From e4a4c5d32b631f9091f654780ba2879cc1d10990 Mon Sep 17 00:00:00 2001 From: spike Date: Mon, 6 Mar 2017 16:11:08 +0100 Subject: [PATCH] wip --- dlnd_image_classification.ipynb | 1089 +++++++++++++++++++++++++++++++ helper.py | 165 +++++ problem_unittests.py | 199 ++++++ 3 files changed, 1453 insertions(+) create mode 100644 dlnd_image_classification.ipynb create mode 100644 helper.py create mode 100644 problem_unittests.py diff --git a/dlnd_image_classification.ipynb b/dlnd_image_classification.ipynb new file mode 100644 index 0000000..47b9200 --- /dev/null +++ b/dlnd_image_classification.ipynb @@ -0,0 +1,1089 @@ +{ + "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": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All files found!\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", + "\"\"\"\n", + "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", + "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('cifar-10-python.tar.gz'):\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", + " 'cifar-10-python.tar.gz',\n", + " pbar.hook)\n", + "\n", + "if not isdir(cifar10_dataset_folder_path):\n", + " with tarfile.open('cifar-10-python.tar.gz') 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": 27, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Stats of batch 1:\n", + "Samples: 10000\n", + "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n", + "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n", + "\n", + "Example of Image 32:\n", + "Image - Min Value: 0 Max Value: 255\n", + "Image - Shape: (32, 32, 3)\n", + "Label - Label Id: 1 Name: automobile\n" + ] + }, + { + "data": { + "image/png": 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YeIFwoi+/Pk2qqDPMJdBBZfK//m6FBtguQDcjDSBeigNCwKkYr2YHhr3VUqDs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/Eua69N84sRMok0n+xpghRfu57QHGAM1/cJit6Kn+DzMc9Teqe5A1a2hKb3BMAw7rFOHUC5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\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pNZc5gA/vdC7U/SYOp6vXuYyKUCh62adxdsBi/k4Azv09n3cCWIfbhN8lG+C2RVgSfunG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\n7S3wZjsMiAh4EU4AaCyC2zuA9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ne7C6/CW8DNgEz+QJjixHNPHyailljtu83N6ecFrWgc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\nhwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 351, + "width": 353 + } + }, + "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 = 1\n", + "sample_id = 32\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": 6, + "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", + " return (x - x.min()) / (x.max() - x.min())\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], + "source": [ + "encodings = {}\n", + "for i in range(10):\n", + " v = np.zeros(10)\n", + " v[i] = 1\n", + " encodings[i] = v\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", + " assert np.max(x) < 10 and np.min(x) >= 0, 'label must be between 0 and 9'\n", + " encoded = np.array(list(map(lambda v: encodings[v],x)))\n", + " return encoded\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL\n", + "\"\"\"\n", + "# 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": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL\n", + "\"\"\"\n", + "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", + "If you're finding it hard to dedicate enough time for this course a 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 [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) to build each layer, except \"Convolutional & Max Pooling\" layer. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.\n", + "\n", + "If you would like to get the most of this course, try to solve all the problems without TF Layers. Let's begin!\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": 53, + "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 bach of image input\n", + " : image_shape: Shape of the images\n", + " : return: Tensor for image input.