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Remove the usages of TF private API deprecation.deprecated.

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PiperOrigin-RevId: 331570018
Change-Id: I66f398fdfc61eb414e4683897fe350fbbdd4ea0e
This commit is contained in:
Yanhui Liang 2020-09-14 10:04:13 -07:00 committed by TensorFlower Gardener
parent 835f274efb
commit 499d35942a
14 changed files with 168 additions and 137 deletions
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22
tensorflow/python/keras/backend.py
View File

@ -27,6 +27,7 @@ import json
import os import os
import sys import sys
import threading import threading
import warnings
import weakref import weakref
import numpy as np import numpy as np
@ -425,10 +426,10 @@ def set_learning_phase(value):
Raises: Raises:
ValueError: if `value` is neither `0` nor `1`. ValueError: if `value` is neither `0` nor `1`.
""" """
logging.warning('`tf.keras.backend.set_learning_phase` is deprecated and ' warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
'will be removed after 2020-10-11. To update it, simply ' 'will be removed after 2020-10-11. To update it, simply '
'pass a True/False value to the `training` argument of the ' 'pass a True/False value to the `training` argument of the '
'`__call__` method of your layer or model.') '`__call__` method of your layer or model.')
deprecated_internal_set_learning_phase(value) deprecated_internal_set_learning_phase(value)
@ -483,10 +484,10 @@ def learning_phase_scope(value):
Raises: Raises:
ValueError: if `value` is neither `0` nor `1`. ValueError: if `value` is neither `0` nor `1`.
""" """
logging.warning('`tf.keras.backend.learning_phase_scope` is deprecated and ' warnings.warn('`tf.keras.backend.learning_phase_scope` is deprecated and '
'will be removed after 2020-10-11. To update it, simply ' 'will be removed after 2020-10-11. To update it, simply '
'pass a True/False value to the `training` argument of the ' 'pass a True/False value to the `training` argument of the '
'`__call__` method of your layer or model.') '`__call__` method of your layer or model.')
with deprecated_internal_learning_phase_scope(value): with deprecated_internal_learning_phase_scope(value):
try: try:
yield yield
@ -6018,8 +6019,9 @@ def random_binomial(shape, p=0.0, dtype=None, seed=None):
<tf.Tensor: shape=(2, 3), dtype=float32, numpy=..., <tf.Tensor: shape=(2, 3), dtype=float32, numpy=...,
dtype=float32)> dtype=float32)>
""" """
logging.warning('`tf.keras.backend.random_binomial` is deprecated. ' warnings.warn('`tf.keras.backend.random_binomial` is deprecated, '
'Please use `tf.keras.backend.random_bernoulli` instead.') 'and will be removed in a future version.'
'Please use `tf.keras.backend.random_bernoulli` instead.')
return random_bernoulli(shape, p, dtype, seed) return random_bernoulli(shape, p, dtype, seed)

