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)