\n", + " \"\"\"\n", + " # shape [None, 32, 32, 3]\n", + " return tf.placeholder(tf.float32, [None] + list(image_shape), name='x')\n", + "\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", + " return tf.placeholder(tf.float32, [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", + " # TODO: Implement Function\n", + " return tf.placeholder(tf.float32, name='keep_prob')\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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. You're free to use any TensorFlow package for all the other layers." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false, + "scrolled": 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_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", + " w_conv = tf.Variable(tf.truncated_normal((conv_ksize[0], # Kernel height\n", + " conv_ksize[1], # Kernel width\n", + " x_tensor.get_shape()[3].value, # Input channels\n", + " conv_num_outputs))) # output depth\n", + " bias_conv = tf.Variable(tf.zeros(conv_num_outputs))\n", + " \n", + " padding = 'SAME'\n", + " \n", + " \n", + " # DEBUG\n", + " #print('conv ksize: ', conv_ksize)\n", + " #print('conv strides: ',conv_strides)\n", + " #print('pool ksize: ', pool_ksize)\n", + " #print('pool strides: ',pool_strides)\n", + " #print('conv_depth: ', conv_num_outputs)\n", + " #print('x_tensor :',x_tensor)\n", + " #print('conv weights :', w_conv) \n", + " # DEBUG\n", + " \n", + " \n", + " # Apply conv to x_tensor\n", + " cv1 = tf.nn.conv2d(x_tensor,\n", + " w_conv,\n", + " [1, conv_strides[0], conv_strides[1], 1],\n", + " padding=padding\n", + " )\n", + " # add bias\n", + " cv1 = tf.nn.bias_add(cv1, bias_conv)\n", + " \n", + " # Apply relu\n", + " cv1 = tf.nn.relu(cv1)\n", + " \n", + " # Apply Maxpooling\n", + " cv1 = tf.nn.max_pool(cv1,\n", + " ksize=[1,pool_ksize[0], pool_ksize[1], 1],\n", + " strides=[1, pool_strides[0], pool_strides[1], 1],\n", + " padding=padding\n", + " )\n", + " \n", + " \n", + " \n", + " #print('conv layer :', cv1.get_shape())\n", + " return cv1 \n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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*). You can 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." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tests Passed\n" + ] + } + ], + "source": [ + "\n", + "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", + " flat_size = np.multiply.reduce(x_tensor.get_shape()[1:].as_list())\n", + " return tf.reshape(x_tensor, [-1, flat_size])\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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*). You can 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." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "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", + " full_w = tf.Variable(tf.truncated_normal((x_tensor.get_shape()[1].value, num_outputs)))\n", + " full_b = tf.Variable(tf.zeros(num_outputs))\n", + " \n", + " full = tf.add(tf.matmul(x_tensor, full_w), full_b)\n", + " full = tf.nn.relu(full)\n", + " return full\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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*). You can 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.\n", + "\n", + "Note: Activation, softmax, or cross entropy shouldn't be applied to this." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "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", + " out_w = tf.Variable(tf.truncated_normal((x_tensor.get_shape()[1].value, num_outputs)))\n", + " out_b = tf.Variable(tf.zeros(num_outputs))\n", + " \n", + " out = tf.add(tf.matmul(x_tensor, out_w), out_b)\n", + " return out\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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": 147, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------\n", + "Tensor(\"MaxPool:0\", shape=(?, 8, 8, 20), dtype=float32)\n", + "Tensor(\"MaxPool_1:0\", shape=(?, 2, 2, 50), dtype=float32)\n", + "Tensor(\"Reshape:0\", shape=(?, 200), dtype=float32)\n", + "Tensor(\"Relu_2:0\", shape=(?, 200), dtype=float32)\n", + "Tensor(\"Relu_3:0\", shape=(?, 10), dtype=float32)\n", + "Tensor(\"Add_2:0\", shape=(?, 10), dtype=float32)\n", + "--------------\n", + "Tensor(\"MaxPool_2:0\", shape=(?, 8, 8, 20), dtype=float32)\n", + "Tensor(\"MaxPool_3:0\", shape=(?, 2, 2, 50), dtype=float32)\n", + "Tensor(\"Reshape_4:0\", shape=(?, 200), dtype=float32)\n", + "Tensor(\"Relu_6:0\", shape=(?, 