def model_fn(features, labels, mode):
"""model_fn for keras Estimator."""
# Raise an error when users use DistributionStrategy with native Keras
# optimizers. Currently we only support native TensorFlow optimizers.
if distribution_strategy_context.has_distribution_strategy() and \
not isinstance(keras_model.optimizer,
(tf_optimizer_module.Optimizer, optimizers.TFOptimizer)):
raise ValueError(
'Only TensorFlow native optimizers are supported with '
'DistributionStrategy.')
model = _clone_and_build_model(mode, keras_model, custom_objects,
features, labels)
model_output_names = []
# We need to make sure that the output names of the last layer in the model
# is the same for each of the cloned models. This is required for mirrored
# strategy when we call regroup.
if distribution_strategy_context.has_distribution_strategy():
for name in model.output_names:
name = re.compile(r'_\d$').sub('', name)
model_output_names.append(name)
else:
model_output_names = model.output_names
# Get inputs to EstimatorSpec
predictions = dict(zip(model_output_names, model.outputs))
loss = None
train_op = None
eval_metric_ops = None
# Set loss and metric only during train and evaluate.
if mode is not model_fn_lib.ModeKeys.PREDICT:
if mode is model_fn_lib.ModeKeys.TRAIN:
model._make_train_function() # pylint: disable=protected-access
else:
model._make_test_function() # pylint: disable=protected-access
loss = model.total_loss
eval_metric_ops = _convert_keras_metrics_to_estimator(model)
# Set train_op only during train.
if mode is model_fn_lib.ModeKeys.TRAIN:
train_op = model.train_function.updates_op
if not model._is_graph_network:
# Reset model state to original state,
# to avoid `model_fn` being destructive for the initial model argument.
models.in_place_subclassed_model_state_restoration(keras_model)
return model_fn_lib.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
export_outputs={
_DEFAULT_SERVING_KEY:
export_lib.export_output.PredictOutput(predictions)
})
def model_fn(features, labels, mode):
"""model_fn for keras Estimator."""
# Raise an error when users use DistributionStrategy with native Keras
# optimizers. Currently we only support native TensorFlow optimizers.
if distribution_strategy_context.has_distribution_strategy() and \
not isinstance(keras_model.optimizer,
(tf_optimizer_module.Optimizer, optimizers.TFOptimizer)):
raise ValueError('Only TensorFlow native optimizers are supported with '
'DistributionStrategy.')
model = _clone_and_build_model(mode, keras_model, custom_objects, features,
labels)
model_output_names = []
# We need to make sure that the output names of the last layer in the model
# is the same for each of the cloned models. This is required for mirrored
# strategy when we call regroup.
if distribution_strategy_context.has_distribution_strategy():
for name in model.output_names:
name = re.compile(r'_\d$').sub('', name)
model_output_names.append(name)
else:
model_output_names = model.output_names
# Get inputs to EstimatorSpec
predictions = dict(zip(model_output_names, model.outputs))
loss = None
train_op = None
eval_metric_ops = None
# Set loss and metric only during train and evaluate.
if mode is not model_fn_lib.ModeKeys.PREDICT:
if mode is model_fn_lib.ModeKeys.TRAIN:
model._make_train_function() # pylint: disable=protected-access
else:
model._make_test_function() # pylint: disable=protected-access
loss = model.total_loss
eval_metric_ops = _convert_keras_metrics_to_estimator(model)
# Set train_op only during train.
if mode is model_fn_lib.ModeKeys.TRAIN:
train_op = model.train_function.updates_op
if not model._is_graph_network:
# Reset model state to original state,
# to avoid `model_fn` being destructive for the initial model argument.
models.in_place_subclassed_model_state_restoration(keras_model)
return model_fn_lib.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
export_outputs={
_DEFAULT_SERVING_KEY:
export_lib.export_output.PredictOutput(predictions)
})
def model_fn(features, labels, mode):
"""model_fn for keras Estimator."""
model = _clone_and_build_model(mode=mode,
keras_model=keras_model,
custom_objects=custom_objects,
features=features,
labels=labels,
optimizer_config=optimizer_config)
model_output_names = []
# We need to make sure that the output names of the last layer in the model
# is the same for each of the cloned models. This is required for mirrored
# strategy when we call regroup.
if tf.distribute.has_strategy():
for name in model.output_names:
name = re.compile(r'_\d$').sub('', name)
model_output_names.append(name)
else:
model_output_names = model.output_names
# Get inputs to EstimatorSpec
predictions = dict(zip(model_output_names, model.outputs))
loss = None
train_op = None
eval_metric_ops = None
# Set loss and metric only during train and evaluate.
if mode is not ModeKeys.PREDICT:
if mode is ModeKeys.TRAIN:
model._make_train_function() # pylint: disable=protected-access
else:
model._make_test_function() # pylint: disable=protected-access
loss = model.total_loss
eval_metric_ops = _convert_keras_metrics_to_estimator(model)
# Set train_op only during train.
if mode is ModeKeys.TRAIN:
train_op = model.train_function.updates_op
if (not model._is_graph_network
and hasattr(keras_model, '_original_attributes_cache')
and keras_model._original_attributes_cache is not None):
# To avoid `model_fn` being destructive for the initial model argument.
models.in_place_subclassed_model_state_restoration(keras_model)
scaffold = None
if save_object_ckpt:
model._track_trackable(tf.compat.v1.train.get_global_step(),
'estimator_global_step')
# Create saver that maps variable names to object-checkpoint keys.
object_graph = graph_view.ObjectGraphView(model)
var_list = object_graph.frozen_saveable_objects()
saver = tf.compat.v1.train.Saver(var_list=var_list, sharded=True)
saver._object_restore_saver = trackable_util.frozen_saver(model)
scaffold = tf.compat.v1.train.Scaffold(saver=saver)
return model_fn_lib.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
export_outputs={
_DEFAULT_SERVING_KEY: export_lib.PredictOutput(predictions)
},
scaffold=scaffold)
