def setUp(self):
# Set up simple model in which the ground-truth data is a tensor of ones and
# the predicted data is a tensor of zeros.
super().setUp() # Sets up the TensorFlow environment, so call it early.
self.sess = tf.keras.backend.get_session()
inputs = tf.keras.Input(1)
outputs = dynamics.TrainingStepCounter()(inputs)
self.model = tf.keras.Model(inputs=inputs, outputs=outputs)
self.model.compile('sgd', 'mse')
def testMultipleCallsAreCountedOnce(self):
"""Calling the same layer twice should not increase the counter twice."""
# Create a model that calls the same TrainingStepCounter layer twice:
counter = dynamics.TrainingStepCounter()
inputs = tf.keras.Input(1)
output1 = counter(inputs)
output2 = counter(inputs)
model = tf.keras.Model(inputs=inputs, outputs=[output1, output2])
model.compile('sgd', 'mse')
# Train:
num_epochs = 2
steps_per_epoch = 5
model.fit(x=np.zeros(1),
y=[np.zeros(1), np.zeros(1)],
epochs=num_epochs,
steps_per_epoch=steps_per_epoch)
step_count = self.sess.run(model.layers[-1].weights[0])
self.assertEqual(step_count, num_epochs * steps_per_epoch)