200), dtype=float32)\n", + "Tensor(\"Relu_7:0\", shape=(?, 10), dtype=float32)\n", + "Tensor(\"Add_5:0\", shape=(?, 10), dtype=float32)\n", + "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", + " # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n", + " # Play around with different number of outputs, kernel size and stride\n", + " # Function Definition from Above:\n", + " # conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n", + " \n", + " conv1_hyper_params = {\n", + " 'conv_num_outputs': 20,\n", + " 'conv_ksize': (2,2),\n", + " 'conv_strides': (2,2),\n", + " 'pool_ksize': (2,2),\n", + " 'pool_strides': (2,2)\n", + " }\n", + " \n", + " conv2_hyper_params = {\n", + " 'conv_num_outputs': 50,\n", + " 'conv_ksize': (2,2),\n", + " 'conv_strides': (2,2),\n", + " 'pool_ksize': (2,2),\n", + " 'pool_strides': (2,2)\n", + " }\n", + " \n", + " conv1 = conv2d_maxpool(x, **conv1_hyper_params)\n", + " conv2 = conv2d_maxpool(conv1, **conv2_hyper_params)\n", + " print('--------------')\n", + " \n", + " # TODO: Apply a Flatten Layer\n", + " # Function Definition from Above:\n", + " # flatten(x_tensor)\n", + " \n", + " flat = flatten(conv2)\n", + " \n", + " \n", + " \n", + " # TODO: 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", + " \n", + " full1 = fully_conn(flat, 200)\n", + " full2 = fully_conn(flat, 10)\n", + " \n", + " \n", + " \n", + " # TODO: Apply an Output Layer\n", + " # Set this to the number of classes\n", + " # Function Definition from Above:\n", + " # output(x_tensor, num_outputs)\n", + " out = output(full2, 10)\n", + " \n", + " \n", + " # DEBUG\n", + " print(conv1)\n", + " print(conv2)\n", + " print(flat)\n", + " print(full1)\n", + " print(full2)\n", + " print(out)\n", + " # DEBUG\n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " return out\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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": 148, + "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", + " session.run([optimizer], feed_dict={x: feature_batch,\n", + " y: label_batch,\n", + " keep_prob: keep_probability\n", + " })\n", + "\n", + "\n", + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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": 155, + "metadata": { + "collapsed": false + }, + "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", + " feed = {\n", + " x: feature_batch,\n", + " y: label_batch,\n", + " keep_prob: 1\n", + " }\n", + " \n", + " loss = session.run(cost, feed)\n", + " accuracy = session.run(accuracy, feed)\n", + " \n", + " print('Loss: {:>3} - Accuracy: {:>2}'.format(loss,accuracy))\n", + " \n", + " " + ] + }, + { + "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": 159, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# TODO: Tune Parameters\n", + "epochs = 10\n", + "batch_size = 1024\n", + "keep_probability = 0.75" + ] + }, + { + "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": 160, + "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: 214.1365966796875 - Accuracy: 0.11138613522052765\n", + "Epoch 2, CIFAR-10 Batch 1: Loss: 153.2625274658203 - Accuracy: 0.11386138945817947\n", + "Epoch 3, CIFAR-10 Batch 1: Loss: 118.0731201171875 - Accuracy: 0.13242574036121368\n", + "Epoch 4, CIFAR-10 Batch 1: Loss: 102.2635498046875 - Accuracy: 0.1336633712053299\n", + "Epoch 5, CIFAR-10 Batch 1: Loss: 86.91572570800781 - Accuracy: 0.12128712981939316\n", + "Epoch 6, CIFAR-10 Batch 1: Loss: 74.41956329345703 - Accuracy: 0.125\n", + "Epoch 7, CIFAR-10 Batch 1: Loss: 62.642093658447266 - Accuracy: 0.12376237660646439\n", + "Epoch 8, CIFAR-10 Batch 1: Loss: 51.168819427490234 - Accuracy: 0.13985148072242737\n", + "Epoch 9, CIFAR-10 Batch 1: Loss: 39.2636604309082 - Accuracy: 0.13861386477947235\n", + "Epoch 10, CIFAR-10 Batch 1: Loss: 26.23513412475586 - Accuracy: 0.13861386477947235\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL\n", + "\"\"\"\n", + "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": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL\n", + "\"\"\"\n", + "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": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "DON'T MODIFY ANYTHING IN THIS CELL\n", + "\"\"\"\n", + "%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_training.