def test_extract_model_metrics(self):
# saving_utils.extract_model_metrics is used in V1 only API
# keras.experimental.export_saved_model.
with tf.Graph().as_default():
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 tf.__internal__.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=tf.compat.v1.train.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
} # 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 metrics.items():
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),
)