29
tensorflow/python/keras/engine/base_layer.py
View File

@ -23,6 +23,7 @@ import copy
import functools import functools
import itertools import itertools
import threading import threading
import warnings
import weakref import weakref
import numpy as np import numpy as np
@ -79,7 +80,6 @@ from tensorflow.python.training.tracking import data_structures
from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
from tensorflow.python.training.tracking import tracking from tensorflow.python.training.tracking import tracking
from tensorflow.python.util import compat from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest from tensorflow.python.util import nest
from tensorflow.python.util import object_identity from tensorflow.python.util import object_identity
from tensorflow.python.util import tf_inspect from tensorflow.python.util import tf_inspect
@ -1370,12 +1370,11 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
return self.trainable_weights + self.non_trainable_weights return self.trainable_weights + self.non_trainable_weights
@property @property
@deprecation.deprecated(
date=None,
instructions='This property should not be used in TensorFlow 2.0, '
'as updates are applied automatically.')
@doc_controls.do_not_generate_docs @doc_controls.do_not_generate_docs
def updates(self): def updates(self):
warnings.warn('`layer.updates` will be removed in a future version. '
'This property should not be used in TensorFlow 2.0, '
'as `updates` are applied automatically.')
if keras_tensor.keras_tensors_enabled(): if keras_tensor.keras_tensors_enabled():
return [] return []
@ -1895,8 +1894,6 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
output_weights.append(weight) output_weights.append(weight)
return backend.batch_get_value(output_weights) return backend.batch_get_value(output_weights)
@deprecation.deprecated(
date=None, instructions='Please use `layer.updates` instead.')
@doc_controls.do_not_generate_docs @doc_controls.do_not_generate_docs
def get_updates_for(self, inputs): def get_updates_for(self, inputs):
"""Deprecated, do NOT use! """Deprecated, do NOT use!
@ -1909,10 +1906,11 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
Returns: Returns:
List of update ops of the layer that depend on `inputs`. List of update ops of the layer that depend on `inputs`.
""" """
warnings.warn('`layer.get_updates_for` is deprecated and '
'will be removed in a future version. '
'Please use `layer.updates` method instead.')
return self.updates return self.updates
@deprecation.deprecated(
date=None, instructions='Please use `layer.losses` instead.')
@doc_controls.do_not_generate_docs @doc_controls.do_not_generate_docs
def get_losses_for(self, inputs): def get_losses_for(self, inputs):
"""Deprecated, do NOT use! """Deprecated, do NOT use!
@ -1925,6 +1923,9 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
Returns: Returns:
List of loss tensors of the layer that depend on `inputs`. List of loss tensors of the layer that depend on `inputs`.
""" """
warnings.warn('`layer.get_losses_for` is deprecated and '
'will be removed in a future version. '
'Please use `layer.losses` instead.')
return self.losses return self.losses
@doc_controls.do_not_doc_inheritable @doc_controls.do_not_doc_inheritable
@ -2229,8 +2230,6 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
# Methods & attributes below are public aliases of other methods. # # Methods & attributes below are public aliases of other methods. #
############################################################################## ##############################################################################
@deprecation.deprecated(
date=None, instructions='Please use `layer.__call__` method instead.')
@doc_controls.do_not_doc_inheritable @doc_controls.do_not_doc_inheritable
def apply(self, inputs, *args, **kwargs): def apply(self, inputs, *args, **kwargs):
"""Deprecated, do NOT use! """Deprecated, do NOT use!
@ -2245,13 +2244,17 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
Returns: Returns:
Output tensor(s). Output tensor(s).
""" """
warnings.warn('`layer.apply` is deprecated and '
'will be removed in a future version. '
'Please use `layer.__call__` method instead.')
return self.__call__(inputs, *args, **kwargs) return self.__call__(inputs, *args, **kwargs)
@deprecation.deprecated(
date=None, instructions='Please use `layer.add_weight` method instead.')
@doc_controls.do_not_doc_inheritable @doc_controls.do_not_doc_inheritable
def add_variable(self, *args, **kwargs): def add_variable(self, *args, **kwargs):
"""Deprecated, do NOT use! Alias for `add_weight`.""" """Deprecated, do NOT use! Alias for `add_weight`."""
warnings.warn('`layer.add_variable` is deprecated and '
'will be removed in a future version. '
'Please use `layer.add_weight` method instead.')
return self.add_weight(*args, **kwargs) return self.add_weight(*args, **kwargs)
@property @property

