class MnistTest(unittest.TestCase): def setUp(self): self.mock = Net() def tearDown(self): self.mock.close() def test_mnist(self): mock = self.mock #= Deco() make_net(mock) test(mock, self)
class Test(unittest.TestCase): def setUp(self): self.mock = Net() def tearDown(self): self.mock.close() def test_add_layer(self): mock = self.mock mock.add_layer(3) def test_add_layer_shape(self): mock = self.mock mock.add_layer(shape=[None, 12 * 12, 5]) def test_for_features_net(self): mock = self.mock width = 12 size = 3 mock.add_layer(shape=[None, width * width * size]) mock.add_layer(100) mock.add_layer(width * width) def test_reshape(self): width = 12 size = 40 features = np.random.rand(width, width, size) features = features.reshape([width * width * size]) def test_tf_reduce_mean_of_boolean_list(self): x = [True, True, True, False] try: with tf.Session() as s: acc = tf.reduce_mean(tf.cast(x, tf.float32)) self.assertEquals(.75, s.run(acc)) finally: s.close() def test_acc(self): out = np.zeros([5, 3]) target = np.zeros([5, 3]) out[:5, 0] = 1 target[:4, 0] = 1 target[4, 1] = 1 try: with tf.Session() as s: acc = self.mock.get_acc(out, target) self.assertAlmostEquals(.8, acc) finally: s.close()
class XorTest(unittest.TestCase): def setUp(self): self.mock = Net() def tearDown(self): self.mock.close() def test_xor(self): make_net(self.mock) mock = self.mock first_loss = mock.run(mock.loss) mock.fit() trained_loss = mock.run(mock.loss) self.assertTrue(first_loss > trained_loss) self.assertGreater(first_loss, trained_loss) self.assertTrue(.005 > trained_loss, msg='trained_loss=%3.5f' % trained_loss)
class Test(ut.TestCase): def setUp(self): ut.TestCase.setUp(self) self.mock = Net() def tearDown(self): ut.TestCase.tearDown(self) self.mock.close() # width == height def test_add_conv2d_square(self): mock = self.mock width = 10 height = width input_channel_size = 3 # 10 * 10 * 3 # width = 10 = height # input channel size = 3 mock.add_layer(width * height * input_channel_size) filter_size = 5 output_channel_size = 64 mock.add_conv2d(filter_size, output_channel_size, width, height, input_channel_size) def test_make_auto_normal_layer(self): mock = self.mock width = 28 height = width input_channel_size = 5 output_channel_size = 7 filter_size = 5 mock.add_conv2d(filter_size, output_channel_size, width, height, input_channel_size) self.assertEquals([None, width * height * input_channel_size], mock.layers[0].get_shape().as_list()) def test_add_conv2d_on_conv2d(self): mock = self.mock width = 28 height = width input_channel_size = 5 output_channel_size = 7 filter_size = 5 mock.add_conv2d(filter_size, output_channel_size, width, height, input_channel_size) mock.add_conv2d(filter_size, output_channel_size) mock.add_layer(5) def test_pool(self): mock = self.mock width = 28 height = width input_channel_size = 5 output_channel_size = 7 filter_size = 5 mock.add_conv2d(filter_size, output_channel_size, width, height, input_channel_size) mock.add_pool() mock.add_conv2d(filter_size, output_channel_size) mock.add_pool() mock.add_layer(7) def test_get_width_height_for_convolution_layer(self): mock = self.mock width = 28 height = width input_channel_size = 5 output_channel_size = 7 filter_size = 5 mock.add_conv2d(filter_size, output_channel_size, width, height, input_channel_size) width_, height_, output_channel_size_ = mock.get_width_height_input_channel_size( ) self.assertEquals([width, height, output_channel_size], [width_, height_, output_channel_size_]) def test_add_second_conv2d_layer_without_width_height_input_channel_size( self): mock = self.mock width = 28 height = width input_channel_size = 5 output_channel_size = 7 filter_size = 5 mock.add_conv2d(filter_size, output_channel_size, width, height, input_channel_size) mock.add_conv2d(filter_size, output_channel_size)