CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Convolutional Neural Network\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import time\n",
    "from datetime import timedelta\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convolutional Layer 1.\n",
    "filter_size1 = 5          # Convolution filters are 5 x 5 pixels.\n",
    "num_filters1 = 16         # There are 16 of these filters.\n",
    "\n",
    "# Convolutional Layer 2.\n",
    "filter_size2 = 5          # Convolution filters are 5 x 5 pixels.\n",
    "num_filters2 = 36         # There are 36 of these filters.\n",
    "\n",
    "# Fully-connected layer.\n",
    "fc_size = 128             # Number of neurons in fully-connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of:\n",
      "- Training-set:\t\t55000\n",
      "- Test-set:\t\t10000\n",
      "- Validation-set:\t5000\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of:\")\n",
    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.test.cls = np.argmax(data.test.labels, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "img_size = 28\n",
    "\n",
    "# Images are stored in one-dimensional arrays of this length.\n",
    "img_size_flat = img_size * img_size\n",
    "\n",
    "# Tuple with height and width of images used to reshape arrays.\n",
    "img_shape = (img_size, img_size)\n",
    "\n",
    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
    "num_channels = 1\n",
    "\n",
    "# Number of classes, one class for each of 10 digits.\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_images(images, cls_true, cls_pred=None):\n",
    "    assert len(images) == len(cls_true) == 9\n",
    "    \n",
    "    # Create figure with 3x3 sub-plots.\n",
    "    fig, axes = plt.subplots(3, 3)\n",
    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
    "\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Plot image.\n",
    "        ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
    "\n",
    "        # Show true and predicted classes.\n",
    "        if cls_pred is None:\n",
    "            xlabel = \"True: {0}\".format(cls_true[i])\n",
    "        else:\n",
    "            xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n",
    "\n",
    "        # Show the classes as the label on the x-axis.\n",
    "        ax.set_xlabel(xlabel)\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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WY+smesMF4TrG4zF6vR4vxCiLmBJNptMpXC4XmwykUimkUikkEgkWOLfbvdArZGH1mEwm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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75039ff320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the first images from the test-set.\n",
    "images = data.test.images[0:9]\n",
    "\n",
    "cls_true = data.test.cls[0:9]\n",
    "\n",
    "plot_images(images=images, cls_true=cls_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def new_weights(shape):\n",
    "    return tf.Variable(tf.truncated_normal(shape, stddev=0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def new_biases(length):\n",
    "    return tf.Variable(tf.constant(0.05, shape=[length]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def new_conv_layer(input,              # The previous layer.\n",
    "                   num_input_channels, # Num. channels in prev. layer.\n",
    "                   filter_size,        # Width and height of each filter.\n",
    "                   num_filters,        # Number of filters.\n",
    "                   use_pooling=True):  # Use 2x2 max-pooling.\n",
    "\n",
    "    # Shape of the filter-weights for the convolution.\n",
    "    # This format is determined by the TensorFlow API.\n",
    "    shape = [filter_size, filter_size, num_input_channels, num_filters]\n",
    "\n",
    "    # Create new weights aka. filters with the given shape.\n",
    "    weights = new_weights(shape=shape)\n",
    "\n",
    "    # Create new biases, one for each filter.\n",
    "    biases = new_biases(length=num_filters)\n",
    "\n",
    "    layer = tf.nn.conv2d(input=input,\n",
    "                         filter=weights,\n",
    "                         strides=[1, 1, 1, 1],\n",
    "                         padding='SAME')\n",
    "\n",
    "    layer += biases\n",
    "\n",
    "    if use_pooling:\n",
    "\n",
