def test_calling_with_unsupported_predefined_callbacks(self, distribution):
with self.cached_session():
with distribution.scope():
model = keras_test_lib.get_model()
optimizer = gradient_descent.GradientDescentOptimizer(0.001)
loss = 'mse'
metrics = ['mae']
model.compile(optimizer, loss, metrics=metrics)
dataset = keras_test_lib.get_dataset(distribution)
def schedule(_):
return 0.001
with self.assertRaisesRegexp(
ValueError, 'You must specify a Keras Optimizer V2 when '
'using'):
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
callbacks=[
keras.callbacks.LearningRateScheduler(schedule)
])
with self.assertRaisesRegexp(
ValueError, 'You must specify a Keras Optimizer V2 when '
'using'):
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
callbacks=[keras.callbacks.ReduceLROnPlateau()])
def test_calling_with_unsupported_predefined_callbacks(self, distribution):
with self.cached_session():
with distribution.scope():
model = keras_test_lib.get_model()
optimizer = gradient_descent.GradientDescentOptimizer(0.001)
loss = 'mse'
metrics = ['mae']
model.compile(optimizer, loss, metrics=metrics)
dataset = keras_test_lib.get_dataset(distribution)
def schedule(_):
return 0.001
with self.assertRaisesRegexp(
ValueError, 'You must specify a Keras Optimizer V2 when '
'using'):
model.fit(
dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
callbacks=[keras.callbacks.LearningRateScheduler(schedule)])
with self.assertRaisesRegexp(
ValueError, 'You must specify a Keras Optimizer V2 when '
'using'):
model.fit(
dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
callbacks=[keras.callbacks.ReduceLROnPlateau()])
def test_unsupported_features(self, distribution):
with self.cached_session():
with distribution.scope():
model = keras_test_lib.get_model()
optimizer = gradient_descent.GradientDescentOptimizer(0.001)
loss = 'mse'
metrics = ['mae']
model.compile(optimizer, loss, metrics=metrics)
dataset = keras_test_lib.get_dataset(distribution)
# Test with validation split
with self.assertRaisesRegexp(
ValueError, '`validation_split` argument is not '
'supported when input `x` is a dataset or a '
'dataset iterator.+'):
model.fit(
dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_split=0.5,
validation_steps=2)
# Test with sample weight.
sample_weight = np.random.random((10,))
with self.assertRaisesRegexp(
ValueError, '`sample_weight` argument is not supported when input '
'`x` is a dataset or a dataset iterator.'):
model.fit(
dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
sample_weight=sample_weight)
# Test with not specifying the `steps` argument for dataset with infinite
# cardinality.
dataset = dataset.repeat()
with self.assertRaisesRegexp(
ValueError, 'When passing an infinitely '
'repeating dataset, you must specify the '
'`steps_per_epoch` argument'):
model.fit(dataset, epochs=1, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'When passing an infinitely '
'repeating dataset, you must specify the '
'`steps` argument'):
model.evaluate(dataset, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'When passing an infinitely '
'repeating dataset, you must specify the '
'`steps` argument'):
model.predict(dataset, verbose=0)
def test_unsupported_features(self, distribution):
with self.cached_session():
with distribution.scope():
model = keras_test_lib.get_model()
optimizer = gradient_descent.GradientDescentOptimizer(0.001)
loss = 'mse'
metrics = ['mae']
model.compile(optimizer, loss, metrics=metrics)
dataset = keras_test_lib.get_dataset(distribution)
# Test with validation split
with self.assertRaisesRegexp(
ValueError, '`validation_split` argument is not '
'supported when input `x` is a dataset or a '
'dataset iterator.+'):
model.fit(
dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_split=0.5,
validation_steps=2)
# Test with sample weight.
sample_weight = np.random.random((10,))
with self.assertRaisesRegexp(
ValueError, '`sample_weight` argument is not supported when input '
'`x` is a dataset or a dataset iterator.'):
model.fit(
dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
sample_weight=sample_weight)
