LeNet-Traffic-Signs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LeNet Lab\n",
    "![LeNet Architecture](lenet.png)\n",
    "Source: Yan LeCun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "Load the MNIST data, which comes pre-loaded with TensorFlow.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Image Shape: (32, 32, 3)\n",
      "\n",
      "Training Set:   34799 samples\n",
      "Validation Set: 4410 samples\n",
      "Test Set:       12630 samples\n"
     ]
    }
   ],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "training_file = 'traffic-signs-data/train.p'\n",
    "validation_file= 'traffic-signs-data/valid.p'\n",
    "testing_file = 'traffic-signs-data/test.p'\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n",
    "\n",
    "assert(len(X_train) == len(y_train))\n",
    "assert(len(X_valid) == len(y_valid))\n",
    "assert(len(X_test) == len(y_test))\n",
    "\n",
    "print()\n",
    "print(\"Image Shape: {}\".format(X_train[0].shape))\n",
    "print()\n",
    "print(\"Training Set:   {} samples\".format(len(X_train)))\n",
    "print(\"Validation Set: {} samples\".format(len(X_valid)))\n",
    "print(\"Test Set:       {} samples\".format(len(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.\n",
    "\n",
    "However, the LeNet architecture only accepts 32x32xC images, where C is the number of color channels.\n",
    "\n",
    "In order to reformat the MNIST data into a shape that LeNet will accept, we pad the data with two rows of zeros on the top and bottom, and two columns of zeros on the left and right (28+2+2 = 32).\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No padding necessary\n"
     ]
    }
   ],
   "source": [
    "# import numpy as np\n",
    "\n",
    "# # Pad images with 0s\n",
    "# X_train      = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
    "# X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
    "# X_test       = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
    "    \n",
    "# print(\"Updated Image Shape: {}\".format(X_train[0].shape))\n",
    "print(\"No padding necessary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Data\n",
    "\n",
    "View a sample from the dataset.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3003fc0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "index = random.randint(0, len(X_train))\n",
    "image = X_train[index].squeeze()\n",
    "\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(image)\n",
    "print(y_train[index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess Data\n",
    "\n",
    "Shuffle the training data.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "X_train, y_train = shuffle(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup TensorFlow\n",
    "The `EPOCH` and `BATCH_SIZE` values affect the training speed and model accuracy.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "EPOCHS = 15\n",
    "BATCH_SIZE = 256\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement LeNet-5\n",
    "Implement the [LeNet-5](http://yann.lecun.com/exdb/lenet/) neural network architecture.\n",
    "\n",
    "This is the only cell you need to edit.\n",
    "### Input\n",
    "The LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.\n",
    "\n",
    "### Architecture\n",
    "**Layer 1: Convolutional.** The output shape should be 28x28x6.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Pooling.** The output shape should be 14x14x6.\n",
    "\n",
    "**Layer 2: Convolutional.** The output shape should be 10x10x16.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Pooling.** The output shape should be 5x5x16.\n",
    "\n",
    "**Flatten.** Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. The easiest way to do is by using `tf.contrib.layers.flatten`, which is already imported for you.\n",
    "\n",
    "**Layer 3: Fully Connected.** This should have 120 outputs.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 4: Fully Connected.** This should have 84 outputs.\n",
    "\n",
    "**Activation.** Your choice of activation function.\n",
    "\n",
    "**Layer 5: Fully Connected (Logits).** This should have 10 outputs.\n",
    "\n",
    "### Output\n",
    "Return the result of the 2nd fully connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.contrib.layers import flatten\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",
    "    # Layer 1: Convolutional. Input = 32x32x3. Output = 28x28x6.\n",
    "    filter_depth = 6\n",
    "    filter_shape = [5, 5, 3, filter_depth]\n",
    "    weights_conv1 = tf.Variable(tf.truncated_normal(filter_shape, mean=mu, stddev=sigma))\n",
    "    bias_conv1 = tf.Variable(tf.truncated_normal([filter_depth]))\n",
    "    x = tf.nn.conv2d(x, weights_conv1, strides=[1, 1, 1, 1], padding='VALID')\n",
    "    conv1 = tf.nn.bias_add(x, bias_conv1)\n",
    "\n",
    "    # Activation.\n",
    "    conv1 = tf.nn.relu(conv1)\n",
    "    _, m, n, d = conv1.get_shape() \n",
    "    assert (m,n,d) == (28,28,6)\n",
    "    \n",
    "    # Apply 3x3x6 with SAME padding\n",