12
tensorflow/python/keras/engine/base_layer_v1.py
View File

@ -22,6 +22,7 @@ import collections
import functools import functools
import itertools import itertools
import threading import threading
import warnings
import numpy as np import numpy as np
import six import six
@ -66,7 +67,6 @@ from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.training.tracking import data_structures from tensorflow.python.training.tracking import data_structures
from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
from tensorflow.python.training.tracking import tracking from tensorflow.python.training.tracking import tracking
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest from tensorflow.python.util import nest
from tensorflow.python.util import object_identity from tensorflow.python.util import object_identity
from tensorflow.python.util import tf_inspect from tensorflow.python.util import tf_inspect
@ -1694,8 +1694,6 @@ class Layer(base_layer.Layer):
# Methods & attributes below are public aliases of other methods. # # Methods & attributes below are public aliases of other methods. #
############################################################################## ##############################################################################
@deprecation.deprecated(
date=None, instructions='Please use `layer.__call__` method instead.')
@doc_controls.do_not_doc_inheritable @doc_controls.do_not_doc_inheritable
def apply(self, inputs, *args, **kwargs): def apply(self, inputs, *args, **kwargs):
"""Deprecated, do NOT use! """Deprecated, do NOT use!
@ -1710,13 +1708,17 @@ class Layer(base_layer.Layer):
Returns: Returns:
Output tensor(s). Output tensor(s).
""" """
warnings.warn('`layer.apply` is deprecated and '
'will be removed in a future version. '
'Please use `layer.__call__` method instead.')
return self.__call__(inputs, *args, **kwargs) return self.__call__(inputs, *args, **kwargs)
@deprecation.deprecated(
date=None, instructions='Please use `layer.add_weight` method instead.')
@doc_controls.do_not_doc_inheritable @doc_controls.do_not_doc_inheritable
def add_variable(self, *args, **kwargs): def add_variable(self, *args, **kwargs):
"""Deprecated, do NOT use! Alias for `add_weight`.""" """Deprecated, do NOT use! Alias for `add_weight`."""
warnings.warn('`layer.add_variable` is deprecated and '
'will be removed in a future version. '
'Please use `layer.add_weight` method instead.')
return self.add_weight(*args, **kwargs) return self.add_weight(*args, **kwargs)
@property @property

25
tensorflow/python/keras/engine/sequential.py
View File

@ -20,6 +20,7 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import copy import copy
import warnings
from tensorflow.python import tf2 from tensorflow.python import tf2
from tensorflow.python.framework import ops from tensorflow.python.framework import ops
@ -421,9 +422,9 @@ class Sequential(functional.Functional):
Returns: Returns:
A Numpy array of probability predictions. A Numpy array of probability predictions.
""" """
logging.warning('`model.predict_proba()` is deprecated and ' warnings.warn('`model.predict_proba()` is deprecated and '
'will be removed after 2021-01-01. ' 'will be removed after 2021-01-01. '
'Please use `model.predict()` instead.') 'Please use `model.predict()` instead.')
preds = self.predict(x, batch_size, verbose) preds = self.predict(x, batch_size, verbose)
if preds.min() < 0. or preds.max() > 1.: if preds.min() < 0. or preds.max() > 1.:
logging.warning('Network returning invalid probability values. ' logging.warning('Network returning invalid probability values. '
@ -446,15 +447,15 @@ class Sequential(functional.Functional):
Returns: Returns:
A numpy array of class predictions. A numpy array of class predictions.
""" """
logging.warning('`model.predict_classes()` is deprecated and ' warnings.warn('`model.predict_classes()` is deprecated and '
'will be removed after 2021-01-01. ' 'will be removed after 2021-01-01. '
'Please use instead:' 'Please use instead:'
'* `np.argmax(model.predict(x), axis=-1)`, ' '* `np.argmax(model.predict(x), axis=-1)`, '
' if your model does multi-class classification ' ' if your model does multi-class classification '
' (e.g. if it uses a `softmax` last-layer activation).' ' (e.g. if it uses a `softmax` last-layer activation).'
'* `(model.predict(x) > 0.5).astype("int32")`, ' '* `(model.predict(x) > 0.5).astype("int32")`, '
' if your model does binary classification ' ' if your model does binary classification '
' (e.g. if it uses a `sigmoid` last-layer activation).') ' (e.g. if it uses a `sigmoid` last-layer activation).')
proba = self.predict(x, batch_size=batch_size, verbose=verbose) proba = self.predict(x, batch_size=batch_size, verbose=verbose)
if proba.shape[-1] > 1: if proba.shape[-1] > 1:
return proba.argmax(axis=-1) return proba.argmax(axis=-1)

