def from_config(cls, config, custom_objects=None):
config = config.copy() # Make a copy, since we mutate config
config['optimizer'] = optimizers.deserialize(
config['optimizer'], custom_objects=custom_objects)
config['loss_scale'] = keras_loss_scale_module.deserialize(
config['loss_scale'], custom_objects=custom_objects)
return cls(**config)
def test_serialization(self):
loss_scale = loss_scale_module.DynamicLossScale(
initial_loss_scale=1, increment_period=2, multiplier=3)
config = loss_scale_module.serialize(loss_scale)
loss_scale = loss_scale_module.deserialize(config)
self.evaluate(variables.global_variables_initializer())
self.assertEqual(self.evaluate(loss_scale()), 1)
self.assertEqual(loss_scale.increment_period, 2)
self.assertEqual(loss_scale.multiplier, 3)
def test_serialization(self):
loss_scale = loss_scale_module.DynamicLossScale(initial_loss_scale=1,
increment_period=2,
multiplier=3)
config = loss_scale_module.serialize(loss_scale)
loss_scale = loss_scale_module.deserialize(config)
self.evaluate(variables.global_variables_initializer())
self.assertEqual(self.evaluate(loss_scale()), 1)
self.assertEqual(loss_scale.increment_period, 2)
self.assertEqual(loss_scale.multiplier, 3)
def from_config(cls, config, custom_objects=None):
if 'loss_scale' in config and isinstance(config['loss_scale'], dict):
config = config.copy()
config['loss_scale'] = keras_loss_scale_module.deserialize(
config['loss_scale'], custom_objects=custom_objects)
return cls(**config)
def test_serialization(self):
loss_scale = loss_scale_module.get(123)
config = loss_scale_module.serialize(loss_scale)
loss_scale = loss_scale_module.deserialize(config)
self.assertEqual(self.evaluate(loss_scale()), 123.)
def __init__(self, optimizer, loss_scale):
warn_msg_prefix = (
'tf.keras.mixed_precision.experimental.LossScaleOptimizer is '
'deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer '
'instead. ')
if isinstance(loss_scale, dict):
loss_scale = keras_loss_scale_module.deserialize(loss_scale)
if isinstance(loss_scale, (int, float)):
tf_logging.warn(
warn_msg_prefix + 'For example\n'
' opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
'opt, dynamic=False, initial_scale={})'.format(loss_scale))
super(LossScaleOptimizerV1, self).__init__(optimizer, dynamic=False,
initial_scale=loss_scale)
elif isinstance(loss_scale, loss_scale_module.FixedLossScale):
ls_val = loss_scale._loss_scale_value # pylint: disable=protected-access
tf_logging.warn(
warn_msg_prefix + 'For example\n'
' opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
'opt, dynamic=False, initial_scale={})'.format(ls_val))
super(LossScaleOptimizerV1, self).__init__(optimizer, dynamic=False,
initial_scale=ls_val)
elif loss_scale == 'dynamic':
tf_logging.warn(
warn_msg_prefix + 'For example\n'
' opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
'opt)')
super(LossScaleOptimizerV1, self).__init__(optimizer)
elif isinstance(loss_scale, loss_scale_module.DynamicLossScale):
kwargs = {}
extra_arguments = ''
if loss_scale.initial_loss_scale != _DEFAULT_INITIAL_SCALE:
kwargs['initial_scale'] = loss_scale.initial_loss_scale
extra_arguments += (', initial_scale=%s' %
loss_scale.initial_loss_scale)
if loss_scale.increment_period != _DEFAULT_GROWTH_STEPS:
kwargs['dynamic_growth_steps'] = loss_scale.increment_period
extra_arguments += (', dynamic_growth_steps=%s' %
loss_scale.increment_period)
if loss_scale.multiplier != 2:
raise ValueError('When passing a DynamicLossScale to "loss_scale", '
'DynamicLossScale.multiplier must be 2. Got: %s'
% (loss_scale,))
tf_logging.warn(
warn_msg_prefix +
'Note that the non-experimental LossScaleOptimizer does not take a '
'DynamicLossScale but instead takes the dynamic configuration '
'directly in the constructor. For example:\n'
' opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
'opt{})\n'.format(extra_arguments))
super(LossScaleOptimizerV1, self).__init__(optimizer, **kwargs)
elif isinstance(loss_scale, loss_scale_module.LossScale):
raise TypeError('Passing a LossScale that is not a FixedLossScale or a '
'DynamicLossScale is no longer supported. Got: {}'
.format(loss_scale))
else:
raise ValueError('Invalid value passed to loss_scale. loss_scale '
'must be the string "dynamic" (recommended), an int, '
'a float, a FixedLossScale, or a DynamicLossScale. Got '
'value: {}'.format(loss_scale))
def test_serialization(self): loss_scale = loss_scale_module.get(123) config = loss_scale_module.serialize(loss_scale) loss_scale = loss_scale_module.deserialize(config) self.assertEqual(self.evaluate(loss_scale()), 123.)
