dlnd_image_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Image Classification\n",
    "In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).  The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.  You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers.  At the end, you'll get to see your neural network's predictions on the sample images.\n",
    "## Get the Data\n",
    "Run the following cell to download the [CIFAR-10 dataset for python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All files found!\n"
     ]
    }
   ],
   "source": [
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir\n",
    "from tqdm import tqdm\n",
    "import problem_unittests as tests\n",
    "import tarfile\n",
    "\n",
    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
    "\n",
    "# Use Floyd's cifar-10 dataset if present\n",
    "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
    "if isfile(floyd_cifar10_location):\n",
    "    tar_gz_path = floyd_cifar10_location\n",
    "else:\n",
    "    tar_gz_path = 'cifar-10-python.tar.gz'\n",
    "\n",
    "class DLProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "if not isfile(tar_gz_path):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
    "        urlretrieve(\n",
    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
    "            tar_gz_path,\n",
    "            pbar.hook)\n",
    "\n",
    "if not isdir(cifar10_dataset_folder_path):\n",
    "    with tarfile.open(tar_gz_path) as tar:\n",
    "        tar.extractall()\n",
    "        tar.close()\n",
    "\n",
    "\n",
    "tests.test_folder_path(cifar10_dataset_folder_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data\n",
    "The dataset is broken into batches to prevent your machine from running out of memory.  The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:\n",
    "* airplane\n",
    "* automobile\n",
    "* bird\n",
    "* cat\n",
    "* deer\n",
    "* dog\n",
    "* frog\n",
    "* horse\n",
    "* ship\n",
    "* truck\n",
    "\n",
    "Understanding a dataset is part of making predictions on the data.  Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.\n",
    "\n",
    "Ask yourself \"What are all possible labels?\", \"What is the range of values for the image data?\", \"Are the labels in order or random?\".  Answers to questions like these will help you preprocess the data and end up with better predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Stats of batch 2:\n",
      "Samples: 10000\n",
      "Label Counts: {0: 984, 1: 1007, 2: 1010, 3: 995, 4: 1010, 5: 988, 6: 1008, 7: 1026, 8: 987, 9: 985}\n",
      "First 20 Labels: [1, 6, 6, 8, 8, 3, 4, 6, 0, 6, 0, 3, 6, 6, 5, 4, 8, 3, 2, 6]\n",
      "\n",
      "Example of Image 2:\n",
      "Image - Min Value: 0 Max Value: 240\n",
      "Image - Shape: (32, 32, 3)\n",
      "Label - Label Id: 6 Name: frog\n"
     ]
    },
    {
     "data": {
      "image/png": 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iB4DCFD0AFKboAaAwRQ8AhSl6AChM0QNAYYoeAApT9ABQmKIHgMIUPQAUpugB\noDBFDwCFKXoAKEzRA0Bhih4AClP0AFCYogeAwhQ9ABSm6AGgMEUPAIUpegAoTNEDQGGKHgAKU/QA\nUJiiB4DCFD0AFKboAaAwRQ8AhSl6AChM0QNAYYoeAApT9ABQmKIHgMIUPQAUpugBoDBFDwCFKXoA\nKEzRA0Bh/xOcxDplVQV0VQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa476360b8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import helper\n",
    "import numpy as np\n",
    "\n",
    "# Explore the dataset\n",
    "batch_id = 2\n",
    "sample_id = 2\n",
    "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement Preprocess Functions\n",
    "### Normalize\n",
    "In the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive.  The return object should be the same shape as `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def normalize(x):\n",
    "    \"\"\"\n",
    "    Normalize a list of sample image data in the range of 0 to 1\n",
    "    : x: List of image data.  The image shape is (32, 32, 3)\n",
    "    : return: Numpy array of normalize data\n",
    "    \"\"\"\n",
    "    \n",
    "    a = 0.0\n",
    "    b = 1.0\n",
    "    color_min = 0\n",
    "    color_max = 255\n",
    "    return a + ( ( (x - color_min)*(b - a) )/( color_max - color_min ) )\n",
    "\n",
    "\n",
    "tests.test_normalize(normalize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encode\n",
    "Just like the previous code cell, you'll be implementing a function for preprocessing.  This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels.  Implement the function to return the list of labels as One-Hot encoded Numpy array.  The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`.  Make sure to save the map of encodings outside the function.\n",
    "\n",
    "Hint: Don't reinvent the wheel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "lb_encoding = None\n",
    "\n",
    "def one_hot_encode(x):\n",
    "    \"\"\"\n",
