    "    test_accuracy = evaluate(X_test, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Run some random test images throw the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(target value, my prediction): ( 2 , 2 )\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa85c6660b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 12\n",
    "with tf.Session() as sess:\n",
    "    #get_argmax = tf.argmax(logits, 1)\n",
    "    saver.restore(sess, './lenet')\n",
    "    \n",
    "    index = random.randint(0,n_test)\n",
    "    image = X_test[index]\n",
    "    image_shape = image.shape\n",
    "    plt.figure(figsize=(1,1))\n",
    "    plt.imshow(image)\n",
    "    \n",
    "    prediction = sess.run(tf.argmax(logits, 1), feed_dict = {x: [image], keep_prob: 1.0})\n",
    "    print (\"(target value, my prediction): (\",y_test[index],\",\", prediction[0],\")\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code_35_ahead_only.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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PjwH/6yT+l5/5UxLqdFyMn3kTpeU35QUFg0KsBRzabQSwoyhEwzVEZ4JBaFFMzyjBWLwp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\n7gB9m+gO0LeJ/h9RFrqkBuXL1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8a0a903c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 13\n",
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def get_image(image_name):\n",
    "    path_to_image = \"bogdan_images/\"+image_name\n",
    "    my_image = cv2.imread(path_to_image)\n",
    "    my_image = cv2.resize(my_image, (32, 32))\n",
    "    my_image = cv2.cvtColor(my_image, cv2.COLOR_BGR2RGB)\n",
    "    return my_image\n",
    "\n",
    "\n",
    "code_35_ahead_only = get_image(\"code_35_ahead_only.png\")\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(code_35_ahead_only)\n",
    "print(\"code_35_ahead_only.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code_3_speed_limit_60.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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Eh6NHyxo/JHoE9EOit3VtUhH5DuA/0l+A8Gf3qDPHriXdDriiwgrw7bvqDMYx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8a0aa2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 14\n",
    "code_3_speed_limit_60 = get_image(\"code_3_speed_limit_60.png\")\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(code_3_speed_limit_60)\n",
    "print(\"code_3_speed_limit_60.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code_9_no_passing.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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TNWRZlQ9+4AniqIiRnWGFZRTCzMwip04dwRiLzwRRQ6VYoVIuUxsoUS4Vee3y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\n5AHQ90keAH2f5AHQ90keAH2f5P8BcLJJBEcqnXsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa89cb07a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 15\n",
    "code_9_no_passing = get_image(\"code_9_no_passing.png\")\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(code_9_no_passing)\n",
    "print(\"code_9_no_passing.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code_13_yeld.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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oVT3d8vOLwF9cqP4nP/GxxvmmkVFGRsfOy4tIFsfE1A08j0pXJydvvY3g0DGG9uyDYhlv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\nRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa89ca67160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 16\n",
    "code_13_yeld = get_image(\"code_13_yeld.png\")\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(code_13_yeld)\n",
    "print(\"code_13_yeld.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code_14_stop.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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nN4G/tavNVfnQ61AQnx0Yzz/YYyw9eS2BHnZ9acUv6fa0iPzUdfrbsaICcC1n\n+EMi8oyI/FcRGYcduc6rijPzfnblQ3f3M5Fx8QvAl3iVFAT81ZgM36mqjwJ/D39x7xryuL380qve\nFtud69zjON2jze5+fkVV34J/It7Kq6Qg4LUFeqj1pXVgSTc8j3LV+qaZLIrILPg0GnsUVarqsvYD\ng48Bf5NrFGfu6mdHm2v0g6pu4evAexTE9a5tUF5LoHvrS4tIEb++9B8PNhCR0UybEL+k29+hn3vc\nXaaRr6gA/ZB/R5sMuFx+ED/B7ch17tFPurvNrn4+gJ8kGaAgnqdPQQyO59ryWnkdmUK8Nxvky8DP\n7fH7Ubw38m3gubwN8If4/4TRBS7gX+OYBL6c9fdF4DN7tPkEniZ4Bp8VsgP9fysbT32gn29co81g\nP3868PerpiDuhuB3WP4qTIZ/LeQu0HdI7gJ9h+Qu0HdI7gJ9h+Qu0HdI7gJ9h+Qu0HdI/h/bQX4J\nk9y3EQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa89c9d5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code cell number 17\n",
