Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Traffic Sign Recognition Classifier with Conv Net\n",
    " Training and testing CNN model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython import get_ipython\n",
    "from glob import glob\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import pickle\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import flatten\n",
    "from sklearn.utils import shuffle\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "training_file = './data/train.p'\n",
    "validation_file='./data/valid.p'\n",
    "testing_file = './data/test.p'\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## 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**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Number of training examples = 34799\nNumber of validation examples = 4410\nNumber of testing examples = 12630\nImage data shape = (32, 32, 3)\nNumber of classes = 43\n"
    }
   ],
   "source": [
    "\n",
    "# Number of training examples\n",
    "n_train = len(X_train)\n",
    "\n",
    "# Number of validation examples\n",
    "n_validation = len(X_valid)\n",
    "\n",
    "# Number of testing examples\n",
    "n_test = len(X_test)\n",
    "\n",
    "# Shape of a traffic sign image\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# 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 validation examples =\", n_validation)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sign_names = pd.read_csv('signnames.csv', delimiter=',')\n",
    "sign_names_dict = sign_names.to_dict()['SignName']\n",
    "def signName(label):\n",
    "    return '{}-{}'.format(label, sign_names_dict[label])\n",
    "\n",
    "def signNames(labels):\n",
    "    return list(map(lambda l: signName(l), labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "41-End of no passing\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "plt.imshow(X_train[0])\n",
    "print(signName(y_train[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1-Speed limit (30km/h)\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Show random image from a train set\n",
    "index = np.random.randint(0, len(X_train))\n",
    "plt.imshow(X_train[index])\n",
    "print(signName(y_train[index]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Show distribution difference among all the data sets\n",
    "plt.hist(y_train, bins=n_classes)\n",
    "plt.hist(y_test, bins=n_classes)\n",
    "plt.hist(y_valid, bins=n_classes)\n",
    "plt.xlabel('Sample Count')\n",
    "plt.ylabel('Number of Instances')\n",
    "plt.legend(['Training Data', 'Test Data', 'Validation Data'], loc='upper center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Pre-processing the Data Set (normalization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing data\n",
    "def preprocess(x):\n",
    "    return (x.astype(np.float32) - 128) / 128\n",
    "X_train_processed = preprocess(X_train)\n",
    "X_valid_processed = preprocess(X_valid)\n",
    "X_test_processsed = preprocess(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity check for preprocessing step...\n",
      "Max value in preprocessed train data 0.9921875\n",
      "Min value in preprocessed train data -1.0\n"
     ]
    }
   ],
   "source": [
    "print('Sanity check for preprocessing step...')\n",
    "print('Max value in preprocessed train data {}'.format(np.max(X_train_processed)))\n",
    "print('Min value in preprocessed train data {}'.format(np.min(X_train_processed)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Net Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def conv2d(x, W, b, strides=1):\n",
    "    conv = tf.nn.conv2d(x, W, strides=[1,strides,strides,1], padding='VALID')\n",
    "    conv = tf.nn.bias_add(conv, b)\n",
    "    return tf.nn.relu(conv)\n",
    "\n",
    "def maxpool2d(x, k=2):\n",
    "    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='VALID')\n",
    "\n",
    "def avgpool2d(x, k=2):\n",
    "    return tf.nn.avg_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='VALID')\n",
    "\n",
    "EPOCHS = 10\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "dropout_keep_prob = tf.placeholder(tf.float32)\n",
    "conv_dropout_keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "conv1 = None\n",
    "def ConvNet(x):\n",
    "    global conv1\n",
    "\n",
    "    # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    # Layer 1: Convolutional. Input = 32x32x3. Output = 28x28x6.\n",
    "    wc1 = tf.Variable(tf.truncated_normal([5, 5, 3, 6], mu, sigma), name='wc1')\n",
    "    bc1 = tf.Variable(tf.truncated_normal([6], mu, sigma), name='bc1')\n",
    "    conv1 = conv2d(x, wc1, bc1, strides=1)\n",
    "\n",
    "    # Pooling. Input = 28x28x6. Output = 14x14x6\n",
    "    conv1 = maxpool2d(conv1, k=2)\n",
    "    conv1 = tf.nn.dropout(conv1, keep_prob=conv_dropout_keep_prob)\n",