    "    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
    "    : x: List of sample Labels\n",
    "    : return: Numpy array of one-hot encoded labels\n",
    "    \"\"\"\n",
    "\n",
    "    global lb_encoding\n",
    "    if lb_encoding is None:\n",
    "        lb = preprocessing.LabelBinarizer()\n",
    "        lb_encoding = lb.fit(x)\n",
    "        \n",
    "    return lb_encoding.transform(x)\n",
    "\n",
    "\n",
    "tests.test_one_hot_encode(one_hot_encode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Randomize Data\n",
    "As you saw from exploring the data above, the order of the samples are randomized.  It doesn't hurt to randomize it again, but you don't need to for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess all the data and save it\n",
    "Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Preprocess Training, Validation, and Testing Data\n",
    "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Point\n",
    "This is your first checkpoint.  If you ever decide to come back to this notebook or have to restart the notebook, you can start from here.  The preprocessed data has been saved to disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import problem_unittests as tests\n",
    "import helper\n",
    "\n",
    "# Load the Preprocessed Validation data\n",
    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the network\n",
    "For the neural network, you'll build each layer into a function.  Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function.  This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.\n",
    "\n",
    ">**Note:** If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages to build each layer, except the layers you build in the \"Convolutional and Max Pooling Layer\" section.  TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.\n",
    "\n",
    ">However, if you would like to get the most out of this course, try to solve all the problems _without_ using anything from the TF Layers packages. You **can** still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the `conv2d` class, [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d), you would want to use the TF Neural Network version of `conv2d`, [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d). \n",
    "\n",
    "Let's begin!\n",
    "\n",
    "### Input\n",
    "The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions\n",
    "* Implement `neural_net_image_input`\n",
    " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
    " * Set the shape using `image_shape` with batch size set to `None`.\n",
    " * Name the TensorFlow placeholder \"x\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
    "* Implement `neural_net_label_input`\n",
    " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
    " * Set the shape using `n_classes` with batch size set to `None`.\n",
    " * Name the TensorFlow placeholder \"y\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
    "* Implement `neural_net_keep_prob_input`\n",
    " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) for dropout keep probability.\n",
    " * Name the TensorFlow placeholder \"keep_prob\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
    "\n",
    "These names will be used at the end of the project to load your saved model.\n",
    "\n",
    "Note: `None` for shapes in TensorFlow allow for a dynamic size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image Input Tests Passed.\n",
      "Label Input Tests Passed.\n",
      "Keep Prob Tests Passed.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "def neural_net_image_input(image_shape):\n",
    "    \"\"\"\n",
    "    Return a Tensor for a batch of image input\n",
    "    : image_shape: Shape of the images\n",
    "    : return: Tensor for image input.\n",
    "    \"\"\"\n",
    "\n",
    "    return tf.placeholder(\"float\", [None, image_shape[0], image_shape[1], image_shape[2]], name=\"x\")\n",
    "\n",
    "\n",
    "def neural_net_label_input(n_classes):\n",
    "    \"\"\"\n",
    "    Return a Tensor for a batch of label input\n",
    "    : n_classes: Number of classes\n",
    "    : return: Tensor for label input.\n",
    "    \"\"\"\n",
    "\n",
    "    return tf.placeholder(\"float\", [None, n_classes], name=\"y\")\n",
    "\n",
    "\n",
    "def neural_net_keep_prob_input():\n",
    "    \"\"\"\n",
    "    Return a Tensor for keep probability\n",
    "    : return: Tensor for keep probability.\n",
    "    \"\"\"\n",
    "\n",
    "    return tf.placeholder(\"float\", name=\"keep_prob\")\n",
    "\n",
    "\n",
    "tf.reset_default_graph()\n",
    "tests.test_nn_image_inputs(neural_net_image_input)\n",
    "tests.test_nn_label_inputs(neural_net_label_input)\n",
    "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolution and Max Pooling Layer\n",
