def test15_deconv_pool_layer_2d_print_layer(self,
mock_batch_normalization_train,
mock_batch_normalization_test,
mock_activate,
mock_unbroadcast):
mock_unbroadcast.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_batch_normalization_train.return_value = (self.output_train, 1, 1,
1, 1)
mock_batch_normalization_test.return_value = self.output_test
self.layer = dl(input=self.input_tensor,
id=self.deconv_pool_layer_2d_name,
input_shape=self.input_shape,
output_shape=self.input_shape,
nkerns=10,
verbose=self.verbose,
input_params=self.input_params,
batch_norm=True)
self.attributes = self.layer._graph_attributes()
self.layer.output_shape = self.input_shape
self.layer.origin = "input"
self.layer.destination = "classifier"
self.layer.batch_norm = False
self.layer.filter_shape = (1, 1)
self.layer.input_shape = (1, 1, 10, 10)
self.layer.poolsize = (1, 1)
self.layer.stride = (1, 1)
self.layer.print_layer(prefix=" ", nest=False, last=False)
self.assertTrue(len(self.layer.prefix) > 0)
def test13_deconv_pool_layer_2d_ip_vals(self,
mock_batch_normalization_train,
mock_batch_normalization_test,
mock_activate, mock_unbroadcast):
mock_unbroadcast.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_batch_normalization_train.return_value = (self.output_train, 1, 1,
1, 1)
mock_batch_normalization_test.return_value = self.output_test
self.deconv_pool_layer_2d = dl(input=self.input_tensor,
id=self.deconv_pool_layer_2d_name,
input_shape=self.input_shape,
output_shape=self.input_shape,
nkerns=10,
verbose=self.verbose,
input_params=self.input_params,
batch_norm=True)
self.assertEqual(self.deconv_pool_layer_2d.id,
self.deconv_pool_layer_2d_name)
self.assertEqual(self.deconv_pool_layer_2d.output_shape,
self.input_shape)
self.assertTrue(
numpy.allclose(self.deconv_pool_layer_2d.output,
self.input_ndarray))
self.assertTrue(
numpy.allclose(self.deconv_pool_layer_2d.inference,
self.input_ndarray))
def test14_deconv_pool_layer_2d_pool_size_mismatch_exception(
self, mock_batch_normalization_train,
mock_batch_normalization_test, mock_activate, mock_unbroadcast):
mock_unbroadcast.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_batch_normalization_train.return_value = (self.output_train, 1, 1,
1, 1)
mock_batch_normalization_test.return_value = self.output_test
try:
self.deconv_pool_layer_2d = dl(input=self.input_tensor,
id=self.deconv_pool_layer_2d_name,
input_shape=self.input_shape,
output_shape=self.input_shape,
nkerns=10,
verbose=self.verbose,
input_params=self.input_params,
poolsize=(2, 2),
batch_norm=True)
self.assertEqual(True, False)
except Exception, c:
self.assertEqual(c.message, self.pool_size_mismatch_exception_msg)
def test15_deconv_pool_layer_2d_activation_tuple_exception(
self, mock_batch_normalization_train,
mock_batch_normalization_test, mock_activate, mock_unbroadcast):
mock_unbroadcast.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_batch_normalization_train.return_value = (self.output_train, 1, 1,
1, 1)
mock_batch_normalization_test.return_value = self.output_test
try:
self.deconv_pool_layer_2d = dl(input=self.input_tensor,
id=self.deconv_pool_layer_2d_name,
input_shape=self.input_shape,
output_shape=self.input_shape,
nkerns=10,
verbose=self.verbose,
input_params=self.input_params,
batch_norm=False,
activation=('maxout', 'RelU'))
self.assertEqual(True, False)
except Exception, c:
print(c.message)
self.assertEqual(c.message, self.activation_tuple_exception_msg)