    "        layer = tf.nn.max_pool(value=layer,\n",
    "                               ksize=[1, 2, 2, 1],\n",
    "                               strides=[1, 2, 2, 1],\n",
    "                               padding='SAME')\n",
    "\n",
    "    # Rectified Linear Unit (ReLU).\n",
    "    # It calculates max(x, 0) for each input pixel x.\n",
    "    # This adds some non-linearity to the formula and allows us\n",
    "    # to learn more complicated functions.\n",
    "    layer = tf.nn.relu(layer)\n",
    "\n",
    "    return layer, weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def flatten_layer(layer):\n",
    "    # Get the shape of the input layer.\n",
    "    layer_shape = layer.get_shape()\n",
    "\n",
    "    num_features = layer_shape[1:4].num_elements()\n",
    "\n",
    "    layer_flat = tf.reshape(layer, [-1, num_features])\n",
    "\n",
    "    return layer_flat, num_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def new_fc_layer(input,          \n",
    "                 num_inputs,     \n",
    "                 num_outputs,    \n",
    "                 use_relu=True):\n",
    "    weights = new_weights(shape=[num_inputs, num_outputs])\n",
    "    biases = new_biases(length=num_outputs)\n",
    "\n",
    "    layer = tf.matmul(input, weights) + biases\n",
    "\n",
    "    if use_relu:\n",
    "        layer = tf.nn.relu(layer)\n",
    "\n",
    "    return layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_true_cls = tf.argmax(y_true, dimension=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer_conv1, weights_conv1 = \\\n",
    "    new_conv_layer(input=x_image,\n",
    "                   num_input_channels=num_channels,\n",
    "                   filter_size=filter_size1,\n",
    "                   num_filters=num_filters1,\n",
    "                   use_pooling=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Relu:0' shape=(?, 14, 14, 16) dtype=float32>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_conv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer_conv2, weights_conv2 = \\\n",
    "    new_conv_layer(input=layer_conv1,\n",
    "                   num_input_channels=num_filters1,\n",
    "                   filter_size=filter_size2,\n",
    "                   num_filters=num_filters2,\n",
    "                   use_pooling=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Relu_1:0' shape=(?, 7, 7, 36) dtype=float32>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_conv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layer_flat, num_features = flatten_layer(layer_conv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Reshape_1:0' shape=(?, 1764) dtype=float32>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1764"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer_fc1 = new_fc_layer(input=layer_flat,\n",
    "                         num_inputs=num_features,\n",
    "                         num_outputs=fc_size,\n",
    "                         use_relu=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Relu_2:0' shape=(?, 128) dtype=float32>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_fc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer_fc2 = new_fc_layer(input=layer_fc1,\n",
    "                         num_inputs=fc_size,\n",
    "                         num_outputs=num_classes,\n",
    "                         use_relu=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_3:0' shape=(?, 10) dtype=float32>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_fc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = tf.nn.softmax(layer_fc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_cls = tf.argmax(y_pred, dimension=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,\n",
    "                                                        labels=y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "session = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "session.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_batch_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Counter for total number of iterations performed so far.\n",
    "total_iterations = 0\n",
    "\n",
    "def optimize(num_iterations):\n",
    "\n",
    "    global total_iterations\n",
    "\n",
    "    start_time = time.time()\n",
    "\n",
    "    for i in range(total_iterations,\n",