    "Convolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:\n",
    "* Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.\n",
    "* Apply a convolution to `x_tensor` using weight and `conv_strides`.\n",
    " * We recommend you use same padding, but you're welcome to use any padding.\n",
    "* Add bias\n",
    "* Add a nonlinear activation to the convolution.\n",
    "* Apply Max Pooling using `pool_ksize` and `pool_strides`.\n",
    " * We recommend you use same padding, but you're welcome to use any padding.\n",
    "\n",
    "**Note:** You **can't** use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for **this** layer, but you can still use TensorFlow's [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) package. You may still use the shortcut option for all the **other** layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
    "    \"\"\"\n",
    "    Apply convolution then max pooling to x_tensor\n",
    "    :param x_tensor: TensorFlow Tensor\n",
    "    :param conv_num_outputs: Number of outputs for the convolutional layer\n",
    "    :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
    "    :param conv_strides: Stride 2-D Tuple for convolution\n",
    "    :param pool_ksize: kernal size 2-D Tuple for pool\n",
    "    :param pool_strides: Stride 2-D Tuple for pool\n",
    "    : return: A tensor that represents convolution and max pooling of x_tensor\n",
    "    \"\"\"\n",
    "\n",
    "    # Create weight and bias\n",
    "    color_channels = x_tensor.get_shape().as_list()[3]\n",
    "    weight = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], color_channels, conv_num_outputs], stddev=0.01))\n",
    "    bias = tf.Variable(tf.zeros(conv_num_outputs))\n",
    "    \n",
    "    # Apply Convolution\n",
    "    conv_layer = tf.nn.conv2d(x_tensor, weight, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME')\n",
    "    \n",
    "    # Add bias\n",
    "    conv_layer = tf.nn.bias_add(conv_layer, bias)\n",
    "    \n",
    "    # Apply ReLU\n",
    "    conv_layer = tf.nn.relu(conv_layer)\n",
    "    \n",
    "    # Apply Max Pooling\n",
    "    conv_layer = tf.nn.max_pool(conv_layer, \n",
    "                                ksize=[1, pool_ksize[0], pool_ksize[1], 1], \n",
    "                                strides=[1, pool_strides[0], pool_strides[1], 1],\n",
    "                                padding='SAME')\n",
    "    \n",
    "    return conv_layer \n",
    "\n",
    "tests.test_con_pool(conv2d_maxpool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flatten Layer\n",
    "Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor.  The output should be the shape (*Batch Size*, *Flattened Image Size*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def flatten(x_tensor):\n",
    "    \"\"\"\n",
    "    Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
    "    : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
    "    : return: A tensor of size (Batch Size, Flattened Image Size).\n",
    "    \"\"\"\n",
    "\n",
    "    shape = x_tensor.get_shape().as_list()\n",
    "    # Get all dimensions besides batch size and get product of those dimensions\n",
    "    dim_prod = np.prod(shape[1:])\n",
    "    return tf.reshape(x_tensor, [-1, dim_prod])\n",
    "\n",
    "tests.test_flatten(flatten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fully-Connected Layer\n",
    "Implement the `fully_conn` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def fully_conn(x_tensor, num_outputs):\n",
    "    \"\"\"\n",
    "    Apply a fully connected layer to x_tensor using weight and bias\n",
    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
    "    : num_outputs: The number of output that the new tensor should be.\n",
    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
    "    \"\"\"\n",
    "\n",
    "    weight = tf.Variable(tf.random_normal([x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.01))\n",
    "    bias = tf.Variable(tf.random_normal([num_outputs]))\n",
    "    \n",
    "    return tf.nn.relu(tf.add(tf.matmul(x_tensor, weight),bias))\n",
    "\n",
    "tests.test_fully_conn(fully_conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Layer\n",
    "Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.\n",
    "\n",
    "**Note:** Activation, softmax, or cross entropy should **not** be applied to this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def output(x_tensor, num_outputs):\n",
    "    \"\"\"\n",
    "    Apply a output layer to x_tensor using weight and bias\n",
    "    : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
    "    : num_outputs: The number of output that the new tensor should be.\n",
    "    : return: A 2-D tensor where the second dimension is num_outputs.\n",
    "    \"\"\"\n",
    "\n",
    "    weight = tf.Variable(tf.random_normal([x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.01))\n",