# Test with not specifying the `steps` argument for dataset with infinite
# cardinality.
dataset = dataset.repeat()
with self.assertRaisesRegexp(
ValueError, 'When passing an infinitely '
'repeating dataset, you must specify the '
'`steps_per_epoch` argument'):
model.fit(dataset, epochs=1, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'When passing an infinitely '
'repeating dataset, you must specify the '
'`steps` argument'):
model.evaluate(dataset, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'When passing an infinitely '
'repeating dataset, you must specify the '
'`steps` argument'):
model.predict(dataset, verbose=0)
def test_save_load_h5(self, distribution):
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(gradient_descent_keras.SGD(0.01), 'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp('.h5')
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(gradient_descent_keras.SGD(0.01), 'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_callbacks_in_eval(self, distribution):
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer='sgd', loss='mse', metrics=['mae'])
dataset = keras_test_lib.get_dataset(distribution)
counter = Counter()
model.evaluate(dataset, steps=5, callbacks=[counter])
self.assertDictEqual(
counter.method_counts, {
'on_test_batch_begin': 5,
'on_test_batch_end': 5,
'on_test_begin': 1,
'on_test_end': 1
})
def test_callbacks_in_fit(self, distribution):
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer='sgd', loss='mse', metrics=['mae'])
dataset = keras_test_lib.get_dataset(distribution)
counter = Counter()
epochs = 2
steps_per_epoch = 5
validation_steps = 3
model.fit(
dataset,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=0,
validation_data=dataset,
validation_steps=validation_steps,
callbacks=[counter])
if isinstance(distribution, tpu_strategy.TPUStrategy):
# TPU Strategy can have multi step training, from extended.steps_per_run
# if steps_per_run = 1, then num_batch_call_per_epoch = steps_per_epoch
steps_per_run = distribution.extended.steps_per_run
num_batch_call_per_epoch = steps_per_epoch // steps_per_run
if steps_per_epoch % steps_per_run:
num_batch_call_per_epoch += 1
else:
num_batch_call_per_epoch = steps_per_epoch
self.assertDictEqual(
counter.method_counts, {
'on_batch_begin': epochs * num_batch_call_per_epoch,
'on_batch_end': epochs * num_batch_call_per_epoch,
'on_epoch_begin': epochs,
'on_epoch_end': epochs,
'on_test_batch_begin': epochs * validation_steps,
'on_test_batch_end': epochs * validation_steps,
'on_test_begin': epochs,
'on_test_end': epochs,
'on_train_batch_begin': epochs * num_batch_call_per_epoch,
'on_train_batch_end': epochs * num_batch_call_per_epoch,
'on_train_begin': 1,
'on_train_end': 1
})
def test_save_load_h5(self, distribution):
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(gradient_descent_keras.SGD(0.01), 'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp('.h5')
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(gradient_descent_keras.SGD(0.01), 'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_callbacks_in_eval(self, distribution):
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer='sgd', loss='mse', metrics=['mae'])
dataset = keras_test_lib.get_dataset(distribution)
counter = Counter()
model.evaluate(dataset, steps=5, callbacks=[counter])
self.assertDictEqual(
counter.method_counts, {
'on_test_batch_begin': 5,
'on_test_batch_end': 5,
'on_test_begin': 1,
'on_test_end': 1
})
def test_callbacks_in_fit(self, distribution):
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer='sgd', loss='mse', metrics=['mae'])
dataset = keras_test_lib.get_dataset(distribution)
counter = Counter()
epochs = 2
steps_per_epoch = 5
validation_steps = 3
model.fit(
dataset,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=0,
validation_data=dataset,
validation_steps=validation_steps,
callbacks=[counter])
if isinstance(distribution, tpu_strategy.TPUStrategy):
# TPU Strategy can have multi step training, from extended.steps_per_run
# if steps_per_run = 1, then num_batch_call_per_epoch = steps_per_epoch
steps_per_run = distribution.extended.steps_per_run
num_batch_call_per_epoch = steps_per_epoch // steps_per_run
if steps_per_epoch % steps_per_run:
num_batch_call_per_epoch += 1
else:
num_batch_call_per_epoch = steps_per_epoch
self.assertDictEqual(
counter.method_counts, {
'on_batch_begin': epochs * num_batch_call_per_epoch,
'on_batch_end': epochs * num_batch_call_per_epoch,
'on_epoch_begin': epochs,
'on_epoch_end': epochs,
'on_test_batch_begin': epochs * validation_steps,
'on_test_batch_end': epochs * validation_steps,
'on_test_begin': epochs,
'on_test_end': epochs,
'on_train_batch_begin': epochs * num_batch_call_per_epoch,
'on_train_batch_end': epochs * num_batch_call_per_epoch,
'on_train_begin': 1,
'on_train_end': 1
})
def test_save_load_trackable_optimizer_v2(self, distribution):
# TODO(b/123533246): Enable the test for TPU once bug is fixed
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(gradient_descent_keras.SGD(0.01), 'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp()
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(gradient_descent_keras.SGD(0.01), 'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_save_load_trackable(self, distribution):
# TODO(sourabhbajaj): Test fails with optimizer v2 without h5
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(gradient_descent.GradientDescentOptimizer(0.01), 'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp()
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(gradient_descent.GradientDescentOptimizer(0.01), 'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_save_load_trackable_optimizer_v2(self, distribution):
# TODO(b/123533246): Enable the test for TPU once bug is fixed
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(gradient_descent_keras.SGD(0.01), 'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp()
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(gradient_descent_keras.SGD(0.01), 'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_save_load_trackable(self, distribution):
# TODO(sourabhbajaj): Test fails with optimizer v2 without h5
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(gradient_descent.GradientDescentOptimizer(0.01),
'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp()
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(
gradient_descent.GradientDescentOptimizer(0.01), 'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