    "                   total_iterations + num_iterations):\n",
    "\n",
    "        x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n",
    "        feed_dict_train = {x: x_batch,\n",
    "                           y_true: y_true_batch}\n",
    "\n",
    "        session.run(optimizer, feed_dict=feed_dict_train)\n",
    "\n",
    "        # Print status every 100 iterations.\n",
    "        if i % 100 == 0:\n",
    "            # Calculate the accuracy on the training-set.\n",
    "            acc = session.run(accuracy, feed_dict=feed_dict_train)\n",
    "\n",
    "            # Message for printing.\n",
    "            msg = \"Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}\"\n",
    "\n",
    "            # Print it.\n",
    "            print(msg.format(i + 1, acc))\n",
    "\n",
    "    # Update the total number of iterations performed.\n",
    "    total_iterations += num_iterations\n",
    "\n",
    "    # Ending time.\n",
    "    end_time = time.time()\n",
    "\n",
    "    # Difference between start and end-times.\n",
    "    time_dif = end_time - start_time\n",
    "\n",
    "    # Print the time-usage.\n",
    "    print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_example_errors(cls_pred, correct):\n",
    "\n",
    "\n",
    "    incorrect = (correct == False)\n",
    "    \n",
    "    # Get the images from the test-set that have been\n",
    "    # incorrectly classified.\n",
    "    images = data.test.images[incorrect]\n",
    "    \n",
    "    # Get the predicted classes for those images.\n",
    "    cls_pred = cls_pred[incorrect]\n",
    "\n",
    "    # Get the true classes for those images.\n",
    "    cls_true = data.test.cls[incorrect]\n",
    "    \n",
    "    # Plot the first 9 images.\n",
    "    plot_images(images=images[0:9],\n",
    "                cls_true=cls_true[0:9],\n",
    "                cls_pred=cls_pred[0:9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cls_pred):\n",
    "    # This is called from print_test_accuracy() below.\n",
    "\n",
    "    # cls_pred is an array of the predicted class-number for\n",
    "    # all images in the test-set.\n",
    "\n",
    "    # Get the true classifications for the test-set.\n",
    "    cls_true = data.test.cls\n",
    "    \n",
    "    # Get the confusion matrix using sklearn.\n",
    "    cm = confusion_matrix(y_true=cls_true,\n",
    "                          y_pred=cls_pred)\n",
    "\n",
    "    # Print the confusion matrix as text.\n",
    "    print(cm)\n",
    "\n",
    "    # Plot the confusion matrix as an image.\n",
    "    plt.matshow(cm)\n",
    "\n",
    "    # Make various adjustments to the plot.\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(num_classes)\n",
    "    plt.xticks(tick_marks, range(num_classes))\n",
    "    plt.yticks(tick_marks, range(num_classes))\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "\n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Split the test-set into smaller batches of this size.\n",
    "test_batch_size = 256\n",
    "\n",
    "def print_test_accuracy(show_example_errors=False,\n",
    "                        show_confusion_matrix=False):\n",
    "\n",
    "    # Number of images in the test-set.\n",
    "    num_test = len(data.test.images)\n",
    "\n",
    "    # Allocate an array for the predicted classes which\n",
    "    # will be calculated in batches and filled into this array.\n",
    "    cls_pred = np.zeros(shape=num_test, dtype=np.int)\n",
    "\n",
    "    # Now calculate the predicted classes for the batches.\n",
    "    # We will just iterate through all the batches.\n",
    "    # There might be a more clever and Pythonic way of doing this.\n",
    "\n",
    "    # The starting index for the next batch is denoted i.\n",
    "    i = 0\n",
    "\n",
    "    while i < num_test:\n",
    "        # The ending index for the next batch is denoted j.\n",
    "        j = min(i + test_batch_size, num_test)\n",
    "\n",
    "        # Get the images from the test-set between index i and j.\n",
    "        images = data.test.images[i:j, :]\n",
    "\n",
    "        # Get the associated labels.\n",
    "        labels = data.test.labels[i:j, :]\n",
    "\n",
    "        # Create a feed-dict with these images and labels.\n",
    "        feed_dict = {x: images,\n",