    "    bias = tf.Variable(tf.random_normal([num_outputs]))\n",
    "    return tf.add(tf.matmul(x_tensor, weight),bias)\n",
    "\n",
    "\n",
    "tests.test_output(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Convolutional Model\n",
    "Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits.  Use the layers you created above to create this model:\n",
    "\n",
    "* Apply 1, 2, or 3 Convolution and Max Pool layers\n",
    "* Apply a Flatten Layer\n",
    "* Apply 1, 2, or 3 Fully Connected Layers\n",
    "* Apply an Output Layer\n",
    "* Return the output\n",
    "* Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Built!\n"
     ]
    }
   ],
   "source": [
    "def conv_net(x, keep_prob):\n",
    "    \"\"\"\n",
    "    Create a convolutional neural network model\n",
    "    : x: Placeholder tensor that holds image data.\n",
    "    : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
    "    : return: Tensor that represents logits\n",
    "    \"\"\"\n",
    "\n",
    "    conv_layer = conv2d_maxpool(x, 128, (4,4), (2,2), (4,4), (2,2))\n",
    "    conv_layer = conv2d_maxpool(conv_layer, 256, (4,4), (2,2), (4,4), (2,2))\n",
    "\n",
    "    # Apply a Flatten Layer\n",
    "    # Function Definition from Above:\n",
    "    #   flatten(x_tensor)\n",
    "    flat_x = flatten(conv_layer)\n",
    "\n",
    "    # Apply 1, 2, or 3 Fully Connected Layers\n",
    "    #    Play around with different number of outputs\n",
    "    # Function Definition from Above:\n",
    "    #   fully_conn(x_tensor, num_outputs)\n",
    "    full_layer = fully_conn(flat_x, 512)\n",
    "    full_layer = fully_conn(full_layer, 1024)\n",
    "    full_layer = tf.nn.dropout(full_layer, keep_prob)\n",
    "    \n",
    "    # Apply an Output Layer\n",
    "    #    Set this to the number of classes\n",
    "    # Function Definition from Above:\n",
    "    #   output(x_tensor, num_outputs)\n",
    "    output_layer = output(full_layer, 10)\n",
    "    \n",
    "    # TODO: return output\n",
    "    return output_layer\n",
    "\n",
    "\n",
    "##############################\n",
    "## Build the Neural Network ##\n",
    "##############################\n",
    "\n",
    "# Remove previous weights, bias, inputs, etc..\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# Inputs\n",
    "x = neural_net_image_input((32, 32, 3))\n",
    "y = neural_net_label_input(10)\n",
    "keep_prob = neural_net_keep_prob_input()\n",
    "\n",
    "# Model\n",
    "logits = conv_net(x, keep_prob)\n",
    "\n",
    "# Name logits Tensor, so that is can be loaded from disk after training\n",
    "logits = tf.identity(logits, name='logits')\n",
    "\n",
    "# Loss and Optimizer\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
    "\n",
    "# Accuracy\n",
    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
    "\n",
    "tests.test_conv_net(conv_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Neural Network\n",
    "### Single Optimization\n",
    "Implement the function `train_neural_network` to do a single optimization.  The optimization should use `optimizer` to optimize in `session` with a `feed_dict` of the following:\n",
    "* `x` for image input\n",
    "* `y` for labels\n",
    "* `keep_prob` for keep probability for dropout\n",
    "\n",
    "This function will be called for each batch, so `tf.global_variables_initializer()` has already been called.\n",
    "\n",
    "Note: Nothing needs to be returned. This function is only optimizing the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
    "    \"\"\"\n",
    "    Optimize the session on a batch of images and labels\n",
    "    : session: Current TensorFlow session\n",
    "    : optimizer: TensorFlow optimizer function\n",
    "    : keep_probability: keep probability\n",
    "    : feature_batch: Batch of Numpy image data\n",
    "    : label_batch: Batch of Numpy label data\n",
    "    \"\"\"\n",
    "\n",
    "    session.run(optimizer, feed_dict={x: feature_batch, y: label_batch, keep_prob: keep_probability})\n",
    "\n",
    "\n",
    "tests.test_train_nn(train_neural_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show Stats\n",
    "Implement the function `print_stats` to print loss and validation accuracy.  Use the global variables `valid_features` and `valid_labels` to calculate validation accuracy.  Use a keep probability of `1.0` to calculate the loss and validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
    "    \"\"\"\n",
    "    Print information about loss and validation accuracy\n",
    "    : session: Current TensorFlow session\n",
    "    : feature_batch: Batch of Numpy image data\n",
    "    : label_batch: Batch of Numpy label data\n",
    "    : cost: TensorFlow cost function\n",
    "    : accuracy: TensorFlow accuracy function\n",