24
tensorflow/python/keras/engine/training.py
View File

@ -22,6 +22,7 @@ import copy
import itertools import itertools
import json import json
import os import os
import warnings
import six import six
from tensorflow.python.autograph.lang import directives from tensorflow.python.autograph.lang import directives
@ -72,7 +73,6 @@ from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.training.tracking import data_structures from tensorflow.python.training.tracking import data_structures
from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
from tensorflow.python.training.tracking import util as trackable_utils from tensorflow.python.training.tracking import util as trackable_utils
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest from tensorflow.python.util import nest
from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_decorator
from tensorflow.python.util.tf_export import keras_export from tensorflow.python.util.tf_export import keras_export
@ -1775,8 +1775,6 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
outputs = self.predict_function(iterator) outputs = self.predict_function(iterator)
return tf_utils.to_numpy_or_python_type(outputs) return tf_utils.to_numpy_or_python_type(outputs)
@deprecation.deprecated(
None, 'Please use Model.fit, which supports generators.')
def fit_generator(self, def fit_generator(self,
generator, generator,
steps_per_epoch=None, steps_per_epoch=None,
@ -1798,6 +1796,9 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
`Model.fit` now supports generators, so there is no longer any need to use `Model.fit` now supports generators, so there is no longer any need to use
this endpoint. this endpoint.
""" """
warnings.warn('`Model.fit_generator` is deprecated and '
'will be removed in a future version. '
'Please use `Model.fit`, which supports generators.')
return self.fit( return self.fit(
generator, generator,
steps_per_epoch=steps_per_epoch, steps_per_epoch=steps_per_epoch,
@ -1814,8 +1815,6 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
shuffle=shuffle, shuffle=shuffle,
initial_epoch=initial_epoch) initial_epoch=initial_epoch)
@deprecation.deprecated(
None, 'Please use Model.evaluate, which supports generators.')
def evaluate_generator(self, def evaluate_generator(self,
generator, generator,
steps=None, steps=None,
@ -1830,6 +1829,9 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
`Model.evaluate` now supports generators, so there is no longer any need `Model.evaluate` now supports generators, so there is no longer any need
to use this endpoint. to use this endpoint.
""" """
warnings.warn('`Model.evaluate_generator` is deprecated and '
'will be removed in a future version. '
'Please use `Model.evaluate`, which supports generators.')
self._check_call_args('evaluate_generator') self._check_call_args('evaluate_generator')
return self.evaluate( return self.evaluate(
@ -1841,8 +1843,6 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
verbose=verbose, verbose=verbose,
callbacks=callbacks) callbacks=callbacks)
@deprecation.deprecated(
None, 'Please use Model.predict, which supports generators.')
def predict_generator(self, def predict_generator(self,
generator, generator,
steps=None, steps=None,
@ -1857,6 +1857,9 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
`Model.predict` now supports generators, so there is no longer any need `Model.predict` now supports generators, so there is no longer any need
to use this endpoint. to use this endpoint.
""" """
warnings.warn('`Model.predict_generator` is deprecated and '
'will be removed in a future version. '
'Please use `Model.predict`, which supports generators.')
return self.predict( return self.predict(
generator, generator,
steps=steps, steps=steps,
@ -2270,10 +2273,6 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
layer.reset_states() layer.reset_states()
@property @property
@deprecation.deprecated(
date=None,
instructions='This property should not be used in TensorFlow 2.0, '
'as updates are applied automatically.')
@doc_controls.do_not_generate_docs @doc_controls.do_not_generate_docs
def state_updates(self): def state_updates(self):
"""Deprecated, do NOT use! """Deprecated, do NOT use!
@ -2287,6 +2286,9 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
Returns: Returns:
A list of update ops. A list of update ops.
""" """
warnings.warn('`Model.state_updates` will be removed in a future version. '
'This property should not be used in TensorFlow 2.0, '
'as `updates` are applied automatically.')
state_updates = [] state_updates = []
for layer in self.layers: for layer in self.layers:
if getattr(layer, 'stateful', False): if getattr(layer, 'stateful', False):