    "                     y_true: labels}\n",
    "\n",
    "        # Calculate the predicted class using TensorFlow.\n",
    "        cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n",
    "\n",
    "        # Set the start-index for the next batch to the\n",
    "        # end-index of the current batch.\n",
    "        i = j\n",
    "\n",
    "    # Convenience variable for the true class-numbers of the test-set.\n",
    "    cls_true = data.test.cls\n",
    "\n",
    "    # Create a boolean array whether each image is correctly classified.\n",
    "    correct = (cls_true == cls_pred)\n",
    "\n",
    "    correct_sum = correct.sum()\n",
    "\n",
    "    # Classification accuracy is the number of correctly classified\n",
    "    # images divided by the total number of images in the test-set.\n",
    "    acc = float(correct_sum) / num_test\n",
    "\n",
    "    # Print the accuracy.\n",
    "    msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n",
    "    print(msg.format(acc, correct_sum, num_test))\n",
    "\n",
    "    # Plot some examples of mis-classifications, if desired.\n",
    "    if show_example_errors:\n",
    "        print(\"Example errors:\")\n",
    "        plot_example_errors(cls_pred=cls_pred, correct=correct)\n",
    "\n",
    "    # Plot the confusion matrix, if desired.\n",
    "    if show_confusion_matrix:\n",
    "        print(\"Confusion Matrix:\")\n",
    "        plot_confusion_matrix(cls_pred=cls_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 9.6% (959 / 10000)\n"
     ]
    }
   ],
   "source": [
    "print_test_accuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Iteration:      1, Training Accuracy:   7.8%\n",
      "Time usage: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 9.0% (903 / 10000)\n"
     ]
    }
   ],
   "source": [
    "print_test_accuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time usage: 0:00:18\n"
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=99) # We already performed 1 iteration above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 68.3% (6826 / 10000)\n",
      "Example errors:\n"
     ]
    },
    {
     "data": {
      "image/png": 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jcXi9XimekmtHCAGbzcZ/9no9GAwGuFwuruUslUqYTqfs/komk9jZ2ZmzVYfD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7503ec4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_test_accuracy(show_example_errors=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance after 1000 optimization iterations\n",
    "\n",
    "After 1000 optimization iterations, the model has greatly increased its accuracy on the test-set to more than 90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Iteration:    101, Training Accuracy:  71.9%\n",
      "Optimization Iteration:    201, Training Accuracy:  75.0%\n",
      "Optimization Iteration:    301, Training Accuracy:  76.6%\n",
      "Optimization Iteration:    401, Training Accuracy:  84.4%\n",
      "Optimization Iteration:    501, Training Accuracy:  89.1%\n",
      "Optimization Iteration:    601, Training Accuracy:  96.9%\n",
      "Optimization Iteration:    701, Training Accuracy:  89.1%\n",
      "Optimization Iteration:    801, Training Accuracy:  92.2%\n",
      "Optimization Iteration:    901, Training Accuracy:  96.9%\n",
      "Time usage: 0:02:23\n"
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=900) # We performed 100 iterations above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 92.8% (9277 / 10000)\n",
      "Example errors:\n"
     ]
    },
    {
     "data": {
      "image/png": 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NfUxRH3MkMZxvUECnJ4/HA5vN9kq5CW1WzWaT3cB6Op0ON3lQVZUD6F6vFx6P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\nTZ9o5anRaDQaTZ/8/+KptMW/dU2DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7503ec4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_test_accuracy(show_example_errors=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance after 10,000 optimization iterations\n",
    "\n",
    "After 10,000 optimization iterations, the model has a classification accuracy on the test-set of about 99%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Iteration:   1001, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   1101, Training Accuracy:  92.2%\n",