    "    # conv1 = tf.nn.conv2d(x, tf.Variable(tf.truncated_normal([3,3,6,6])), strides=[1,1,1,1], padding='SAME')\n",
    "\n",
    "    # Pooling. Input = 28x28x6. Output = 14x14x6.\n",
    "    conv1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "\n",
    "    # Layer 2: Convolutional. Input = 14x14x6. Output = 10x10x16.\n",
    "    l2_in = conv1\n",
    "    l2_filter_depth = 16\n",
    "    l2_filter_shape = [5,5,6,l2_filter_depth]\n",
    "    weights_conv2 = tf.Variable(tf.truncated_normal(l2_filter_shape, mean=mu, stddev=sigma))\n",
    "    bias_conv2 = tf.Variable(tf.truncated_normal([l2_filter_depth]))\n",
    "    conv2 = tf.nn.conv2d(l2_in, weights_conv2, strides=[1,1,1,1], padding='VALID')\n",
    "    conv2 = tf.nn.bias_add(conv2, bias_conv2)\n",
    "    \n",
    "    # Activation.\n",
    "    conv2 = tf.nn.relu(conv2)\n",
    "    \n",
    "    # #AD Apply 1x1 convolution -- didn't help\n",
    "    # conv2 = tf.nn.conv2d(conv2, tf.Variable(tf.truncated_normal([1,1,16,16])), strides=[1,1,1,1], padding='VALID')\n",
    "    \n",
    "    # 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",
    "    # Flatten. Input = 5x5x16. Output = 400.\n",
    "    flat = tf.contrib.layers.flatten(conv2)\n",
    "    \n",
    "    # Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "    weights_fc_l3 = tf.Variable(tf.truncated_normal([400,120], mean=mu, stddev=sigma))\n",
    "    bias_fc_l3 = tf.Variable(tf.truncated_normal([120]))\n",
    "    fc1 = tf.add(bias_fc_l3, tf.matmul(flat,weights_fc_l3))\n",
    "    \n",
    "    # Activation.\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "\n",
    "    # Layer 4: Fully Connected. Input = 120. Output = 84.\n",
    "    weights_fc_l4 = tf.Variable(tf.truncated_normal([120,84], mean=mu, stddev=sigma))\n",
    "    bias_fc_l4 = tf.Variable(tf.truncated_normal([84]))\n",
    "    fc2 = tf.add(bias_fc_l4, tf.matmul(fc1, weights_fc_l4))\n",
    "    \n",
    "    # Activation.\n",
    "    fc2 = tf.nn.relu(fc2)\n",
    "\n",
    "    # Layer 5: Fully Connected. Input = 84. Output = 43.\n",
    "    weights_out = tf.Variable(tf.truncated_normal([84,43], mean=mu, stddev=sigma))\n",
    "    bias_out = tf.Variable(tf.truncated_normal([43]))\n",
    "    logits = tf.add(tf.matmul(fc2,weights_out), bias_out)\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LeNet5 - Implmeneting my own variation of LeNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "def assert_shape(expected_shape, layer):\n",
    "    _, m, n, d = layer.get_shape() \n",
    "    assert expected_shape == (m,n,d)\n",
    "\n",
    "def LeNet6(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",
    "    # Layer 1: Convolutional. Input = 32x32x3. Output = 28x28x6.\n",
    "    filter_depth = 24\n",
    "    filter_shape = [3, 3, 3, filter_depth]\n",
    "    weights_conv1 = tf.Variable(tf.truncated_normal(filter_shape, mean=mu, stddev=sigma))\n",
    "    bias_conv1 = tf.Variable(tf.truncated_normal([filter_depth]))\n",
    "    x = tf.nn.conv2d(x, weights_conv1, strides=[1, 1, 1, 1], padding='SAME')\n",
    "    conv1 = tf.nn.bias_add(x, bias_conv1)\n",
    "\n",
    "    # Activation.\n",
    "    conv1 = tf.nn.relu(conv1)\n",
    "    assert_shape((32,32,24), conv1)\n",
    "    \n",
    "    # Pooling. Input = 32x32x24. Output = 16x16x24.\n",
    "    conv1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "    assert_shape((16,16,24), conv1)\n",
    "\n",
    "    # Layer 2: Convolutional. Input = 16x16x24. Output = 12x12x48.\n",
    "    l2_in = conv1\n",
    "    l2_filter_depth = 48\n",
    "    l2_filter_shape = [5,5,24,l2_filter_depth]\n",
    "    weights_conv2 = tf.Variable(tf.truncated_normal(l2_filter_shape, mean=mu, stddev=sigma))\n",
    "    bias_conv2 = tf.Variable(tf.truncated_normal([l2_filter_depth]))\n",
    "    conv2 = tf.nn.conv2d(l2_in, weights_conv2, strides=[1,1,1,1], padding='VALID')\n",
    "    conv2 = tf.nn.bias_add(conv2, bias_conv2)\n",
    "    \n",
    "    # Activation.\n",
    "    conv2 = tf.nn.relu(conv2)\n",
    "    assert_shape((12,12,48), conv2)\n",
    "        \n",
    "    # Pooling. Input = 10x10x16. Output = 5x5x16.\n",
    "    conv2 = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,1,1,1], padding='SAME')\n",
    "    assert_shape((12,12,48), conv2)\n",
    "    \n",
    "    # Layer 3 input 12x12x48 output 4x4x96\n",
    "    filter_depth = 96\n",
    "    w3 = tf.Variable(tf.truncated_normal([7,7,48,filter_depth], mean=mu, stddev=sigma))\n",
    "    conv3 = tf.nn.conv2d(conv2, w3, strides=[1,1,1,1], padding='VALID')\n",
    "    conv3 = tf.nn.bias_add(conv3, tf.Variable(tf.truncated_normal([filter_depth])))\n",
    "    conv3 = tf.nn.relu(conv3)\n",
    "    assert_shape((6,6,96), conv3)\n",
    "    \n",
    "    # Maxpooling\n",
    "    conv3 = tf.nn.max_pool(conv3, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "    assert_shape((3,3,96),conv3)\n",
    "\n",
    "    # Flatten. Input = 3x3x96. Output = 864.\n",
    "    flat = tf.contrib.layers.flatten(conv3)\n",
    "    \n",
    "    # Layer 4: Fully Connected. Input = 864. Output = 400.\n",