17
tensorflow/python/keras/engine/training_v1.py
View File

@ -18,6 +18,7 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import collections import collections
import warnings
import numpy as np import numpy as np
@ -61,7 +62,6 @@ from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
from tensorflow.python.types import core from tensorflow.python.types import core
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest from tensorflow.python.util import nest
from tensorflow.python.util import tf_inspect from tensorflow.python.util import tf_inspect
from tensorflow.python.util.compat import collections_abc from tensorflow.python.util.compat import collections_abc
@ -1211,8 +1211,6 @@ class Model(training_lib.Model):
return outputs[0] return outputs[0]
return outputs return outputs
@deprecation.deprecated(
None, 'Please use Model.fit, which supports generators.')
def fit_generator(self, def fit_generator(self,
generator, generator,
steps_per_epoch=None, steps_per_epoch=None,
@ -1234,6 +1232,9 @@ class Model(training_lib.Model):
`Model.fit` now supports generators, so there is no longer any need to use `Model.fit` now supports generators, so there is no longer any need to use
this endpoint. this endpoint.
""" """
warnings.warn('`model.fit_generator` is deprecated and '
'will be removed in a future version. '
'Please use `Model.fit`, which supports generators.')
return self.fit( return self.fit(
generator, generator,
steps_per_epoch=steps_per_epoch, steps_per_epoch=steps_per_epoch,
@ -1250,8 +1251,6 @@ class Model(training_lib.Model):
shuffle=shuffle, shuffle=shuffle,
initial_epoch=initial_epoch) initial_epoch=initial_epoch)
@deprecation.deprecated(
None, 'Please use Model.evaluate, which supports generators.')
def evaluate_generator(self, def evaluate_generator(self,
generator, generator,
steps=None, steps=None,
@ -1266,6 +1265,9 @@ class Model(training_lib.Model):
`Model.evaluate` now supports generators, so there is no longer any need `Model.evaluate` now supports generators, so there is no longer any need
to use this endpoint. to use this endpoint.
""" """
warnings.warn('`Model.evaluate_generator` is deprecated and '
'will be removed in a future version. '
'Please use `Model.evaluate`, which supports generators.')
self._check_call_args('evaluate_generator') self._check_call_args('evaluate_generator')
return self.evaluate( return self.evaluate(
@ -1277,8 +1279,6 @@ class Model(training_lib.Model):
verbose=verbose, verbose=verbose,
callbacks=callbacks) callbacks=callbacks)
@deprecation.deprecated(
None, 'Please use Model.predict, which supports generators.')
def predict_generator(self, def predict_generator(self,
generator, generator,
steps=None, steps=None,
@ -1293,6 +1293,9 @@ class Model(training_lib.Model):
`Model.predict` now supports generators, so there is no longer any need `Model.predict` now supports generators, so there is no longer any need
to use this endpoint. to use this endpoint.
""" """
warnings.warn('`Model.predict_generator` is deprecated and '
'will be removed in a future version. '
'Please use `Model.predict`, which supports generators.')
return self.predict( return self.predict(
generator, generator,
steps=steps, steps=steps,