      "Optimization Iteration:   1201, Training Accuracy:  92.2%\n",
      "Optimization Iteration:   1301, Training Accuracy:  92.2%\n",
      "Optimization Iteration:   1401, Training Accuracy:  89.1%\n",
      "Optimization Iteration:   1501, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   1601, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   1701, Training Accuracy:  93.8%\n",
      "Optimization Iteration:   1801, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   1901, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   2001, Training Accuracy:  89.1%\n",
      "Optimization Iteration:   2101, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   2201, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   2301, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   2401, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   2501, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   2601, Training Accuracy:  92.2%\n",
      "Optimization Iteration:   2701, Training Accuracy:  98.4%\n",
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     ]
    },
    {
     "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<ipython-input-53-33cf782ce895>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moptimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_iterations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9000\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# We performed 1000 iterations above.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-42-81f5f2bc0346>\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(num_iterations)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[1;31m# TensorFlow assigns the variables in feed_dict_train\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[1;31m# to the placeholder variables and then runs the optimizer.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[1;31m# Print status every 100 iterations.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\LUCIFER\\Anaconda3\\lib\\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    765\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\LUCIFER\\Anaconda3\\lib\\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    963\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\LUCIFER\\Anaconda3\\lib\\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   1013\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32mC:\\Users\\LUCIFER\\Anaconda3\\lib\\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   1020\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\LUCIFER\\Anaconda3\\lib\\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   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=9000) # We performed 1000 iterations above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 98.6% (9856 / 10000)\n",
      "Example errors:\n"
     ]
    },
    {
     "data": {
      "image/png": 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zWWSzWRQKBa65p9misViMM9BJPOcz0EU8r0D/x6xpGkajEQaDAZrNJgqFAo6P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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x750708c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 975    0    0    0    0    0    1    1    3    0]\n",
      " [   0 1132    2    0    0    0    1    0    0    0]\n",
      " [   4    1 1019    0    1    0    0    4    3    0]\n",
      " [   1    0    2 1001    0    1    0    3    2    0]\n",
      " [   0    0    2    0  968    0    1    1    0   10]\n",
      " [   2    0    1    8    0  877    2    1    0    1]\n",
      " [   8    3    0    1    1    5  938    0    2    0]\n",
      " [   1    1   11    1    0    0    0 1011    1    2]\n",
      " [   6    1    4    3    2    1    0    2  949    6]\n",
      " [   4    5    0    3    6    0    0    5    0  986]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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MrC154rDWCO6kp7buIo1xD+A72f4fkR7VNTPrPwf31gjuEXEPrfNAlZm1Owf31gjuZmaF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\nzCro/wMjoSokXrjIdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x750950f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_test_accuracy(show_example_errors=True,\n",
    "                    show_confusion_matrix=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_conv_weights(weights, input_channel=0):\n",
    "\n",