    "    weights_fc_l3 = tf.Variable(tf.truncated_normal([864,400], mean=mu, stddev=sigma))\n",
    "    bias_fc_l3 = tf.Variable(tf.truncated_normal([400]))\n",
    "    fc1 = tf.add(bias_fc_l3, tf.matmul(flat,weights_fc_l3))\n",
    "    \n",
    "    # Activation.\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "\n",
    "    # Layer 5: Fully Connected. Input = 400. Output = 120.\n",
    "    weights_fc_l4 = tf.Variable(tf.truncated_normal([400,120], mean=mu, stddev=sigma))\n",
    "    bias_fc_l4 = tf.Variable(tf.truncated_normal([120]))\n",
    "    fc2 = tf.add(bias_fc_l4, tf.matmul(fc1, weights_fc_l4))\n",
    "    \n",
    "    # Activation.\n",
    "    fc2 = tf.nn.relu(fc2)\n",
    "\n",
    "    # Layer 6, input 120 output 84\n",
    "    fc2 = tf.matmul(fc2, tf.Variable(tf.truncated_normal([120,84]))) + tf.Variable(tf.truncated_normal([84]))\n",
    "    fc2 = tf.nn.relu(fc2)\n",
    "    \n",
    "    # Layer 6: Fully Connected. Input = 84. Output = 43.\n",
    "    weights_out = tf.Variable(tf.truncated_normal([84,43], mean=mu, stddev=sigma))\n",
    "    bias_out = tf.Variable(tf.truncated_normal([43]))\n",
    "    logits = tf.add(tf.matmul(fc2,weights_out), bias_out)\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features and Labels\n",
    "Train LeNet to classify [MNIST](http://yann.lecun.com/exdb/mnist/) data.\n",
    "\n",
    "`x` is a placeholder for a batch of input images.\n",
    "`y` is a placeholder for a batch of output labels.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "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, 43)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Pipeline\n",
    "Create a training pipeline that uses the model to classify MNIST data.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rate = 0.001\n",
    "\n",
    "logits = LeNet6(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "Evaluate how well the loss and accuracy of the model for a given dataset.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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",
    "    losses = []\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, loss = sess.run([accuracy_operation, loss_operation], feed_dict={x: batch_x, y: batch_y})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "        losses.append(loss)\n",
    "    return (total_accuracy / num_examples, losses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model\n",
    "Run the training data through the training pipeline to train the model.\n",
    "\n",
    "Before each epoch, shuffle the training set.\n",
    "\n",
    "After each epoch, measure the loss and accuracy of the validation set.\n",
    "\n",
    "Save the model after training.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 ...\n",
      "Validation Accuracy = 0.091\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    764\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    765\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 766\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    767\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    962\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 964\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    965\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1014\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1015\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1019\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1020\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1001\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1002\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\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",
    "    accuracy = []\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_operation, feed_dict={x: batch_x, y: batch_y})\n",
    "            \n",
    "        validation_accuracy, loss = evaluate(X_valid, y_valid)\n",
    "        accuracy.append(validation_accuracy)\n",
    "        print(\"EPOCH {} ...\".format(i+1))\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "\n",
    "    saver.save(sess, './lenet')\n",
    "    print(\"Model saved\")\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.plot(accuracy)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Model\n",
    "Once you are completely satisfied with your model, evaluate the performance of the model on the test set.\n",
    "\n",
    "Be sure to only do this once!\n",
    "\n",
    "If you were to measure the performance of your trained model on the test set, then improve your model, and then measure the performance of your model on the test set again, that would invalidate your test results. You wouldn't get a true measure of how well your model would perform against real data.\n",
    "\n",
    "You do not need to modify this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy = 0.457\n",
      "CPU times: user 6.63 s, sys: 1.09 s, total: 7.72 s\n",
      "Wall time: 2.52 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "\n",
    "    test_accuracy,_ = evaluate(X_test, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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