def _setup(self):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
if EstimatorCifar10BenchmarkTests.local_flags is None:
cifar_main.define_cifar_flags()
# Loads flags to get defaults to then override.
flags.FLAGS(['foo'])
saved_flag_values = flagsaver.save_flag_values()
EstimatorCifar10BenchmarkTests.local_flags = saved_flag_values
return
flagsaver.restore_flag_values(EstimatorCifar10BenchmarkTests.local_flags)
def _setup(self):
"""Setups up and resets flags before each test."""
tf.logging.set_verbosity(tf.logging.DEBUG)
if KerasCifar10BenchmarkTests.local_flags is None:
keras_common.define_keras_flags()
cifar_main.define_cifar_flags()
# Loads flags to get defaults to then override. List cannot be empty.
flags.FLAGS(['foo'])
saved_flag_values = flagsaver.save_flag_values()
KerasCifar10BenchmarkTests.local_flags = saved_flag_values
return
flagsaver.restore_flag_values(KerasCifar10BenchmarkTests.local_flags)
def setUpClass(cls): # pylint: disable=invalid-name
super(BaseTest, cls).setUpClass()
cifar10_main.define_cifar_flags()
num_eval_steps = None
validation_data = None
history = model.fit(
train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=[time_callback, lr_callback, tensorboard_callback],
validation_steps=num_eval_steps,
validation_data=validation_data,
verbose=2)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=1)
stats = keras_common.build_stats(history, eval_output, time_callback)
return stats
def main(_):
with logger.benchmark_context(flags.FLAGS):
return run(flags.FLAGS)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
cifar_main.define_cifar_flags()
keras_common.define_keras_flags()
absl_app.run(main)
def setUpClass(cls): # pylint: disable=invalid-name super(KerasCifarTest, cls).setUpClass() cifar10_main.define_cifar_flags() keras_common.define_keras_flags()
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=[
time_callback,
lr_callback,
tensorboard_callback
],
validation_steps=num_eval_steps,
validation_data=validation_data,
verbose=1)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=1)
stats = keras_common.build_stats(history, eval_output, time_callback)
return stats
def main(_):
with logger.benchmark_context(flags.FLAGS):
return run(flags.FLAGS)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
cifar_main.define_cifar_flags()
keras_common.define_keras_flags()
absl_app.run(main)
def setUpClass(cls): # pylint: disable=invalid-name
super(KerasCifarTest, cls).setUpClass()
cifar10_main.define_cifar_flags()
keras_common.define_keras_flags()
def setUpClass(cls): # pylint: disable=invalid-name super(BaseTest, cls).setUpClass() cifar10_main.define_cifar_flags()
def setUpClass(cls): # pylint: disable=invalid-name
super(BaseTest, cls).setUpClass()
if keras_utils.is_v2_0:
tf.compat.v1.disable_eager_execution()
cifar10_main.define_cifar_flags()