def test_tf_optimizer_iterations(self):
if context.executing_eagerly():
self.skipTest('v1 optimizer does not run in eager mode')
with self.cached_session():
optimizer = optimizer_v1.TFOptimizer(AdamOptimizer(0.01))
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
2,
input_shape=(3, ),
kernel_constraint=keras.constraints.MaxNorm(1)))
model.compile(loss='mean_squared_error',
optimizer=optimizer,
run_eagerly=testing_utils.should_run_eagerly())
keras.backend.track_tf_optimizer(optimizer)
self.assertEqual(
keras.backend.get_value(model.optimizer.iterations), 0)
model.fit(np.random.random((55, 3)),
np.random.random((55, 2)),
epochs=1,
batch_size=5,
verbose=0)
self.assertEqual(
keras.backend.get_value(model.optimizer.iterations), 11)
def test_tf_optimizer(self):
if context.executing_eagerly():
self.skipTest('v1 optimizer does not run in eager mode')
optimizer = optimizer_v1.TFOptimizer(AdamOptimizer(0.01))
model = keras.models.Sequential()
model.add(
keras.layers.Dense(2,
input_shape=(3, ),
kernel_constraint=keras.constraints.MaxNorm(1)))
# This is possible
model.compile(loss='mean_squared_error',
optimizer=optimizer,
run_eagerly=testing_utils.should_run_eagerly())
keras.backend.track_tf_optimizer(optimizer)
model.fit(np.random.random((5, 3)),
np.random.random((5, 2)),
epochs=1,
batch_size=5,
verbose=0)
# not supported
with self.assertRaises(NotImplementedError):
_ = optimizer.weights
with self.assertRaises(NotImplementedError):
optimizer.get_config()
with self.assertRaises(NotImplementedError):
optimizer.from_config(None)
def test_optimizer_garbage_collection(self):
if context.executing_eagerly():
self.skipTest('v1 optimizer does not run in eager mode')
graph = ops.Graph()
with graph.as_default():
optimizer = optimizer_v1.TFOptimizer(AdamOptimizer(0.01))
keras.backend.track_tf_optimizer(optimizer)
optimizer_weak = weakref.ref(optimizer)
graph_weak = weakref.ref(graph)
del graph, optimizer
gc.collect()
# Check that the weak references are dead now.
self.assertIs(graph_weak(), None)
self.assertIs(optimizer_weak(), None)
def clone_and_build_model(model,
input_tensors=None,
target_tensors=None,
custom_objects=None,
compile_clone=True,
in_place_reset=False,
optimizer_iterations=None,
optimizer_config=None):
"""Clone a `Model` and build/compile it with the same settings used before.
This function can be run in the same graph or in a separate graph from the
model. When using a separate graph, `in_place_reset` must be `False`.
Note that, currently, the clone produced from this function may not work with
TPU DistributionStrategy. Try at your own risk.
Args:
model: `tf.keras.Model` object. Can be Functional, Sequential, or
sub-classed.
input_tensors: Optional list or dictionary of input tensors to build the
model upon. If not provided, placeholders will be created.
target_tensors: Optional list of target tensors for compiling the model. If
not provided, placeholders will be created.
custom_objects: Optional dictionary mapping string names to custom classes
or functions.
compile_clone: Boolean, whether to compile model clone (default `True`).
in_place_reset: Boolean, whether to reset the model in place. Only used if
the model is a subclassed model. In the case of a subclassed model,
this argument must be set to `True` (default `False`). To restore the
original model, use the function
`in_place_subclassed_model_state_restoration(model)`.
optimizer_iterations: An iterations variable that will be incremented by the
optimizer if the clone is compiled. This argument is used when a Keras
model is cloned into an Estimator model function, because Estimators
create their own global step variable.
optimizer_config: Optimizer config dictionary or list of dictionary
returned from `get_config()`. This argument should be defined if
`clone_and_build_model` is called in a different graph or session from
the original model, and the optimizer is an instance of `OptimizerV2`.
Returns:
Clone of the model.
Raises:
ValueError: Cloning fails in the following cases
- cloning a subclassed model with `in_place_reset` set to False.
- compiling the clone when the original model has not been compiled.
"""
# Grab optimizer now, as we reset-in-place for subclassed models, but
# want to maintain access to the original optimizer.
orig_optimizer = model.optimizer
if compile_clone and not orig_optimizer:
raise ValueError(
'Error when cloning model: compile_clone was set to True, but the '
'original model has not been compiled.')
if compile_clone:
compile_args = model._get_compile_args() # pylint: disable=protected-access
# Allows this method to be robust to switching graph and eager classes.
model._get_compile_args = lambda: compile_args
with CustomObjectScope(custom_objects or {}):
if model._is_graph_network:
clone = clone_model(model, input_tensors=input_tensors)
elif isinstance(model, Sequential):
clone = clone_model(model, input_tensors=input_tensors)
if (not clone._is_graph_network
and model._build_input_shape is not None):
if ops.executing_eagerly_outside_functions():
clone.build(model._build_input_shape)
else:
clone._set_inputs(
backend.placeholder(model._build_input_shape,
dtype=model.inputs[0].dtype))
else:
try:
# Prefer cloning the model if serial/deserial logic is implemented for
# subclassed model.
clone = model.__class__.from_config(model.get_config())
except NotImplementedError:
logging.warning(
'This model is a subclassed model. Please implement '
'`get_config` and `from_config` to better support '
'cloning the model.')
if not in_place_reset:
raise ValueError(
'This model is a subclassed model. '
'Such a model cannot be cloned, but there is a workaround where '
'the model is reset in-place. To use this, please set the '
'argument `in_place_reset` to `True`. This will reset the '
'attributes in the original model. To restore the attributes, '
'call `in_place_subclassed_model_state_restoration(model)`.'
)
clone = model
_in_place_subclassed_model_reset(clone)
if input_tensors is not None:
if isinstance(input_tensors,
(list, tuple)) and len(input_tensors) == 1:
input_tensors = input_tensors[0]
clone._set_inputs(input_tensors)
if compile_clone:
if isinstance(orig_optimizer, optimizer_v1.TFOptimizer):
optimizer = optimizer_v1.TFOptimizer(orig_optimizer.optimizer,
optimizer_iterations)
backend.track_tf_optimizer(optimizer)
else:
if not isinstance(orig_optimizer, (tuple, list)):
orig_optimizer = [orig_optimizer]
if optimizer_config is None:
optimizer = [
opt.__class__.from_config(opt.get_config())
for opt in orig_optimizer
]
elif isinstance(optimizer_config, dict):
optimizer = [
orig_optimizer[0].__class__.from_config(optimizer_config)
]
else:
# optimizer config is list of dict, same order as orig_optimizer.
optimizer = [
opt.__class__.from_config(opt_config)
for (opt,
opt_config) in zip(orig_optimizer, optimizer_config)
]
if optimizer_iterations is not None:
for opt in optimizer:
opt.iterations = optimizer_iterations
if len(optimizer) == 1:
optimizer = optimizer[0]
compile_args['optimizer'] = optimizer
if target_tensors is not None:
compile_args['target_tensors'] = target_tensors
# Ensure Metric objects in new model are separate from existing model.
compile_args['metrics'] = metrics_module.clone_metrics(
compile_args['metrics'])
compile_args['weighted_metrics'] = metrics_module.clone_metrics(
compile_args['weighted_metrics'])
clone.compile(**compile_args)
return clone