def make_model():
_x = Input((10, ))
_y = Dense(10)(_x)
_m = Model(_x, _y)
_m.compile('adam', 'mean_squared_error')
_m._make_train_function()
return _m
def load_optimizer_weights(model: Model, load_path: pathlib.Path) -> None:
"""
Load the optimizer states from a tf.keras model saved with
tf.keras.models.save_model(). Ignores and prints a warning message when
encountering a graph network. This implementation is lifted from
tf.keras.models.load_model().
"""
f = h5py.File(str(load_path), mode="r")
if "optimizer_weights" in f:
# Build train function (to get weight updates). Models that aren't
# graph networks must wait until they are called with data to
# _make_train_function() and so can't load optimizer weights.
if model._is_graph_network: # pylint: disable=protected-access
model._make_train_function()
optimizer_weight_values = load_optimizer_weights_from_hdf5_group(f)
try:
model.optimizer.set_weights(optimizer_weight_values)
except ValueError:
logging.warning("Error in loading the saved optimizer "
"state. As a result, your model is "
"starting with a freshly initialized "
"optimizer.")
else:
logging.warning("Sequential models without an `input_shape` "
"passed to the first layer cannot reload their "
"optimizer state. As a result, your model is "
"starting with a freshly initialized optimizer.")
def load_optimizer_weights(
model: Model, h5group: Any, optimizer: tf.keras.optimizers.Optimizer
) -> None:
"""
Load the optimizer states from a tf.keras model saved with
tf.keras.models.save_model(). Ignores and prints a warning message when
encountering a graph network. This implementation is lifted from
tf.keras.models.load_model().
"""
tf2_2_or_newer = version.parse(tf.__version__) >= version.parse("2.2.0")
if model._is_graph_network or tf2_2_or_newer: # pylint: disable=protected-access
if tf2_2_or_newer:
try:
optimizer._create_all_weights(model.trainable_variables)
except (NotImplementedError, AttributeError):
logging.warning(
"Error when creating the weights of optimizer, making it "
"impossible to restore the saved optimizer state. As a result, "
"your model is starting with a freshly initialized optimizer."
)
else:
# Build train function (to get weight updates). Models that aren't
# graph networks must wait until they are called with data to
# _make_train_function() and so can't load optimizer weights.
model._make_train_function()
optimizer_weight_values = load_optimizer_weights_from_hdf5_group(h5group)
try:
optimizer.set_weights(optimizer_weight_values)
except ValueError:
logging.warning(
"Error in loading the saved optimizer "
"state. As a result, your model is "
"starting with a freshly initialized "
"optimizer."
)
else:
logging.warning(
"Sequential models without an `input_shape` "
"passed to the first layer cannot reload their "
"optimizer state. As a result, your model is "
"starting with a freshly initialized optimizer."
)
