def test_extract_model_metrics(self):
a = keras.layers.Input(shape=(3, ), name='input_a')
b = keras.layers.Input(shape=(3, ), name='input_b')
dense = keras.layers.Dense(4, name='dense')
c = dense(a)
d = dense(b)
e = keras.layers.Dropout(0.5, name='dropout')(c)
model = keras.models.Model([a, b], [d, e])
extract_metrics = saving_utils.extract_model_metrics(model)
self.assertEqual(None, extract_metrics)
extract_metric_names = [
'dense_loss', 'dropout_loss', 'dense_binary_accuracy',
'dropout_binary_accuracy'
]
model_metric_names = ['loss'] + extract_metric_names
model.compile(loss='mae',
metrics=[keras.metrics.BinaryAccuracy()],
optimizer=rmsprop.RMSPropOptimizer(learning_rate=0.01),
run_eagerly=None)
extract_metrics = saving_utils.extract_model_metrics(model)
self.assertEqual(set(model_metric_names), set(model.metrics_names))
self.assertEqual(set(extract_metric_names),
set(extract_metrics.keys()))
def test_extract_model_metrics(self):
a = keras.layers.Input(shape=(3,), name='input_a')
b = keras.layers.Input(shape=(3,), name='input_b')
dense = keras.layers.Dense(4, name='dense')
c = dense(a)
d = dense(b)
e = keras.layers.Dropout(0.5, name='dropout')(c)
model = keras.models.Model([a, b], [d, e])
extract_metrics = saving_utils.extract_model_metrics(model)
self.assertEqual(None, extract_metrics)
extract_metric_names = [
'dense_loss', 'dropout_loss', 'dense_binary_accuracy',
'dropout_binary_accuracy'
]
model_metric_names = ['loss'] + extract_metric_names
model.compile(
loss='mae',
metrics=[keras.metrics.BinaryAccuracy()],
optimizer=rmsprop.RMSPropOptimizer(learning_rate=0.01),
run_eagerly=None)
extract_metrics = saving_utils.extract_model_metrics(model)
self.assertEqual(set(model_metric_names), set(model.metrics_names))
self.assertEqual(set(extract_metric_names), set(extract_metrics.keys()))
def test_extract_model_metrics(self):
a = keras.layers.Input(shape=(3,), name='input_a')
b = keras.layers.Input(shape=(3,), name='input_b')
dense = keras.layers.Dense(4, name='dense')
c = dense(a)
d = dense(b)
e = keras.layers.Dropout(0.5, name='dropout')(c)
model = keras.models.Model([a, b], [d, e])
extract_metrics = saving_utils.extract_model_metrics(model)
self.assertEqual(None, extract_metrics)
extract_metric_names = [
'dense_binary_accuracy', 'dropout_binary_accuracy',
'dense_mean_squared_error', 'dropout_mean_squared_error'
]
if tf2.enabled():
extract_metric_names.extend(['dense_mae', 'dropout_mae'])
else:
extract_metric_names.extend(
['dense_mean_absolute_error', 'dropout_mean_absolute_error'])
model_metric_names = ['loss', 'dense_loss', 'dropout_loss'
] + extract_metric_names
model.compile(
loss='mae',
metrics=[
keras.metrics.BinaryAccuracy(), 'mae',
keras.metrics.mean_squared_error
],
optimizer=rmsprop.RMSPropOptimizer(learning_rate=0.01))
extract_metrics = saving_utils.extract_model_metrics(model)
self.assertEqual(set(model_metric_names), set(model.metrics_names))
self.assertEqual(set(extract_metric_names), set(extract_metrics.keys()))
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))
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))