    "    w = session.run(weights)\n",
    "\n",
    "    w_min = np.min(w)\n",
    "    w_max = np.max(w)\n",
    "\n",
    "    num_filters = w.shape[3]\n",
    "\n",
    "    # Number of grids to plot.\n",
    "    # Rounded-up, square-root of the number of filters.\n",
    "    num_grids = math.ceil(math.sqrt(num_filters))\n",
    "    \n",
    "    # Create figure with a grid of sub-plots.\n",
    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
    "\n",
    "    # Plot all the filter-weights.\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Only plot the valid filter-weights.\n",
    "        if i<num_filters:\n",
    "            img = w[:, :, input_channel, i]\n",
    "\n",
    "            # Plot image.\n",
    "            ax.imshow(img, vmin=w_min, vmax=w_max,\n",
    "                      interpolation='nearest', cmap='seismic')\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_conv_layer(layer, image):\n",
    "    # Assume layer is a TensorFlow op that outputs a 4-dim tensor\n",
    "    # which is the output of a convolutional layer,\n",
    "    # e.g. layer_conv1 or layer_conv2.\n",
    "\n",
    "    # Create a feed-dict containing just one image.\n",
    "    # Note that we don't need to feed y_true because it is\n",
    "    # not used in this calculation.\n",
    "    feed_dict = {x: [image]}\n",
    "\n",
    "    # Calculate and retrieve the output values of the layer\n",
    "    # when inputting that image.\n",
    "    values = session.run(layer, feed_dict=feed_dict)\n",
    "\n",
    "    # Number of filters used in the conv. layer.\n",
    "    num_filters = values.shape[3]\n",
    "\n",
    "    # Number of grids to plot.\n",
    "    # Rounded-up, square-root of the number of filters.\n",
    "    num_grids = math.ceil(math.sqrt(num_filters))\n",
    "    \n",
    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
    "\n",
    "    # Plot the output images of all the filters.\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Only plot the images for valid filters.\n",
    "        if i<num_filters:\n",
    "            # Get the output image of using the i'th filter.\n",
    "            # See new_conv_layer() for details on the format\n",
    "            # of this 4-dim tensor.\n",
    "            img = values[0, :, :, i]\n",
    "\n",
    "            # Plot image.\n",
    "            ax.imshow(img, interpolation='nearest', cmap='binary')\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_image(image):\n",
    "    plt.imshow(image.reshape(img_shape),\n",
    "               interpolation='nearest',\n",
    "               cmap='binary')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lMoQlKZEhLEmJvGxZL3LmmWdmt1CbK6+8MrsFaQpXwpKUyBCWpESGsCQlMoQlKZEhLEmJ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\nJSmRISxJiQxhSUr0/yod1yglcXuAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7507063898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = data.test.images[1]\n",
    "plot_image(image1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HP/jByrVr166tsRP1E2fCkpTIEJakRIawJCUyhCUpkSEsSYkMYUlKZAhLUiJDWJISGcKS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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75091a6080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image2 = data.test.images[3]\n",
    "plot_image(image2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jxmV7NP6C4s7yyRAAEB5lCAAIjzIEAIRHGQIAwqMMAQDhUYYAgPAoQwBAeJQhACA8yhAA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75091a6048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(weights=weights_conv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vx/FiRVUso7LUVajU6kWV4c4QABA8BkMAQPAYDAEAwcvInOHo0aMjxw8++KBr\no3YzOHjwoMvUrgRqBfXU1ekry9QOGH369HFZ3759z35dk79Bh0TN0U6ePNllPXr0cFl8Vwn1sLRa\n7EDNQ7Vq1cpl8V0xzMwKCwtdljpvoeZJ4LVo0cJlzzzzTKL/dvfu3S5T17za5SDJ4hclJSUuS304\nnzlDraCgIHL8q1/9yrVRtRVqpyC1M4haOEVdo/G5RXVNqjnD1M8iNZdcGe4MAQDBYzAEAASPwRAA\nEDwGQwBA8DJSQNOtW7fI8SOPPOLaqIfp1UO5qoBFTaaridP4yvZqIrc6qlgHushh3LhxLhs+fLjL\n4ivWqwUYVB+r4g31MLAqlqmuAEP9/yC9ixHEd0cwS16gFl/QQRVXtG/fvkavgf+Jf+4WFxe7NipT\n4sVxZrqP1Wdx/BqsTSGVKsiqDHeGAIDgMRgCAILHYAgACB6DIQAgeBmZQWaiOkxqdXqVxamVgZB9\nVEGEWlUIFy5VJHW+4s4QABA8BkMAQPAYDAEAwUs6mZdnZlZaWlqPp3JhSPkd+OXSL2x5ZmYrV648\n1+dxXkj5PWRlP3MtZ/+1TB/XrI9z1CovrlFOziQz+3PdTivrTK6oqHjhXJ9EutDHlaKfsx99nP2q\n7eOkg2GRmY03szIzK0/LqV248sys2MxmV1RU+HXDLlD0sUM/Zz/6OPsl7uNEgyEAANmMAhoAQPAY\nDAEAwWMwBAAEj8EQABA8BkMAQPAYDAEAwWMwBAAEj8EQABA8BkMAQPAYDAEAwUu0awVr3UWwnmEY\n6OfsRx9nv8R9nHQLp/HGKuhxk80sa1a6N/q4MvRz9qOPs1+1fZx0MCwzM5s5c6b179+/judkdubM\nGZcdOXLEZfv27Uv0eq1atXJZ8+bNa35iCaxcudLuvPNOs89/J1mkzMxs2rRp1q9fv7S84M6dO122\nYcMGl+3duzdy3KRJE9emV69eLuvUqZPLGjduXJNTrNSqVats6tSpZlnaz9OnT7eSkpI6v9ixY8dc\nduLECZepDQFatGjhsgYNMjdzU1paalOmTDHL0j5O1+f16dOnXXbq1KlEmepPlanrNh3vhZp8Xicd\nDMvNzPr372/Dhg2r/Zl9Tg2Ghw4dcpn6MFXatm3rssLCwpqfWM1k258fys3M+vXrZ0OHDk3LC27e\nvNll6g0e/4dLXp7fh1Nd1MXFxS5TA2kdZWU/l5SUpOVaPnz4sMuOHz/uMjUYqn/ENmzYsM7nVAtZ\n2cfp+rwO9SS+AAAIyklEQVRWg+HJkycTZep6V32cm5ub6L+tg2r7mAIaAEDwkt4ZptVrr73msjlz\n5rhM/UtD3VVedNFFLps4caLL1J9lkB5Llixx2SeffOKyxYsXu2z//v1VHpvpPp40aZLLBg4cWOV5\nom7id/vqus3Pz3eZugts1qyZy4YPH+4ydc2rDOkRn7aYO3eua/Pmm2+6rKyszGXqju+mm25y2Zgx\nY1ym/vJTn7gzBAAEj8EQABA8BkMAQPAYDAEAwctIAc3bb78dOf6///s/10Y9o9KzZ0+XqUna7du3\nu+zGG290GQU06XH06FGXqaIX9XiLeoYx/jjE2rVrXZvycl8ZrX4m0kcVsM2aNStyvG7dOtdm0KBB\nLnv66addtnTpUpc99dRTLvv8ObEICmjSQ/XxP/7xj8jxW2+95dp8/PHHLlOP2ajHKNTjT23atHEZ\nBTQAAGQYgyEAIHgMhgCA4DEYAgCCl/YCGrVI77Zt2yLHalJVTaCqYot3333XZWqVA7WeHtJDrRxy\nww03uGz27Nkuy8nJcVl81Ri1Ju2CBQtcloH1Z4OmrqF4IZO69lQhRdICKNX3ajHwdC3IHjpVQBPv\nlw4dOrg2asUY1W758uUuU+vUqvdMpnFnCAAIHoMhACB4DIYAgOClfc5QPQwb32/w/vvvd20uu+wy\nlz366KOJXr+oqMhl9bW5L5JT8zrqgez4Q/xqYQX1Wt27d6/9yaFa6nc+ePDgyLGa42vatKnLevfu\n7TK1qMbVV1/tsoMHD7pMXfOoOdXH8R1/ku41+7e//c1ljRr5IUbVHJwPC6JwZwgACB6DIQAgeAyG\nAIDgMRgCAIKX9gKavLw8l40dO7ba/+53v/udy+bMmeOyBg38+H3LLbe4rGXLltX+TKSPejBaPYS7\nZcsWl3322WeRYzXprgorVJGUeqAXtaMWxxg9enTkWBW3PPvssy4rKChwWUlJicvUQ+DHjx+v8jxR\ne6qAJl7wqN4Hb7zxhss2bdqU6GeqzwVVWJdp3BkCAILHYAgACB6DIQAgeAyGAIDgpb2ARv6QWEHE\nxx9/7No888wzLlPFEF/72tdcduONN9bh7JAO8VVkzMx27drlMtWnS5YsiRyrApp777030WuhfsV3\nHXn//fddm/guNWZ6hZFhw4a5TPV9+/bta3KKqKP4Kl9qx5hly5a5TBXadO7c2WXXXXedy7p27VqT\nU6wX3BkCAILHYAgACB6DIQAgeAyGAIDgZaSA5vTp05Hj559/3rVZt26dy3r06OEyVUgRXzEBmbd2\n7dpE7WbPnu2yffv2RY6/9a1vuTZqZSNkXnx7rXfeece1UVv+jBkzxmWFhYUuU4UUqjAD9Se+StTc\nuXNdm/Lycpepa7Rv374uU6vNqMKpTOPOEAAQPAZDAEDwGAwBAMFjMAQABC8js5bxrZj+9Kc/uTZq\na6Z77rnHZWrViqQrkZw4cSJyfODAAddGbSHTunXrs1+riWPobXZUUdTq1atdFp9Qv/TSS10b1S+n\nTp1yWbzAw8xs9+7dLmvatKnLunTpcvbrPXv2uO/DF8yo3+2QIUNcpgpj1Ko0+fn5LosX4JmZHT58\nOHKs3gutWrVymfqcQVS8YEZdC6pPioqKXDZixAiXqe281Gd4vE/jK+OY+RWRzKJ9rN4XleGdAQAI\nHoMhACB4DIYAgOAxGAIAgpeRApo1a9ZEjo8dO+badOjQwWVqVYLnnnvOZWoFGlUcs3379six2nao\nWbNmLrv++uvPfr1hwwb3fZj16dPHZT/84Q9dNn/+fJfF+/nuu+92bVThgyrAUO8ttdJJy5YtXZZa\nnEU/a/Gte1RBVElJictUgZVaWUYV5KjsyJEjkWP1/lCv36ZNm7Nfnw+rnpyP4itC7dixw7VZsWJF\notd67733XKa28FPvj/iqVnv37nVt1HU8duzYs1/Hx56qcGcIAAgegyEAIHgMhgCA4DEYAgCCl5EZ\n5PhEdbt27VwbtVqE2jpETbQmLZCIr0CjJtDV6gipqxjUZEWDkHTs2NFlarUgVSgVt3//fpepfunZ\ns6fLiouLXda8eXOXqZUrUt+Dahsi+H6IF7KYmc2bN89lqk/Vlj8DBgxwmSqOia+KogqnmjRp4rJx\n48ad/bq0tNR9H/7zVF0LqjBl165dLnv//fddplZ/Up8L8c/6xo0buzbqM+DgwYNnv1bvz8pwZwgA\nCB6DIQAgeAyGAIDgZWTOsHv37pHj22+/3bWJr0Jvph+cV/MHubm5ibL4PGKnTp1cm9SdC1QWfyAV\nlfvJT37iMjVPFJ8L+Oyzz1wbNcen5orV6vdqrkGtxJ86l8HuJNqECRMix2qubuvWrS5bunSpy9Qu\nBGp+StUTxOfuVR+rmoDUzxT1mQOzMWPGRI7VfP3w4cNdpuYM1VyxWgzhS1/6ksu6desWOVZ1CamL\nKHwhdeEU9blRGe4MAQDBYzAEAASPwRAAEDwGQwBA8DJSQHPFFVdEjnv37u3aqIfp1YSs2mlCTbCr\nydaioqLIsXootzpqBXfoQor8/HyXnTx50mXxQop4P5npwhg1Oa6KJlRxhSqeSqUe1IfZiBEjIsfq\nWla7C6hCCrUbheo/9UB2586dI8fqPaOK7VKpHWrgF0W56aabXJvUxQu+oK5HtUiJehBe9Xu8CFL1\nV3V9zEP3AADUAIMhACB4DIYAgOAxGAIAgpeRApr4qiPxlQUq06dPn/o4HZxDqpglTq1qj/NDvGBB\n7UCjMmQXtZKUoooUVWHd+YA7QwBA8BgMAQDBYzAEAAQv6ZxhnpnZypUr6/FULgwpv4NkfzS/cOSZ\nma1atepcn8d5IeX3kJX9zC7vkd9BVvYxn9c1+7zOUSt7uEY5OZPM7M91O62sM7miouKFc30S6UIf\nV4p+zn70cfarto+TDoZFZjbezMrMLPSN3vLMrNjMZldUVPhN8S5Q9LFDP2c/+jj7Je7jRIMhAADZ\njAIaAEDwGAwBAMFjMAQABI/BEAAQPAZDAEDwGAwBAMFjMAQABO//AT5nBMCfqHpyAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7506ceaa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv1, image=image1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qq1TWo0cPlSE1WrRoobJf/epXKvv0009V5v67v3UfwOqSP2TIEJX1799fZal8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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7509215240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv1, image=image2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lzc3lctF/KuYB/Xv/umHvJPrXGbHpnUT/Outg7Z/X4AQAAPvx5SAAAAIwOAEA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7506667320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