    "    \"\"\"\n",
    "\n",
    "    global valid_features\n",
    "    global valid_labels\n",
    "    loss = session.run(cost, feed_dict={x: feature_batch, y: label_batch, keep_prob: 1.0})\n",
    "    valid_acc = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: 1.0})\n",
    "    print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss,valid_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters\n",
    "Tune the following parameters:\n",
    "* Set `epochs` to the number of iterations until the network stops learning or start overfitting\n",
    "* Set `batch_size` to the highest number that your machine has memory for.  Most people set them to common sizes of memory:\n",
    " * 64\n",
    " * 128\n",
    " * 256\n",
    " * ...\n",
    "* Set `keep_probability` to the probability of keeping a node using dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Tune Parameters\n",
    "epochs = 25\n",
    "batch_size = 128\n",
    "keep_probability = 0.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train on a Single CIFAR-10 Batch\n",
    "Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy.  Once the final validation accuracy is 50% or greater, run the model on all the data in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking the Training on a Single Batch...\n",
      "Epoch  1, CIFAR-10 Batch 1:  Loss:     2.1756 Validation Accuracy: 0.180000\n",
      "Epoch  2, CIFAR-10 Batch 1:  Loss:     2.1032 Validation Accuracy: 0.218000\n",
      "Epoch  3, CIFAR-10 Batch 1:  Loss:     1.8785 Validation Accuracy: 0.326800\n",
      "Epoch  4, CIFAR-10 Batch 1:  Loss:     1.7946 Validation Accuracy: 0.362600\n",
      "Epoch  5, CIFAR-10 Batch 1:  Loss:     1.7090 Validation Accuracy: 0.376600\n",
      "Epoch  6, CIFAR-10 Batch 1:  Loss:     1.6672 Validation Accuracy: 0.392200\n",
      "Epoch  7, CIFAR-10 Batch 1:  Loss:     1.4595 Validation Accuracy: 0.427600\n",
      "Epoch  8, CIFAR-10 Batch 1:  Loss:     1.3384 Validation Accuracy: 0.457800\n",
      "Epoch  9, CIFAR-10 Batch 1:  Loss:     1.1936 Validation Accuracy: 0.476400\n",
      "Epoch 10, CIFAR-10 Batch 1:  Loss:     1.0825 Validation Accuracy: 0.489600\n",
      "Epoch 11, CIFAR-10 Batch 1:  Loss:     1.0186 Validation Accuracy: 0.514000\n",
      "Epoch 12, CIFAR-10 Batch 1:  Loss:     0.9345 Validation Accuracy: 0.519400\n",
      "Epoch 13, CIFAR-10 Batch 1:  Loss:     0.8484 Validation Accuracy: 0.531600\n",
      "Epoch 14, CIFAR-10 Batch 1:  Loss:     0.7733 Validation Accuracy: 0.541200\n",
      "Epoch 15, CIFAR-10 Batch 1:  Loss:     0.7036 Validation Accuracy: 0.544800\n",
      "Epoch 16, CIFAR-10 Batch 1:  Loss:     0.6434 Validation Accuracy: 0.538800\n",
      "Epoch 17, CIFAR-10 Batch 1:  Loss:     0.5594 Validation Accuracy: 0.550800\n",
      "Epoch 18, CIFAR-10 Batch 1:  Loss:     0.5775 Validation Accuracy: 0.528600\n",
      "Epoch 19, CIFAR-10 Batch 1:  Loss:     0.5959 Validation Accuracy: 0.527600\n",
      "Epoch 20, CIFAR-10 Batch 1:  Loss:     0.5006 Validation Accuracy: 0.528600\n",
      "Epoch 21, CIFAR-10 Batch 1:  Loss:     0.4228 Validation Accuracy: 0.539400\n",
      "Epoch 22, CIFAR-10 Batch 1:  Loss:     0.3589 Validation Accuracy: 0.536200\n",
      "Epoch 23, CIFAR-10 Batch 1:  Loss:     0.3141 Validation Accuracy: 0.555600\n",
      "Epoch 24, CIFAR-10 Batch 1:  Loss:     0.2578 Validation Accuracy: 0.558200\n",
      "Epoch 25, CIFAR-10 Batch 1:  Loss:     0.2683 Validation Accuracy: 0.550800\n"
     ]
    }
   ],
   "source": [
    "print('Checking the Training on a Single Batch...')\n",
    "with tf.Session() as sess:\n",
    "    # Initializing the variables\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # Training cycle\n",
    "    for epoch in range(epochs):\n",
    "        batch_i = 1\n",
    "        for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
    "            train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
    "        print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
    "        print_stats(sess, batch_features, batch_labels, cost, accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fully Train the Model\n",
    "Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "Epoch  1, CIFAR-10 Batch 1:  Loss:     2.2359 Validation Accuracy: 0.151000\n",
      "Epoch  1, CIFAR-10 Batch 2:  Loss:     2.0776 Validation Accuracy: 0.203800\n",
      "Epoch  1, CIFAR-10 Batch 3:  Loss:     1.6751 Validation Accuracy: 0.263600\n",
      "Epoch  1, CIFAR-10 Batch 4:  Loss:     1.7012 Validation Accuracy: 0.290200\n",
      "Epoch  1, CIFAR-10 Batch 5:  Loss:     1.7643 Validation Accuracy: 0.367800\n",
      "Epoch  2, CIFAR-10 Batch 1:  Loss:     1.8371 Validation Accuracy: 0.396200\n",
      "Epoch  2, CIFAR-10 Batch 2:  Loss:     1.7462 Validation Accuracy: 0.400000\n",
      "Epoch  2, CIFAR-10 Batch 3:  Loss:     1.2560 Validation Accuracy: 0.410600\n",
