def _create_signature_def_map(model, mode):
"""Creates a SignatureDef map from a Keras model."""
inputs_dict = {name: x for name, x in zip(model.input_names, model.inputs)}
if model.optimizer:
targets_dict = {
x.name.split(':')[0]: x
for x in model._targets if x is not None
} # pylint: disable=protected-access
inputs_dict.update(targets_dict)
outputs_dict = {
name: x
for name, x in zip(model.output_names, model.outputs)
}
metrics = saving_utils.extract_model_metrics(model)
# Add metric variables to the `LOCAL_VARIABLES` collection. Metric variables
# are by default not added to any collections. We are doing this here, so
# that metric variables get initialized.
local_vars = set(
tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.LOCAL_VARIABLES))
vars_to_add = set()
if metrics is not None:
for key, value in six.iteritems(metrics):
if isinstance(value, metrics_lib.Metric):
vars_to_add.update(value.variables)
# Convert Metric instances to (value_tensor, update_op) tuple.
metrics[key] = (value.result(), value.updates[0])
# Remove variables that are in the local variables collection already.
vars_to_add = vars_to_add.difference(local_vars)
for v in vars_to_add:
tf.compat.v1.add_to_collection(tf.compat.v1.GraphKeys.LOCAL_VARIABLES,
v)
export_outputs = model_utils.export_outputs_for_mode(
mode,
predictions=outputs_dict,
loss=model.total_loss if model.optimizer else None,
metrics=metrics)
return model_utils.build_all_signature_defs(
inputs_dict,
export_outputs=export_outputs,
serving_only=(mode == mode_keys.ModeKeys.PREDICT))
def _create_signature_def_map(model, mode):
"""Creates a SignatureDef map from a Keras model."""
inputs_dict = {name: x for name, x in zip(model.input_names, model.inputs)}
if model.optimizer:
targets_dict = {x.name.split(':')[0]: x
for x in model.targets if x is not None}
inputs_dict.update(targets_dict)
outputs_dict = {name: x
for name, x in zip(model.output_names, model.outputs)}
metrics = saving_utils.extract_model_metrics(model)
# Add metric variables to the `LOCAL_VARIABLES` collection. Metric variables
# are by default not added to any collections. We are doing this here, so
# that metric variables get initialized.
local_vars = set(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES))
vars_to_add = set()
if metrics is not None:
from tensorflow.python.keras.metrics import Metric # pylint: disable=g-import-not-at-top
for key, value in six.iteritems(metrics):
if isinstance(value, Metric):
vars_to_add.update(value.variables)
# Convert Metric instances to (value_tensor, update_op) tuple.
metrics[key] = (value.result(), value.updates[0])
# Remove variables that are in the local variables collection already.
vars_to_add = vars_to_add.difference(local_vars)
for v in vars_to_add:
ops.add_to_collection(ops.GraphKeys.LOCAL_VARIABLES, v)
export_outputs = model_utils.export_outputs_for_mode(
mode,
predictions=outputs_dict,
loss=model.total_loss if model.optimizer else None,
metrics=metrics)
return model_utils.build_all_signature_defs(
inputs_dict,
export_outputs=export_outputs,
serving_only=(mode == mode_keys.ModeKeys.PREDICT))