    "code_14_stop = get_image(\"code_14_stop.png\")\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(code_14_stop)\n",
    "print(\"code_14_stop.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code_35_ahead_only.png ->  [35]\n",
      "code_3_speed_limit_60.png ->  [3]\n",
      "code_9_no_passing.png ->  [9]\n",
      "code_13_yeld.png ->  [13]\n",
      "code_14_stop.png ->  [14]\n"
     ]
    }
   ],
   "source": [
    "#code cell number 18\n",
    "### 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",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, './lenet')\n",
    "    \n",
    "    #code_35_ahead_only.png\n",
    "    prediction = sess.run(tf.argmax(logits, 1), feed_dict = {x: [code_35_ahead_only], keep_prob: 1.0})\n",
    "    print (\"code_35_ahead_only.png -> \", prediction)\n",
    "    \n",
    "    #code_3_speed_limit_60.png\n",
    "    prediction = sess.run(tf.argmax(logits, 1), feed_dict = {x: [code_3_speed_limit_60], keep_prob: 1.0})\n",
    "    print (\"code_3_speed_limit_60.png -> \", prediction)\n",
    "    \n",
    "    #code_9_no_passing.png\n",
    "    prediction = sess.run(tf.argmax(logits, 1), feed_dict = {x: [code_9_no_passing], keep_prob: 1.0})\n",
    "    print (\"code_9_no_passing.png -> \", prediction)\n",
    "    \n",
    "    #code_13_yeld.png\n",
    "    prediction = sess.run(tf.argmax(logits, 1), feed_dict = {x: [code_13_yeld], keep_prob: 1.0})\n",
    "    print (\"code_13_yeld.png -> \", prediction)\n",
    "    \n",
    "    #code_14_stop.png\n",
    "    prediction = sess.run(tf.argmax(logits, 1), feed_dict = {x: [code_14_stop], keep_prob: 1.0})\n",
    "    print (\"code_14_stop.png -> \", prediction)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Test Accuracy = 100% acurate on the downloaded images. :)\n"
     ]
    }
   ],
   "source": [
    "#code cell number 19\n",
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, './lenet')\n",
    "    \n",
    "    my_images = [code_35_ahead_only, code_3_speed_limit_60, code_9_no_passing, code_13_yeld, code_14_stop]\n",
    "    my_codes = [35, 3, 9, 13, 14]\n",
    "\n",
    "    my_test_accuracy = evaluate(my_images, my_codes) * 100\n",
    "    print(\"My Test Accuracy = {:.0f}% acurate on the downloaded images. :)\".format(my_test_accuracy))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tk.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top 5 softmax probabilities [[ 1.     0.     0.     0.     0.   ]\n",
      " [ 0.449  0.331  0.209  0.009  0.003]\n",
      " [ 1.     0.     0.     0.     0.   ]\n",
      " [ 1.     0.     0.     0.     0.   ]\n",
      " [ 1.     0.     0.     0.     0.   ]]\n",
      "top 5 predictios [[35 18 22 33  0]\n",
      " [ 3  5  2  1  7]\n",
      " [ 9  0  1  2  3]\n",
      " [13  0  1  2  3]\n",
      " [14  3 23 30 25]]\n"
     ]
    }
   ],
   "source": [
    "#code cell number 20\n",
    "### 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')\n",
    "    \n",
    "    my_images = [code_35_ahead_only, code_3_speed_limit_60, code_9_no_passing, code_13_yeld, code_14_stop]\n",
    "    #my_codes = [35, 3, 9, 13, 14]\n",
    "        \n",
    "    #top_values = sess.run(tf.nn.top_k(logits, 5), feed_dict = {x: my_images, keep_prob: 1.0})\n",
    "    #print ('top_values', top_values)\n",
    "\n",
    "    np.set_printoptions(precision=3,suppress=True)\n",
    "    softmax_values = sess.run(tf.nn.softmax(logits), feed_dict = {x: my_images, keep_prob: 1.0})   \n",
    "    values, indeces = tf.nn.top_k(softmax_values,5)\n",
    "    \n",
    "    values = sess.run(values)\n",
    "    indeces = sess.run(indeces)\n",
    "    print (\"top 5 softmax probabilities\", values)\n",
    "    print (\"top 5 predictios\", indeces)\n",
    "    \n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "> **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. 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. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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. "
   ]
  }
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