def test_network_invocation_with_logit_output(self):
"""Validate that the logit outputs are correct."""
input_width = 512
num_classes = 10
test_object = classification.Classification(
input_width=input_width, num_classes=num_classes, output='predictions')
logit_object = classification.Classification(
input_width=input_width, num_classes=num_classes, output='logits')
logit_object.set_weights(test_object.get_weights())
# Create a 2-dimensional input (the first dimension is implicit).
cls_data = tf.keras.Input(shape=(input_width,), dtype=tf.float32)
output = test_object(cls_data)
logit_output = logit_object(cls_data)
model = tf.keras.Model(cls_data, output)
logits_model = tf.keras.Model(cls_data, logit_output)
batch_size = 3
input_data = 10 * np.random.random_sample((batch_size, input_width))
outputs = model.predict(input_data)
logits = logits_model.predict(input_data)
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, num_classes)
self.assertEqual(expected_output_shape, outputs.shape)
self.assertEqual(expected_output_shape, logits.shape)
# Ensure that the logits, when softmaxed, create the outputs.
input_tensor = tf.keras.Input(expected_output_shape[1:])
output_tensor = tf.keras.layers.Activation(tf.nn.log_softmax)(input_tensor)
softmax_model = tf.keras.Model(input_tensor, output_tensor)
calculated_softmax = softmax_model.predict(logits)
self.assertAllClose(outputs, calculated_softmax)
def test_network_invocation_with_internal_and_external_logits(
self, num_classes):
"""Validate that the logit outputs are correct."""
input_width = 512
test_object = classification.Classification(input_width=input_width,
num_classes=num_classes,
output='logits')
# Create a 2-dimensional input (the first dimension is implicit).
cls_data = tf.keras.Input(shape=(input_width, ), dtype=tf.float32)
output = test_object(cls_data)
model = tf.keras.Model(cls_data, output)
logits_model = tf.keras.Model(test_object.inputs, test_object.logits)
batch_size = 3
input_data = 10 * np.random.random_sample((batch_size, input_width))
outputs = model.predict(input_data)
logits = logits_model.predict(input_data)
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, num_classes)
self.assertEqual(expected_output_shape, outputs.shape)
self.assertEqual(expected_output_shape, logits.shape)
self.assertAllClose(outputs, logits)
def test_network_creation(self, num_classes):
"""Validate that the Keras object can be created."""
input_width = 512
test_object = classification.Classification(input_width=input_width,
num_classes=num_classes)
# Create a 2-dimensional input (the first dimension is implicit).
cls_data = tf.keras.Input(shape=(input_width, ), dtype=tf.float32)
output = test_object(cls_data)
# Validate that the outputs are of the expected shape.
expected_output_shape = [None, num_classes]
self.assertEqual(expected_output_shape, output.shape.as_list())
def test_network_invocation(self, num_classes):
"""Validate that the Keras object can be invoked."""
input_width = 512
test_object = classification.Classification(
input_width=input_width, num_classes=num_classes, output='predictions')
# Create a 2-dimensional input (the first dimension is implicit).
cls_data = tf.keras.Input(shape=(input_width,), dtype=tf.float32)
output = test_object(cls_data)
# Invoke the network as part of a Model.
model = tf.keras.Model(cls_data, output)
input_data = 10 * np.random.random_sample((3, input_width))
_ = model.predict(input_data)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
network = classification.Classification(input_width=128,
num_classes=10,
initializer='zeros',
output='predictions')
# Create another network object from the first object's config.
new_network = classification.Classification.from_config(
network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
def test_unknown_output_type_fails(self):
with self.assertRaisesRegex(ValueError,
'Unknown `output` value "bad".*'):
_ = classification.Classification(input_width=128,
num_classes=10,
output='bad')