      "Epoch  2, CIFAR-10 Batch 4:  Loss:     1.5739 Validation Accuracy: 0.404400\n",
      "Epoch  2, CIFAR-10 Batch 5:  Loss:     1.6493 Validation Accuracy: 0.450000\n",
      "Epoch  3, CIFAR-10 Batch 1:  Loss:     1.5155 Validation Accuracy: 0.466000\n",
      "Epoch  3, CIFAR-10 Batch 2:  Loss:     1.3766 Validation Accuracy: 0.448400\n",
      "Epoch  3, CIFAR-10 Batch 3:  Loss:     1.0064 Validation Accuracy: 0.498400\n",
      "Epoch  3, CIFAR-10 Batch 4:  Loss:     1.3731 Validation Accuracy: 0.494400\n",
      "Epoch  3, CIFAR-10 Batch 5:  Loss:     1.4939 Validation Accuracy: 0.519400\n",
      "Epoch  4, CIFAR-10 Batch 1:  Loss:     1.2359 Validation Accuracy: 0.529200\n",
      "Epoch  4, CIFAR-10 Batch 2:  Loss:     1.0825 Validation Accuracy: 0.515600\n",
      "Epoch  4, CIFAR-10 Batch 3:  Loss:     0.8847 Validation Accuracy: 0.548800\n",
      "Epoch  4, CIFAR-10 Batch 4:  Loss:     1.2474 Validation Accuracy: 0.529000\n",
      "Epoch  4, CIFAR-10 Batch 5:  Loss:     1.3574 Validation Accuracy: 0.545200\n",
      "Epoch  5, CIFAR-10 Batch 1:  Loss:     1.1095 Validation Accuracy: 0.566000\n",
      "Epoch  5, CIFAR-10 Batch 2:  Loss:     1.0054 Validation Accuracy: 0.559200\n",
      "Epoch  5, CIFAR-10 Batch 3:  Loss:     0.8202 Validation Accuracy: 0.570400\n",
      "Epoch  5, CIFAR-10 Batch 4:  Loss:     1.0768 Validation Accuracy: 0.578800\n",
      "Epoch  5, CIFAR-10 Batch 5:  Loss:     1.2286 Validation Accuracy: 0.583600\n",
      "Epoch  6, CIFAR-10 Batch 1:  Loss:     0.9947 Validation Accuracy: 0.599400\n",
      "Epoch  6, CIFAR-10 Batch 2:  Loss:     0.9598 Validation Accuracy: 0.587000\n",
      "Epoch  6, CIFAR-10 Batch 3:  Loss:     0.6897 Validation Accuracy: 0.596000\n",
      "Epoch  6, CIFAR-10 Batch 4:  Loss:     0.9304 Validation Accuracy: 0.599000\n",
      "Epoch  6, CIFAR-10 Batch 5:  Loss:     1.0322 Validation Accuracy: 0.607000\n",
      "Epoch  7, CIFAR-10 Batch 1:  Loss:     0.8866 Validation Accuracy: 0.622000\n",
      "Epoch  7, CIFAR-10 Batch 2:  Loss:     0.7770 Validation Accuracy: 0.598200\n",
      "Epoch  7, CIFAR-10 Batch 3:  Loss:     0.5788 Validation Accuracy: 0.622800\n",
      "Epoch  7, CIFAR-10 Batch 4:  Loss:     0.7256 Validation Accuracy: 0.620400\n",
      "Epoch  7, CIFAR-10 Batch 5:  Loss:     0.9173 Validation Accuracy: 0.614800\n",
      "Epoch  8, CIFAR-10 Batch 1:  Loss:     0.7587 Validation Accuracy: 0.628400\n",
      "Epoch  8, CIFAR-10 Batch 2:  Loss:     0.7028 Validation Accuracy: 0.634400\n",
      "Epoch  8, CIFAR-10 Batch 3:  Loss:     0.4923 Validation Accuracy: 0.628800\n",
      "Epoch  8, CIFAR-10 Batch 4:  Loss:     0.5979 Validation Accuracy: 0.627800\n",
      "Epoch  8, CIFAR-10 Batch 5:  Loss:     0.8066 Validation Accuracy: 0.623400\n",
      "Epoch  9, CIFAR-10 Batch 1:  Loss:     0.6617 Validation Accuracy: 0.650600\n",
      "Epoch  9, CIFAR-10 Batch 2:  Loss:     0.5813 Validation Accuracy: 0.644000\n",
      "Epoch  9, CIFAR-10 Batch 3:  Loss:     0.4585 Validation Accuracy: 0.626800\n",
      "Epoch  9, CIFAR-10 Batch 4:  Loss:     0.5305 Validation Accuracy: 0.626600\n",
      "Epoch  9, CIFAR-10 Batch 5:  Loss:     0.6728 Validation Accuracy: 0.624200\n",
      "Epoch 10, CIFAR-10 Batch 1:  Loss:     0.5585 Validation Accuracy: 0.641600\n",
      "Epoch 10, CIFAR-10 Batch 2:  Loss:     0.5207 Validation Accuracy: 0.631000\n",
      "Epoch 10, CIFAR-10 Batch 3:  Loss:     0.3915 Validation Accuracy: 0.637800\n",
      "Epoch 10, CIFAR-10 Batch 4:  Loss:     0.4354 Validation Accuracy: 0.658600\n",
      "Epoch 10, CIFAR-10 Batch 5:  Loss:     0.6109 Validation Accuracy: 0.607800\n",
      "Epoch 11, CIFAR-10 Batch 1:  Loss:     0.4992 Validation Accuracy: 0.649600\n",
      "Epoch 11, CIFAR-10 Batch 2:  Loss:     0.4578 Validation Accuracy: 0.640800\n",
      "Epoch 11, CIFAR-10 Batch 3:  Loss:     0.3534 Validation Accuracy: 0.621000\n",
      "Epoch 11, CIFAR-10 Batch 4:  Loss:     0.4212 Validation Accuracy: 0.655600\n",
      "Epoch 11, CIFAR-10 Batch 5:  Loss:     0.5185 Validation Accuracy: 0.604000\n",
      "Epoch 12, CIFAR-10 Batch 1:  Loss:     0.4235 Validation Accuracy: 0.642600\n",
      "Epoch 12, CIFAR-10 Batch 2:  Loss:     0.4470 Validation Accuracy: 0.623800\n",
      "Epoch 12, CIFAR-10 Batch 3:  Loss:     0.3713 Validation Accuracy: 0.594600\n",
      "Epoch 12, CIFAR-10 Batch 4:  Loss:     0.3607 Validation Accuracy: 0.662800\n",
      "Epoch 12, CIFAR-10 Batch 5:  Loss:     0.4864 Validation Accuracy: 0.623600\n",
      "Epoch 13, CIFAR-10 Batch 1:  Loss:     0.3794 Validation Accuracy: 0.661000\n",
      "Epoch 13, CIFAR-10 Batch 2:  Loss:     0.4274 Validation Accuracy: 0.629000\n",
      "Epoch 13, CIFAR-10 Batch 3:  Loss:     0.2540 Validation Accuracy: 0.619800\n",
      "Epoch 13, CIFAR-10 Batch 4:  Loss:     0.3031 Validation Accuracy: 0.666000\n",