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 train_feature_batch, train_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: train_feature_batch, loaded_y: train_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()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why 50-70% 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 70%](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." + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/helper.py b/helper.py new file mode 100644 index 0000000..fc971f9 --- /dev/null +++ b/helper.py @@ -0,0 +1,165 @@ +import pickle +import numpy as np +import matplotlib.pyplot as plt +from sklearn.preprocessing import LabelBinarizer + + +def _load_label_names(): + """ + Load the label names from file + """ + return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] + + +def load_cfar10_batch(cifar10_dataset_folder_path, batch_id): + """ + Load a batch of the dataset + """ + with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file: + batch = pickle.load(file, encoding='latin1') + + features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1) + labels = batch['labels'] + + return features, labels + + +def display_stats(cifar10_dataset_folder_path, batch_id, sample_id): + """ + Display Stats of the the dataset + """ + batch_ids = list(range(1, 6)) + + if batch_id not in batch_ids: + print('Batch Id out of Range. Possible Batch Ids: {}'.format(batch_ids)) + return None + + features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_id) + + if not (0 <= sample_id < len(features)): + print('{} samples in batch {}. {} is out of range.'.format(len(features), batch_id, sample_id)) + return None + + print('\nStats of batch {}:'.format(batch_id)) + print('Samples: {}'.format(len(features))) + print('Label Counts: {}'.format(dict(zip(*np.unique(labels, return_counts=True))))) + print('First 20 Labels: {}'.format(labels[:20])) + + sample_image = features[sample_id] + sample_label = labels[sample_id] + label_names = _load_label_names() + + print('\nExample of Image {}:'.format(sample_id)) + print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max())) + print('Image - Shape: {}'.format(sample_image.shape)) + print('Label - Label Id: {} Name: {}'.format(sample_label, label_names[sample_label])) + plt.axis('off') + plt.imshow(sample_image) + + +def _preprocess_and_save(normalize, one_hot_encode, features, labels, filename): + """ + Preprocess data and save it to file + """ + features = normalize(features) + labels = one_hot_encode(labels) + + pickle.dump((features, labels), open(filename, 'wb')) + + +def preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode): + """ + Preprocess Training and Validation Data + """ + n_batches = 5 + valid_features = [] + valid_labels = [] + + for batch_i in range(1, n_batches + 1): + features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i) + validation_count = int(len(features) * 0.1) + + # Prprocess and save a batch of training data + _preprocess_and_save( + normalize, + one_hot_encode, + features[:-validation_count], + labels[:-validation_count], + 'preprocess_batch_' + str(batch_i) + '.p') + + # Use a portion of training batch for validation + valid_features.extend(features[-validation_count:]) + valid_labels.extend(labels[-validation_count:]) + + # Preprocess and Save all validation data + _preprocess_and_save( + normalize, + one_hot_encode, + np.array(valid_features), + np.array(valid_labels), + 'preprocess_validation.p') + + with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file: + batch = pickle.load(file, encoding='latin1') + + # load the training data + test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1) + test_labels = batch['labels'] + + # Preprocess and Save all training data + _preprocess_and_save( + normalize, + one_hot_encode, + np.array(test_features), + np.array(test_labels), + 'preprocess_training.p') + + +def batch_features_labels(features, labels, batch_size): + """ + Split features and labels into batches + """ + for start in range(0, len(features), batch_size): + end = min(start + batch_size, len(features)) + yield features[start:end], labels[start:end] + + +def load_preprocess_training_batch(batch_id, batch_size): + """ + Load the Preprocessed Training data and return them in batches of or less + """ + filename = 'preprocess_batch_' + str(batch_id) + '.p' + features, labels = pickle.load(open(filename, mode='rb')) + + # Return the training data in batches of size or less + return batch_features_labels(features, labels, batch_size) + + +def display_image_predictions(features, labels, predictions): + n_classes = 10 + label_names = _load_label_names() + label_binarizer = LabelBinarizer() + label_binarizer.fit(range(n_classes)) + label_ids = label_binarizer.inverse_transform(np.array(labels)) + + fig, axies = plt.subplots(nrows=4, ncols=2) + fig.tight_layout() + fig.suptitle('Softmax Predictions', fontsize=20, y=1.1) + + n_predictions = 3 + margin = 0.05 + ind = np.arange(n_predictions) + width = (1. - 2. * margin) / n_predictions + + for image_i, (feature, label_id, pred_indicies, pred_values) in enumerate(zip(features, label_ids, predictions.indices, predictions.values)): + pred_names = [label_names[pred_i] for pred_i in pred_indicies] + correct_name = label_names[label_id] + + axies[image_i][0].imshow(feature*255) + axies[image_i][0].set_title(correct_name) + axies[image_i][0].set_axis_off() + + axies[image_i][1].barh(ind + margin, pred_values[::-1], width) + axies[image_i][1].set_yticks(ind + margin) + axies[image_i][1].set_yticklabels(pred_names[::-1]) + axies[image_i][1].set_xticks([0, 0.5, 1.0]) diff --git a/problem_unittests.py b/problem_unittests.py new file mode 100644 index 0000000..35e9f5b --- /dev/null +++ b/problem_unittests.py @@ -0,0 +1,199 @@ +import os +import numpy as np +import tensorflow as tf +import random +from unittest.mock import MagicMock + + +def _print_success_message(): + return print('Tests Passed') + + +def test_folder_path(cifar10_dataset_folder_path): + assert cifar10_dataset_folder_path is not None,\ + 'Cifar-10 data folder not set.' + assert cifar10_dataset_folder_path[-1] != '/',\ + 'The "/" shouldn\'t be added to the end of the path.' + assert os.path.exists(cifar10_dataset_folder_path),\ + 'Path not found.' + assert os.path.isdir(cifar10_dataset_folder_path),\ + '{} is not a folder.'.format(os.path.basename(cifar10_dataset_folder_path)) + + train_files = [cifar10_dataset_folder_path + '/data_batch_' + str(batch_id) for batch_id in range(1, 6)] + other_files = [cifar10_dataset_folder_path + '/batches.meta', cifar10_dataset_folder_path + '/test_batch'] + missing_files = [path for path in train_files + other_files if not os.path.exists(path)] + + assert not missing_files,\ + 'Missing files in directory: {}'.format(missing_files) + + print('All files found!') + + +def test_normalize(normalize): + test_shape = (np.random.choice(range(1000)), 32, 32, 3) + test_numbers = np.random.choice(range(256), test_shape) + normalize_out = normalize(test_numbers) + + assert type(normalize_out).__module__ == np.__name__,\ + 'Not Numpy Object' + + assert normalize_out.shape == test_shape,\ + 'Incorrect Shape. {} shape found'.format(normalize_out.shape) + + assert normalize_out.max() <= 1 and normalize_out.min() >= 0,\ + 'Incorect Range. {} to {} found'.format(normalize_out.min(), normalize_out.max()) + + _print_success_message() + + +def test_one_hot_encode(one_hot_encode): + test_shape = np.random.choice(range(1000)) + test_numbers = np.random.choice(range(10), test_shape) + one_hot_out = one_hot_encode(test_numbers) + + assert type(one_hot_out).__module__ == np.__name__,\ + 'Not Numpy Object' + + assert one_hot_out.shape == (test_shape, 10),\ + 'Incorrect Shape. {} shape found'.format(one_hot_out.shape) + + n_encode_tests = 5 + test_pairs = list(zip(test_numbers, one_hot_out)) + test_indices = np.random.choice(len(test_numbers), n_encode_tests) + labels = [test_pairs[test_i][0] for test_i in test_indices] + enc_labels = np.array([test_pairs[test_i][1] for test_i in test_indices]) + new_enc_labels = one_hot_encode(labels) + + assert np.array_equal(enc_labels, new_enc_labels),\ + 'Encodings returned different results for the same numbers.