29
tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py
View File

@ -25,6 +25,7 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import collections import collections
import warnings
from tensorflow.python.eager import context from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op from tensorflow.python.framework import constant_op
@ -416,9 +417,10 @@ class BasicRNNCell(LayerRNNCell):
name=None, name=None,
dtype=None, dtype=None,
**kwargs): **kwargs):
logging.warning("`tf.nn.rnn_cell.BasicRNNCell` is deprecated. This class " warnings.warn("`tf.nn.rnn_cell.BasicRNNCell` is deprecated and will be "
"is equivalent as `tf.keras.layers.SimpleRNNCell`, " "removed in a future version. This class "
"and will be replaced by that in Tensorflow 2.0.") "is equivalent as `tf.keras.layers.SimpleRNNCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(BasicRNNCell, self).__init__( super(BasicRNNCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs) _reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype) _check_supported_dtypes(self.dtype)
@ -523,9 +525,10 @@ class GRUCell(LayerRNNCell):
name=None, name=None,
dtype=None, dtype=None,
**kwargs): **kwargs):
logging.warning("`tf.nn.rnn_cell.GRUCell` is deprecated. This class " warnings.warn("`tf.nn.rnn_cell.GRUCell` is deprecated and will be removed "
"is equivalent as `tf.keras.layers.GRUCell`, " "in a future version. This class "
"and will be replaced by that in Tensorflow 2.0.") "is equivalent as `tf.keras.layers.GRUCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(GRUCell, self).__init__( super(GRUCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs) _reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype) _check_supported_dtypes(self.dtype)
@ -695,9 +698,10 @@ class BasicLSTMCell(LayerRNNCell):
When restoring from CudnnLSTM-trained checkpoints, must use When restoring from CudnnLSTM-trained checkpoints, must use
`CudnnCompatibleLSTMCell` instead. `CudnnCompatibleLSTMCell` instead.
""" """
logging.warning("`tf.nn.rnn_cell.BasicLSTMCell` is deprecated. This class " warnings.warn("`tf.nn.rnn_cell.BasicLSTMCell` is deprecated and will be "
"is equivalent as `tf.keras.layers.LSTMCell`, " "removed in a future version. This class "
"and will be replaced by that in Tensorflow 2.0.") "is equivalent as `tf.keras.layers.LSTMCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(BasicLSTMCell, self).__init__( super(BasicLSTMCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs) _reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype) _check_supported_dtypes(self.dtype)
@ -895,9 +899,10 @@ class LSTMCell(LayerRNNCell):
When restoring from CudnnLSTM-trained checkpoints, use When restoring from CudnnLSTM-trained checkpoints, use
`CudnnCompatibleLSTMCell` instead. `CudnnCompatibleLSTMCell` instead.
""" """
logging.warning("`tf.nn.rnn_cell.LSTMCell` is deprecated. This class " warnings.warn("`tf.nn.rnn_cell.LSTMCell` is deprecated and will be "
"is equivalent as `tf.keras.layers.LSTMCell`, " "removed in a future version. This class "
"and will be replaced by that in Tensorflow 2.0.") "is equivalent as `tf.keras.layers.LSTMCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(LSTMCell, self).__init__( super(LSTMCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs) _reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype) _check_supported_dtypes(self.dtype)