      "Epoch 13, CIFAR-10 Batch 5:  Loss:     0.3698 Validation Accuracy: 0.652200\n",
      "Epoch 14, CIFAR-10 Batch 1:  Loss:     0.3353 Validation Accuracy: 0.648000\n",
      "Epoch 14, CIFAR-10 Batch 2:  Loss:     0.3988 Validation Accuracy: 0.651600\n",
      "Epoch 14, CIFAR-10 Batch 3:  Loss:     0.1968 Validation Accuracy: 0.619000\n",
      "Epoch 14, CIFAR-10 Batch 4:  Loss:     0.2834 Validation Accuracy: 0.673000\n",
      "Epoch 14, CIFAR-10 Batch 5:  Loss:     0.3173 Validation Accuracy: 0.643600\n",
      "Epoch 15, CIFAR-10 Batch 1:  Loss:     0.2615 Validation Accuracy: 0.649800\n",
      "Epoch 15, CIFAR-10 Batch 2:  Loss:     0.3592 Validation Accuracy: 0.658400\n",
      "Epoch 15, CIFAR-10 Batch 3:  Loss:     0.1675 Validation Accuracy: 0.635800\n",
      "Epoch 15, CIFAR-10 Batch 4:  Loss:     0.2153 Validation Accuracy: 0.675200\n",
      "Epoch 15, CIFAR-10 Batch 5:  Loss:     0.2801 Validation Accuracy: 0.658800\n",
      "Epoch 16, CIFAR-10 Batch 1:  Loss:     0.2107 Validation Accuracy: 0.648200\n",
      "Epoch 16, CIFAR-10 Batch 2:  Loss:     0.3551 Validation Accuracy: 0.634600\n",
      "Epoch 16, CIFAR-10 Batch 3:  Loss:     0.1723 Validation Accuracy: 0.643800\n",
      "Epoch 16, CIFAR-10 Batch 4:  Loss:     0.2228 Validation Accuracy: 0.668200\n",
      "Epoch 16, CIFAR-10 Batch 5:  Loss:     0.3137 Validation Accuracy: 0.657000\n",
      "Epoch 17, CIFAR-10 Batch 1:  Loss:     0.2352 Validation Accuracy: 0.661600\n",
      "Epoch 17, CIFAR-10 Batch 2:  Loss:     0.3270 Validation Accuracy: 0.631600\n",
      "Epoch 17, CIFAR-10 Batch 3:  Loss:     0.1130 Validation Accuracy: 0.660000\n",
      "Epoch 17, CIFAR-10 Batch 4:  Loss:     0.1810 Validation Accuracy: 0.672600\n",
      "Epoch 17, CIFAR-10 Batch 5:  Loss:     0.2713 Validation Accuracy: 0.646000\n",
      "Epoch 18, CIFAR-10 Batch 1:  Loss:     0.2260 Validation Accuracy: 0.663200\n",
      "Epoch 18, CIFAR-10 Batch 2:  Loss:     0.2630 Validation Accuracy: 0.644400\n",
      "Epoch 18, CIFAR-10 Batch 3:  Loss:     0.1149 Validation Accuracy: 0.662400\n",
      "Epoch 18, CIFAR-10 Batch 4:  Loss:     0.2335 Validation Accuracy: 0.657000\n",
      "Epoch 18, CIFAR-10 Batch 5:  Loss:     0.1983 Validation Accuracy: 0.638600\n",
      "Epoch 19, CIFAR-10 Batch 1:  Loss:     0.2247 Validation Accuracy: 0.651000\n",
      "Epoch 19, CIFAR-10 Batch 2:  Loss:     0.2715 Validation Accuracy: 0.648200\n",
      "Epoch 19, CIFAR-10 Batch 3:  Loss:     0.0993 Validation Accuracy: 0.662200\n",
      "Epoch 19, CIFAR-10 Batch 4:  Loss:     0.2379 Validation Accuracy: 0.645600\n",
      "Epoch 19, CIFAR-10 Batch 5:  Loss:     0.1583 Validation Accuracy: 0.643600\n",
      "Epoch 20, CIFAR-10 Batch 1:  Loss:     0.1766 Validation Accuracy: 0.648800\n",
      "Epoch 20, CIFAR-10 Batch 2:  Loss:     0.2180 Validation Accuracy: 0.657000\n",
      "Epoch 20, CIFAR-10 Batch 3:  Loss:     0.0888 Validation Accuracy: 0.668200\n",
      "Epoch 20, CIFAR-10 Batch 4:  Loss:     0.1706 Validation Accuracy: 0.652600\n",
      "Epoch 20, CIFAR-10 Batch 5:  Loss:     0.1318 Validation Accuracy: 0.660200\n",
      "Epoch 21, CIFAR-10 Batch 1:  Loss:     0.1410 Validation Accuracy: 0.658000\n",
      "Epoch 21, CIFAR-10 Batch 2:  Loss:     0.2274 Validation Accuracy: 0.644200\n",
      "Epoch 21, CIFAR-10 Batch 3:  Loss:     0.0832 Validation Accuracy: 0.659000\n",
      "Epoch 21, CIFAR-10 Batch 4:  Loss:     0.1526 Validation Accuracy: 0.661600\n",
      "Epoch 21, CIFAR-10 Batch 5:  Loss:     0.1132 Validation Accuracy: 0.637400\n",
      "Epoch 22, CIFAR-10 Batch 1:  Loss:     0.1259 Validation Accuracy: 0.668800\n",
      "Epoch 22, CIFAR-10 Batch 2:  Loss:     0.2519 Validation Accuracy: 0.631000\n",
      "Epoch 22, CIFAR-10 Batch 3:  Loss:     0.0514 Validation Accuracy: 0.676600\n",
      "Epoch 22, CIFAR-10 Batch 4:  Loss:     0.1686 Validation Accuracy: 0.654600\n",
      "Epoch 22, CIFAR-10 Batch 5:  Loss:     0.1388 Validation Accuracy: 0.631800\n",
      "Epoch 23, CIFAR-10 Batch 1:  Loss:     0.1245 Validation Accuracy: 0.649800\n",
      "Epoch 23, CIFAR-10 Batch 2:  Loss:     0.2537 Validation Accuracy: 0.624400\n",
      "Epoch 23, CIFAR-10 Batch 3:  Loss:     0.0584 Validation Accuracy: 0.665200\n",
      "Epoch 23, CIFAR-10 Batch 4:  Loss:     0.1844 Validation Accuracy: 0.659800\n",
      "Epoch 23, CIFAR-10 Batch 5:  Loss:     0.1218 Validation Accuracy: 0.635200\n",
      "Epoch 24, CIFAR-10 Batch 1:  Loss:     0.1167 Validation Accuracy: 0.646800\n",
      "Epoch 24, CIFAR-10 Batch 2:  Loss:     0.1658 Validation Accuracy: 0.654200\n",
      "Epoch 24, CIFAR-10 Batch 3:  Loss:     0.0554 Validation Accuracy: 0.659000\n",
      "Epoch 24, CIFAR-10 Batch 4:  Loss:     0.1657 Validation Accuracy: 0.649200\n",
      "Epoch 24, CIFAR-10 Batch 5:  Loss:     0.1271 Validation Accuracy: 0.617400\n",