(weights=weights_conv2, input_channel=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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164RuWDuJ+nVGbGonUb/OOlLr59U4AQDAIVwcBABAABonAAABaJwAAASgcQIA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7507057400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(weights=weights_conv2, input_channel=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CAODA6XaUgwcPBob8Hj58WG3/+uuvG+PasGERkX/5l38xxv0TBCej5ubmwK0QlZWVavsX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\nHadpSk6R9p2TEo7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iAL/4xS+c4p2lrKxMfvKTn3Tpa3SWKVOmqPtWrFhx7g4kRr9+/eJq\nxydOAAAc0HECAOCAjhMAAAd0nAAAOIh3cFC6iEhlZWVwh2VwULduiemXYwY9uI9c6hrpIiIVFRWB\nHYmqUSz/oIeY4wxD/dTaiSS+fvX19YFYzPsk1PXT5oUW0eeM1eYQFRHZv3+/MW6bX3THjh2e7erq\n6rZ/hqF2IiF/7/ol07UvEomc84M5m7jrF41Gz/oQkdkiEk3Cx+x4fr6uflC/z2TtqF+S1476UT/t\nETnzw1lFIpF+IjJdRKpERP8vZ3iki8hwEXk9Go3WJPhYqF8HJGHtRKhfR4SmdiLUr6PO1/rF1XEC\nAIDTwvdHegAAQoyOEwAAB3ScAAA4oOMEAMABHScAAA7imgDhfB1SfK5Qv/ZLwtqJUL+OCE3tRKhf\nR5239fss38TKTcDhr18S1476JXntqB/10x7xTrlXJSKyZMkSKSws9Oz485//rCY988wzxvjRo0fV\nnJkzZxrj06ZNU3P8a/pVVlbKvHnzRM4cdwhUiYisXLlSRo0a1eEn27x5s7pvy5YtxnhpaamaU1BQ\n4NkuLy9vW7Oyqh2H19mqRESefPJJKSoqCuz813/9V2PSmDFj1CecMWOGMZ6fn6/mlJWVGeMTJ04M\nxCoqKmTu3LkiIarfc889FzgHWlpa1KTf/e53xviHH36o5gwYMMAYnzVrlpozdOhQz3ZlZaXccccd\nIuGonYilfra1Q99++21j3D+9ZSx/Ldr4r2+xznROn6qoqAjTe1eknde+NWvWGOPNzc1qjjaF30UX\nXaTm+NfOjffaF2/H2SgiUlhYGPgl7t27V01KTU01xrt3767mDBw40BgvLi5WcyZNmqTtCsufBhpF\nREaNGiWTJ0/u8JPZFmnW5gW1dSSWTjUM9WsUESkqKpLx48cHdmqLbufl5alPqNXC/5/CWKY5aUXk\nbL/P0NSvtLQ0cKy2i9CGDRuM8W3btqk52nt39OjRao7pP0NnhKF2Ipb61dXVqUl79uwxxm3z9mrX\nONs55u84Y4Sqfq7XvvbMlax1nBMmTFBzLAtXW+vH4CAAABzQcQIA4CDeP9WKyOk/sfr/TJiTk6O2\nv+WWW4xxbfkhEf3PkLt371Zz/Mv7hHG5n85k+474jTfeMMZtf6pNBrW1tXL48OFAvGfPnsb2tj/P\nfP/73zfGV65cqeZ88sknxvill14aiNm+x0qUU6dOBf5MuH79erX9q6++aoxrX7+IiPTp08cY1/7M\nLRL82ias791u3boFjs32PtS+P961a5eaM336dOfj8v95MoxLdWk++OADdd+JEyeMcdvXfNp37JY/\nx7ZbOM9SAABCio4TAAAHdJwAADig4wQAwAEdJwAADug4AQBw4HQ7Sk5OjvTt29cTu+GGG9T2tn2a\n1157zRhPpmHWnUW7reHf//3f1Zx9+/YZ4z/96U875ZgSZciQITJixIhAfP78+cb2L774ovpcjzzy\niDHe1NSk5mRmZp7lCMPt1KlTgfPJdovSK6+8YoyvW7dOzTH9fkT021RERE6ePKnuC5NIJBK4Btmm\nLCwpKTHGly5dquZ873vfa9/BJSnbrSVf/OIXneIi9vd8Z+MTJwAADug4AQBwQMcJAIADOk4AABzQ\ncQIA4MBpVK1pom3bpMX+BZLb2CbdnTJlijFuG4GVLBNFu9IWDX7zzTfVnK9//evGuK0m/km4tQmW\nE6mhoUGOHz8eiN98883G9v4FamMtW7bMGH/vvffUHNNaoCLm89J2riZKSkpKYIJ229qQq1atMsYt\n6z+q710b/6IOtrVmEykajQZ+dstaouramrZFqTUfffSRus//O7Wtlxo2tvMlIyPDGLetx2lbT1fj\n789qa2vjyjs/ehgAAM4ROk4AABzQcQIA4ICOEwAAB3ScAAA4oOMEAMCB09hv05Bs260Lv/rVr4zx\nf/qnf1Jz6urqjPH+/furOTNnzvRsHzp0SG2bTFasWGGM2261WLRokTHuH3Ydy3/by+bNm89+cOdY\nbW2t1NTUBOLa+fLEE0+oz7Vp0yZjvLy8XM3Rzr+JEycGYmFckMA0ybvt9hvt9qVBgwapOQ8++KAx\nXlVVpeb4ryem33FYbd261TlHe0+L6Oel7ZY//7Xg6NGjzseUKOvXr1f3XXrppcb4Sy+9pOZoC4TY\n+oOUlBTP9vbt29W2sfjECQCAAzpOAAAc0HECAOCAjhMAAAd0nAAAOHAaVdu7d+/ABO22UXYnT540\nxpubm9Wcvn37GuPXXnutmnPJJZd4tnfs2CEvv/yy2j5ZzJkzxxh/8skn1RzTROgi9hGADQ0Nnm3b\nRMqJkpOTY1wc4Hvf+56x/XXXXac+l3b+jRo1qn0HlwRaWloCP7dtpLr23v23f/s3NWfNmjXGuG0U\n+C233OLZ9k9aHmYHDhxQ9/l/rjaTJ092fh1twniTrKws5+dPFNs16ctf/rIxbjuXLr/8cmPcf32L\nNWDAAM92WVmZPPDAA2r7NnziBADAAR0nAAAO6DgBAHBAxwkAgAM6TgAAHNBxAgDgwOl2FNNE0TZX\nXnmlMd7VtzuUlZXJww8/3KWv0VlaW1vVfcOGDTPGtYnNRfRbLS644AI1p6CgwLPdq1cvtW2iZGVl\nGY/roYce6rTXGDx4sHOO6bYN7VaOROrWrVtg4nbbbQ4TJkwwxqdPn96px+VXVlZmncg7USKRSGDy\nfq1GIiI/+MEPuvqQkp7/uhNrzJgxzs+n3YrTFbfo8IkTAAAHdJwAADig4wQAwAEdJwAADuIdHJQu\nIlJZWRnYEca5JcvLy9v+mZ7I44iRLuI5rk/ZBgfV1NQY47Yvu1taWpziIiI9enhPg82bN7f9Mwz1\nSxcRqaioMO70H/u5ZhoIFHOsoamf6b0bjUb1pPTEHHoyvXdPnDihJmnz2GpzSXeWZKqf9p4W0c8/\nW807Q9z1i0ajZ32IyGwRiSbhY3Y8P19XP6jfZ7J21C/Ja0f9qJ/2iJz54awikUg/EZkuIlUiEr6l\nM4LSRWS4iLwejUbNH9vOIerXfklYOxHq1xGhqZ0I9euo87V+cXWcAADgNAYHAQDggI4TAAAHdJwA\nADig4wQAwAEdJwAADug4AQBwQMcJAICD/wcIP/3a1N7K/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75081f6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv2, image=image1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S5Cn9H8vUTWH5aOCr+vl/Pm1qLhttyjgRkUGDBhnjtmn6LL+PMNRPvfZELu3A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\nBwycAAA4YOAEAMDB/wfrZk1m/xgzRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7506dae390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv2, image=image2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}