problem_unittests.py

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()