      "Epoch 25, CIFAR-10 Batch 1:  Loss:     0.1052 Validation Accuracy: 0.645000\n",
      "Epoch 25, CIFAR-10 Batch 2:  Loss:     0.1503 Validation Accuracy: 0.655600\n",
      "Epoch 25, CIFAR-10 Batch 3:  Loss:     0.0569 Validation Accuracy: 0.657800\n",
      "Epoch 25, CIFAR-10 Batch 4:  Loss:     0.1455 Validation Accuracy: 0.629400\n",
      "Epoch 25, CIFAR-10 Batch 5:  Loss:     0.1486 Validation Accuracy: 0.626600\n"
     ]
    }
   ],
   "source": [
    "save_model_path = './image_classification'\n",
    "\n",
    "print('Training...')\n",
    "with tf.Session() as sess:\n",
    "    # Initializing the variables\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # Training cycle\n",
    "    for epoch in range(epochs):\n",
    "        # Loop over all batches\n",
    "        n_batches = 5\n",
    "        for batch_i in range(1, n_batches + 1):\n",
    "            for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
    "                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
    "            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
    "            \n",
    "    # Save Model\n",
    "    saver = tf.train.Saver()\n",
    "    save_path = saver.save(sess, save_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checkpoint\n",
    "The model has been saved to disk.\n",
    "## Test Model\n",
    "Test your model against the test dataset.  This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./image_classification\n",
      "Testing Accuracy: 0.6223299050632911\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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45lFuiojIeLUE2ObuB47kSSds5/jv33SRA5h3l7e5twPQle52t9eWy4yeqGMR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\nN3i2HFpTc+T5NrfOB+CEpVnU9rTlKwAYHouo9MBodt1pnZE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FodqvvMr76EXubvvxZ/1s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\n4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lMQkyOidy0NZYd1ZZ+2cjHM5d320L0A3LvlwbSsY0losxaPj+7b05+WtQ+HcRV2Z1uy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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa443343c8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 319,
       "width": 355
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import tensorflow as tf\n",
    "import pickle\n",
    "import helper\n",
    "import random\n",
    "\n",
    "# Set batch size if not already set\n",
    "try:\n",
    "    if batch_size:\n",
    "        pass\n",
    "except NameError:\n",
    "    batch_size = 64\n",
    "\n",
    "save_model_path = './image_classification'\n",
    "n_samples = 4\n",
    "top_n_predictions = 3\n",
    "\n",
    "def test_model():\n",
    "    \"\"\"\n",
    "    Test the saved model against the test dataset\n",
    "    \"\"\"\n",
    "\n",
    "    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
    "    loaded_graph = tf.Graph()\n",
    "\n",
    "    with tf.Session(graph=loaded_graph) as sess:\n",
    "        # Load model\n",
    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
    "        loader.restore(sess, save_model_path)\n",
    "\n",
    "        # Get Tensors from loaded model\n",
    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
    "        \n",
    "        # Get accuracy in batches for memory limitations\n",
    "        test_batch_acc_total = 0\n",
    "        test_batch_count = 0\n",
    "        \n",
    "        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
    "            test_batch_acc_total += sess.run(\n",
    "                loaded_acc,\n",
    "                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
    "            test_batch_count += 1\n",
    "\n",
    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
    "\n",
    "        # Print Random Samples\n",
    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
    "        random_test_predictions = sess.run(\n",
    "            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
    "            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
    "        helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
    "\n",
    "\n",
    "test_model()"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Why 50-80% Accuracy?\n",
    "You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN.  Pure guessing would get you 10% accuracy. However, you might notice people are getting scores [well above 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130).  That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.\n",
    "## Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook.  Save the notebook file as \"dlnd_image_classification.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\".  Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
   ]
  }
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