    "    \n",
    "    # Layer 2: Convolutional. Output = 10x10x16.\n",
    "    wc2 = tf.Variable(tf.truncated_normal([5, 5, 6, 16], mu, sigma), name='wc2')\n",
    "    bc2 = tf.Variable(tf.truncated_normal([16], mu, sigma), name='bc2')\n",
    "    conv2 = conv2d(conv1, wc2, bc2, strides=1)\n",
    "\n",
    "    # Pooling. Input = 10x10x16. Output = 5x5x16\n",
    "    conv2 = maxpool2d(conv2, k=2)\n",
    "    conv2 = tf.nn.dropout(conv2, keep_prob=conv_dropout_keep_prob)\n",
    "\n",
    "    # Flatten. Input = 5x5x16. Output = 400.\n",
    "    conv2Flat = flatten(conv2)\n",
    "    \n",
    "    # Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "    wd1 = tf.Variable(tf.truncated_normal([400, 120], mu, sigma), name='wd1')\n",
    "    bd1 = tf.Variable(tf.truncated_normal([120], mu, sigma), name='bd1')\n",
    "    fc1 = tf.add(tf.matmul(conv2Flat, wd1), bd1)\n",
    "\n",
    "    # ReLU Activation\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "    # Dropout\n",
    "    fc1 = tf.nn.dropout(fc1, keep_prob=dropout_keep_prob)\n",
    "\n",
    "    # Layer 4: Fully Connected. Input = 120. Output = 84.\n",
    "    wd2 = tf.Variable(tf.truncated_normal([120, 84], mu, sigma), name='wd2')\n",
    "    bd2 = tf.Variable(tf.truncated_normal([84], mu, sigma), name='bd2')\n",
    "    fc2 = tf.add(tf.matmul(fc1, wd2), bd2)\n",
    "    \n",
    "    # ReLU Activation\n",
    "    fc2 = tf.nn.relu(fc2)\n",
    "    # Dropout\n",
    "    fc2 = tf.nn.dropout(fc2, keep_prob=dropout_keep_prob)\n",
    "\n",
    "    # Layer 5: Fully Connected. Input = 84. Output = n_classes\n",
    "    wOut = tf.Variable(tf.truncated_normal([84, n_classes], mu, sigma), name='wOut')\n",
    "    bOut = tf.Variable(tf.truncated_normal([n_classes], mu, sigma), name='bOut')\n",
    "    logits = tf.add(tf.matmul(fc2, wOut), bOut)\n",
    "    \n",
    "    return logits\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, (None, 32, 32, 3))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y = tf.one_hot(y, n_classes)\n",
    "\n",
    "rate = 0.001\n",
    "\n",
    "logits = ConvNet(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=one_hot_y)\n",
    "loss = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=rate)\n",
    "training_operation = optimizer.minimize(loss)\n",
    "\n",
    "### Model Evaluation\n",
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "def evaluate(X_data, y_data):\n",
    "    sess = tf.get_default_session()\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    for offset in range(0, num_examples, BATCH_SIZE):\n",
    "        maxI = offset + BATCH_SIZE\n",
    "        batch_x, batch_y = X_data[offset:maxI], y_data[offset:maxI]\n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, dropout_keep_prob: 1, conv_dropout_keep_prob: 1})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "model_save_file = './model/model.ckpt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1: Validation accuracy 0.724\n",
      "EPOCH 2: Validation accuracy 0.843\n",
      "EPOCH 3: Validation accuracy 0.874\n",
      "EPOCH 4: Validation accuracy 0.913\n",
      "EPOCH 5: Validation accuracy 0.923\n",
      "EPOCH 6: Validation accuracy 0.919\n",
      "EPOCH 7: Validation accuracy 0.933\n",
      "EPOCH 8: Validation accuracy 0.943\n",
      "EPOCH 9: Validation accuracy 0.941\n",
      "EPOCH 10: Validation accuracy 0.943\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "tf.set_random_seed(123456)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    num_examples = len(X_train_processed)\n",
    "\n",
    "    print(\"Training...\")\n",
    "    print()\n",
    "    for i in range(EPOCHS):\n",
    "        X_shuffled, y_shuffled = shuffle(X_train_processed, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            batch_x, batch_y = X_shuffled[offset:offset+BATCH_SIZE], y_shuffled[offset:offset+BATCH_SIZE]\n",
    "            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, dropout_keep_prob: 0.75, conv_dropout_keep_prob: 0.9})\n",
    "        \n",
    "        validation_accuracy = evaluate(X_valid_processed, y_valid)\n",
    "        print(\"EPOCH {}: Validation accuracy {:.3f}\".format(i+1, validation_accuracy))\n",
    "\n",
    "    saver.save(sess, model_save_file)\n",
    "    print(\"Model saved\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model.ckpt\n",
      "Train Accuracy = 0.988\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, model_save_file)\n",
    "\n",
    "    test_accuracy = evaluate(X_train_processed, y_train)\n",
    "    print(\"Train Accuracy = {:.3f}\".format(test_accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Validation Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model.ckpt\n",
      "Validation Accuracy = 0.943\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, model_save_file)\n",
    "\n",