\n' \ + 'For the first call it returned:\n' \ + '{}\n' \ + 'For the second call it returned\n' \ + '{}\n' \ + 'Make sure you save the map of labels to encodings outside of the function.'.format(enc_labels, new_enc_labels) + + _print_success_message() + + +def test_nn_image_inputs(neural_net_image_input): + image_shape = (32, 32, 3) + nn_inputs_out_x = neural_net_image_input(image_shape) + + assert nn_inputs_out_x.get_shape().as_list() == [None, image_shape[0], image_shape[1], image_shape[2]],\ + 'Incorrect Image Shape. Found {} shape'.format(nn_inputs_out_x.get_shape().as_list()) + + assert nn_inputs_out_x.op.type == 'Placeholder',\ + 'Incorrect Image Type. Found {} type'.format(nn_inputs_out_x.op.type) + + assert nn_inputs_out_x.name == 'x:0', \ + 'Incorrect Name. Found {}'.format(nn_inputs_out_x.name) + + print('Image Input Tests Passed.') + + +def test_nn_label_inputs(neural_net_label_input): + n_classes = 10 + nn_inputs_out_y = neural_net_label_input(n_classes) + + assert nn_inputs_out_y.get_shape().as_list() == [None, n_classes],\ + 'Incorrect Label Shape. Found {} shape'.format(nn_inputs_out_y.get_shape().as_list()) + + assert nn_inputs_out_y.op.type == 'Placeholder',\ + 'Incorrect Label Type. Found {} type'.format(nn_inputs_out_y.op.type) + + assert nn_inputs_out_y.name == 'y:0', \ + 'Incorrect Name. Found {}'.format(nn_inputs_out_y.name) + + print('Label Input Tests Passed.') + + +def test_nn_keep_prob_inputs(neural_net_keep_prob_input): + nn_inputs_out_k = neural_net_keep_prob_input() + + assert nn_inputs_out_k.get_shape().ndims is None,\ + 'Too many dimensions found for keep prob. Found {} dimensions. It should be a scalar (0-Dimension Tensor).'.format(nn_inputs_out_k.get_shape().ndims) + + assert nn_inputs_out_k.op.type == 'Placeholder',\ + 'Incorrect keep prob Type. Found {} type'.format(nn_inputs_out_k.op.type) + + assert nn_inputs_out_k.name == 'keep_prob:0', \ + 'Incorrect Name. Found {}'.format(nn_inputs_out_k.name) + + print('Keep Prob Tests Passed.') + + +def test_con_pool(conv2d_maxpool): + test_x = tf.placeholder(tf.float32, [None, 32, 32, 5]) + test_num_outputs = 10 + test_con_k = (2, 2) + test_con_s = (4, 4) + test_pool_k = (2, 2) + test_pool_s = (2, 2) + + conv2d_maxpool_out = conv2d_maxpool(test_x, test_num_outputs, test_con_k, test_con_s, test_pool_k, test_pool_s) + + assert conv2d_maxpool_out.get_shape().as_list() == [None, 4, 4, 10],\ + 'Incorrect Shape. Found {} shape'.format(conv2d_maxpool_out.get_shape().as_list()) + + _print_success_message() + + +def test_flatten(flatten): + test_x = tf.placeholder(tf.float32, [None, 10, 30, 6]) + flat_out = flatten(test_x) + + assert flat_out.get_shape().as_list() == [None, 10*30*6],\ + 'Incorrect Shape. Found {} shape'.format(flat_out.get_shape().as_list()) + + _print_success_message() + + +def test_fully_conn(fully_conn): + test_x = tf.placeholder(tf.float32, [None, 128]) + test_num_outputs = 40 + + fc_out = fully_conn(test_x, test_num_outputs) + + assert fc_out.get_shape().as_list() == [None, 40],\ + 'Incorrect Shape. Found {} shape'.format(fc_out.get_shape().as_list()) + + _print_success_message() + + +def test_output(output): + test_x = tf.placeholder(tf.float32, [None, 128]) + test_num_outputs = 40 + + output_out = output(test_x, test_num_outputs) + + assert output_out.get_shape().as_list() == [None, 40],\ + 'Incorrect Shape. Found {} shape'.format(output_out.get_shape().as_list()) + + _print_success_message() + + +def test_conv_net(conv_net): + test_x = tf.placeholder(tf.float32, [None, 32, 32, 3]) + test_k = tf.placeholder(tf.float32) + + logits_out = conv_net(test_x, test_k) + + assert logits_out.get_shape().as_list() == [None, 10],\ + 'Incorrect Model Output. Found {}'.format(logits_out.get_shape().as_list()) + + print('Neural Network Built!') + + +def test_train_nn(train_neural_network): + mock_session = tf.Session() + test_x = np.random.rand(128, 32, 32, 3) + test_y = np.random.rand(128, 10) + test_k = np.random.rand(1) + test_optimizer = tf.train.AdamOptimizer() + + mock_session.run = MagicMock() + train_neural_network(mock_session, test_optimizer, test_k, test_x, test_y) + + assert mock_session.run.called, 'Session not used' + + _print_success_message()