def from_config(cls, config, custom_objects=None):
user_cells = []
session_cells = []
for cell_config in config.pop('user_cells'):
user_cells.append(
deserialize_layer(cell_config, custom_objects=custom_objects))
for cell_config in config.pop('session_cells'):
session_cells.append(
deserialize_layer(cell_config, custom_objects=custom_objects))
embedding = deserialize_layer(config.pop('embedding_layer'),
custom_objects=custom_objects)
return cls(user_cells, session_cells, embedding, **config)
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
cells = []
for cell_config in config.pop('cells'):
cells.append(
deserialize_layer(cell_config, custom_objects=custom_objects))
return cls(cells, **config)
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
# Avoid mutating the input dict
config = copy.deepcopy(config)
layer = deserialize_layer(config.pop('layer'),
custom_objects=custom_objects)
return cls(layer, **config)
def from_config(cls, config):
config = config.copy()
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
layer = deserialize_layer(config.pop('layer'))
config['layer'] = layer
return cls(**config)
def from_config(cls, config, custom_objects=None):
# Instead of updating the input, create a copy and use that.
config = copy.deepcopy(config)
num_constants = config.pop('num_constants', 0)
# Handle forward layer instantiation (as would parent class).
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
config['layer'] = deserialize_layer(config['layer'],
custom_objects=custom_objects)
# Handle (optional) backward layer instantiation.
backward_layer_config = config.pop('backward_layer', None)
if backward_layer_config is not None:
backward_layer = deserialize_layer(backward_layer_config,
custom_objects=custom_objects)
config['backward_layer'] = backward_layer
# Instantiate the wrapper, adjust it and return it.
layer = cls(**config)
layer._num_constants = num_constants
return layer
def from_config(cls, config):
config = config.copy()
quantize_provider = deserialize_keras_object(
config.pop('quantize_provider'),
module_objects=globals(),
custom_objects=None)
layer = deserialize_layer(config.pop('layer'))
return cls(layer=layer, quantize_provider=quantize_provider, **config)
def _from_config(cls_initializer, config):
"""All shared from_config logic for fused layers."""
config = config.copy()
# use_bias is not an argument of this class, as explained by
# comment in __init__.
config.pop('use_bias')
is_advanced_activation = 'class_name' in config['post_activation']
if is_advanced_activation:
config['post_activation'] = deserialize_layer(config['post_activation'])
else:
config['post_activation'] = activations.deserialize(
config['post_activation'])
return cls_initializer(**config)
def from_config(cls, config, custom_objects=None):
config = config.copy()
number_of_clusters = config.pop('number_of_clusters')
cluster_centroids_init = config.pop('cluster_centroids_init')
config['number_of_clusters'] = number_of_clusters
config['cluster_centroids_init'] = cluster_centroids_init
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
layer = deserialize_layer(config.pop('layer'),
custom_objects=custom_objects)
config['layer'] = layer
return cls(**config)
def from_config(cls, config, custom_objects=None):
# Instead of updating the input, create a copy and use that.
config = config.copy()
num_constants = config.pop('num_constants', None)
backward_layer_config = config.pop('backward_layer', None)
if backward_layer_config is not None:
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
backward_layer = deserialize_layer(
backward_layer_config, custom_objects=custom_objects)
config['backward_layer'] = backward_layer
layer = super(Bidirectional, cls).from_config(config,
custom_objects=custom_objects)
layer._num_constants = num_constants
return layer
def from_config(cls, config):
config = config.copy()
# QuantizeWrapper may be constructed with any QuantizeProvider and the
# wrapper itself cannot know all the possible provider classes.
# The deserialization code should ensure the QuantizeProvider is in keras
# serialization scope.
quantize_provider = deserialize_keras_object(
config.pop('quantize_provider'),
module_objects=globals(),
custom_objects=None)
layer = deserialize_layer(config.pop('layer'))
return cls(layer=layer, quantize_provider=quantize_provider, **config)
def from_config(cls, config, custom_objects=None):
# Instead of updating the input, create a copy and use that.
config = config.copy()
num_constants = config.pop('num_constants', 0)
backward_layer_config = config.pop('backward_layer', None)
if backward_layer_config is not None:
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
backward_layer = deserialize_layer(
backward_layer_config, custom_objects=custom_objects)
config['backward_layer'] = backward_layer
layer = super(Bidirectional, cls).from_config(config,
custom_objects=custom_objects)
layer._num_constants = num_constants
return layer
def testSerializationQuantizeAnnotate(self):
input_shape = (2,)
layer = keras.layers.Dense(3)
wrapper = quantize_annotate.QuantizeAnnotate(
layer=layer,
quantize_provider=self.TestQuantizeProvider(),
input_shape=input_shape)
custom_objects = {
'QuantizeAnnotate': quantize_annotate.QuantizeAnnotate,
'TestQuantizeProvider': self.TestQuantizeProvider
}
serialized_wrapper = serialize_layer(wrapper)
with keras.utils.custom_object_scope(custom_objects):
wrapper_from_config = deserialize_layer(serialized_wrapper)
self.assertEqual(wrapper_from_config.get_config(), wrapper.get_config())
def testSerializationQuantizeWrapper(self):
input_shape = (2,)
layer = keras.layers.Dense(3)
wrapper = QuantizeWrapper(
layer=layer,
quantize_provider=self.quantize_registry.get_quantize_provider(layer),
input_shape=input_shape)
custom_objects = {
'QuantizeAwareActivation': QuantizeAwareActivation,
'QuantizeWrapper': QuantizeWrapper
}
custom_objects.update(tflite_quantize_registry._types_dict())
serialized_wrapper = serialize_layer(wrapper)
with keras.utils.custom_object_scope(custom_objects):
wrapper_from_config = deserialize_layer(serialized_wrapper)
self.assertEqual(wrapper_from_config.get_config(), wrapper.get_config())
def from_config(cls, config):
config = config.copy()
quantize_provider = config.pop('quantize_provider')
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object # pylint: disable=g-import-not-at-top
# TODO(pulkitb): Add all known `QuantizeProvider`s to custom_objects
custom_objects = {
'QuantizeProvider': quantize_provider_mod.QuantizeProvider
}
config['quantize_provider'] = deserialize_keras_object(
quantize_provider,
module_objects=globals(),
custom_objects=custom_objects)
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
layer = deserialize_layer(config.pop('layer'))
config['layer'] = layer
return cls(**config)
def from_config(cls, config):
config = config.copy()
pruning_schedule = config.pop('pruning_schedule')
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object # pylint: disable=g-import-not-at-top
# TODO(pulkitb): This should ideally be fetched from pruning_schedule,
# which should maintain a list of all the pruning_schedules.
custom_objects = {
'ConstantSparsity': pruning_sched.ConstantSparsity,
'PolynomialDecay': pruning_sched.PolynomialDecay
}
config['pruning_schedule'] = deserialize_keras_object(
pruning_schedule,
module_objects=globals(),
custom_objects=custom_objects)
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
layer = deserialize_layer(config.pop('layer'))
config['layer'] = layer
return cls(**config)
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
layer = deserialize_layer(config.pop('layer'),
custom_objects=custom_objects)
return cls(layer, **config)
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer
cell = deserialize_layer(config.pop('cell'))
return cls(cell, **config)
def from_config(cls, config, custom_objects=None):
config = config.copy()
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
cell = deserialize_layer(config.pop("cell"),
custom_objects=custom_objects)
return cls(cell, **config)
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
layer = deserialize_layer(
config.pop('layer'), custom_objects=custom_objects)
return cls(layer, **config)