    "    test_accuracy = evaluate(X_valid_processed, y_valid)\n",
    "    print(\"Validation Accuracy = {:.3f}\".format(test_accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Test Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model.ckpt\n",
      "Test Accuracy = 0.932\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, model_save_file)\n",
    "\n",
    "    test_accuracy = evaluate(X_test_processsed, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Test a Model on New Images (out of this data set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "\n",
    "filenames = glob('./images/*')\n",
    "test_imgs = []\n",
    "test_labels = []\n",
    "plt.figure(1, figsize=(15,15))\n",
    "for i in range(len(filenames)):\n",
    "    img = mpimg.imread(filenames[i])\n",
    "    img = cv2.resize(img, dsize=(32, 32), interpolation=cv2.INTER_LINEAR)\n",
    "    test_imgs.append(img)\n",
    "    label = int(filenames[i].split('/')[-1].split('_')[0])\n",
    "    test_labels.append(label)\n",
    "    # plot\n",
    "    plt.subplot(2,3,i+1)\n",
    "    plt.title(signName(label))\n",
    "    plt.imshow(img)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model.ckpt\n",
      "Accuracy = 100.00%\n",
      "======================\n",
      "======================\n",
      "TopKV2(values=array([[  9.99997020e-01,   2.31072067e-06,   4.70548628e-07,\n",
      "          1.85036569e-07,   6.92207678e-08],\n",
      "       [  9.99997020e-01,   3.00198985e-06,   7.49278623e-13,\n",
      "          1.73374092e-13,   8.46017389e-14],\n",
      "       [  9.99999285e-01,   6.31251737e-07,   8.66772112e-08,\n",
      "          1.08704201e-09,   2.64516742e-10],\n",
      "       [  5.95162451e-01,   2.19559431e-01,   1.84057906e-01,\n",
      "          7.50366249e-04,   3.85624764e-04],\n",
      "       [  5.99067986e-01,   4.00929153e-01,   1.64152198e-06,\n",
      "          1.24570909e-06,   3.27628896e-10]], dtype=float32), indices=array([[12, 10, 42, 17,  9],\n",
      "       [17, 14,  0, 26,  9],\n",
      "       [14, 17,  1, 29,  0],\n",
      "       [32, 18, 38, 20, 41],\n",
      "       [39, 33, 37, 40, 35]], dtype=int32))\n",
      "LABELS\n",
      "['12-Priority road', '17-No entry', '14-Stop', '32-End of all speed and passing limits', '39-Keep left']\n",
      "======================\n",
      "PREDICTIONS\n",
      "['12-Priority road', '17-No entry', '14-Stop', '32-End of all speed and passing limits', '39-Keep left']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test_imgs_processed = preprocess(np.array(test_imgs))\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, model_save_file)\n",
    "\n",
    "    accuracy_test_imgs = evaluate(test_imgs_processed, test_labels)\n",
    "    print(\"Accuracy = {:.2f}%\".format(accuracy_test_imgs*100))\n",
    "    print(\"======================\")\n",
    "    print(\"======================\")\n",
    "\n",
    "    softmax = tf.nn.softmax(logits)\n",
    "    top5_softmax_eval = sess.run(tf.nn.top_k(softmax, k=5), feed_dict={x: test_imgs_processed, y: test_labels, dropout_keep_prob: 1., conv_dropout_keep_prob: 1.0})\n",
    "    print(top5_softmax_eval)\n",
    "\n",
    "predictions = top5_softmax_eval.indices[:,0]\n",
    "print(\"LABELS\")\n",
    "print(signNames(test_labels))\n",
    "print(\"======================\")\n",
    "print(\"PREDICTIONS\")\n",
    "print(signNames(predictions))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Top 5 Softmax Predictions per Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 720x1440 with 10 Axes>"
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   ],
   "source": [
    "\n",
    "softmax_labels_named = list(map(lambda x: signNames(x), top5_softmax_eval.indices))\n",
    "plt.figure(figsize=(10,20))\n",
    "for i in range(len(softmax_labels_named)):\n",
    "    plt.subplot(5,2,i*2+1)\n",
    "    y_pos = np.arange(len(softmax_labels_named[i]))\n",
    "    plt.barh(y_pos, top5_softmax_eval.values[i], align='center', alpha=0.5)\n",
    "    plt.yticks(y_pos, softmax_labels_named[i])\n",
    "    plt.subplot(5,2,i*2+2)\n",
    "    plt.imshow(test_imgs[i])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Visualizing the Neural Network's State with Test Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/model.ckpt\n",
      "(1, 14, 14, 6)\n"
     ]
    },
    {
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    "# 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",
    "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, dropout_keep_prob: 1., conv_dropout_keep_prob: 1.})\n",
    "    print(activation.shape)\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, model_save_file)\n",
    "    outputFeatureMap([test_imgs_processed[4]], conv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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