9
tensorflow/python/keras/layers/recurrent.py
View File

@ -19,6 +19,8 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import warnings
import numpy as np import numpy as np
from tensorflow.python.distribute import distribution_strategy_context as ds_context from tensorflow.python.distribute import distribution_strategy_context as ds_context
@ -43,7 +45,6 @@ from tensorflow.python.ops import state_ops
from tensorflow.python.platform import tf_logging as logging from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.training.tracking import data_structures from tensorflow.python.training.tracking import data_structures
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest from tensorflow.python.util import nest
from tensorflow.python.util.compat import collections_abc from tensorflow.python.util.compat import collections_abc
from tensorflow.python.util.tf_export import keras_export from tensorflow.python.util.tf_export import keras_export
@ -2545,8 +2546,6 @@ class PeepholeLSTMCell(LSTMCell):
``` ```
""" """
@deprecation.deprecated(
None, 'Please use tensorflow_addons.rnn.PeepholeLSTMCell instead')
def __init__(self, def __init__(self,
units, units,
activation='tanh', activation='tanh',
@ -2565,6 +2564,10 @@ class PeepholeLSTMCell(LSTMCell):
dropout=0., dropout=0.,
recurrent_dropout=0., recurrent_dropout=0.,
**kwargs): **kwargs):
warnings.warn('`tf.keras.experimental.PeepholeLSTMCell` is deprecated '
'and will be removed in a future version. '
'Please use tensorflow_addons.rnn.PeepholeLSTMCell '
'instead.')
super(PeepholeLSTMCell, self).__init__( super(PeepholeLSTMCell, self).__init__(
units=units, units=units,
activation=activation, activation=activation,

10
tensorflow/python/keras/legacy_tf_layers/base.py
View File

@ -18,6 +18,7 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import copy import copy
import warnings
from tensorflow.python.eager import context from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes from tensorflow.python.framework import dtypes
@ -29,7 +30,6 @@ from tensorflow.python.keras.mixed_precision.experimental import policy
from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as tf_variables from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.util import deprecation
from tensorflow.python.util import function_utils from tensorflow.python.util import function_utils
from tensorflow.python.util import nest from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_contextlib
@ -237,11 +237,11 @@ class Layer(base_layer.Layer):
# We no longer track graph in tf.layers layers. This property is only kept to # We no longer track graph in tf.layers layers. This property is only kept to
# maintain API backward compatibility. # maintain API backward compatibility.
@property @property
@deprecation.deprecated(
date=None,
instructions='Stop using this property because tf.layers layers no '
'longer track their graph.')
def graph(self): def graph(self):
warnings.warn('`Layer.graph` is deprecated and '
'will be removed in a future version. '
'Please stop using this property because tf.layers layers no '
'longer track their graph.')
if context.executing_eagerly(): if context.executing_eagerly():
raise RuntimeError('Layer.graph not supported when executing eagerly.') raise RuntimeError('Layer.graph not supported when executing eagerly.')
return None return None

45
tensorflow/python/keras/legacy_tf_layers/convolutional.py
View File

@ -19,10 +19,11 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import warnings
from tensorflow.python.keras import layers as keras_layers from tensorflow.python.keras import layers as keras_layers
from tensorflow.python.keras.legacy_tf_layers import base from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.ops import init_ops from tensorflow.python.ops import init_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export from tensorflow.python.util.tf_export import tf_export
@ -118,9 +119,6 @@ class Conv1D(keras_layers.Conv1D, base.Layer):
name=name, **kwargs) name=name, **kwargs)
@deprecation.deprecated(
date=None,
instructions='Use `tf.keras.layers.Conv1D` instead.')
@tf_export(v1=['layers.conv1d']) @tf_export(v1=['layers.conv1d'])
def conv1d(inputs, def conv1d(inputs,
filters, filters,
@ -201,6 +199,9 @@ def conv1d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.conv1d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv1D` instead.')
layer = Conv1D( layer = Conv1D(
filters=filters, filters=filters,
kernel_size=kernel_size, kernel_size=kernel_size,
@ -323,9 +324,6 @@ class Conv2D(keras_layers.Conv2D, base.Layer):
name=name, **kwargs) name=name, **kwargs)
@deprecation.deprecated(
date=None,
instructions='Use `tf.keras.layers.Conv2D` instead.')
@tf_export(v1=['layers.conv2d']) @tf_export(v1=['layers.conv2d'])
def conv2d(inputs, def conv2d(inputs,
filters, filters,
@ -413,6 +411,9 @@ def conv2d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.conv2d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv2D` instead.')
layer = Conv2D( layer = Conv2D(
filters=filters, filters=filters,
kernel_size=kernel_size, kernel_size=kernel_size,
@ -536,9 +537,6 @@ class Conv3D(keras_layers.Conv3D, base.Layer):
name=name, **kwargs) name=name, **kwargs)
@deprecation.deprecated(
date=None,
instructions='Use `tf.keras.layers.Conv3D` instead.')
@tf_export(v1=['layers.conv3d']) @tf_export(v1=['layers.conv3d'])
def conv3d(inputs, def conv3d(inputs,
filters, filters,
@ -627,6 +625,9 @@ def conv3d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.conv3d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv3D` instead.')
layer = Conv3D( layer = Conv3D(
filters=filters, filters=filters,
kernel_size=kernel_size, kernel_size=kernel_size,
@ -875,9 +876,6 @@ class SeparableConv2D(keras_layers.SeparableConv2D, base.Layer):
**kwargs) **kwargs)
@deprecation.deprecated(
date=None,
instructions='Use `tf.keras.layers.SeparableConv1D` instead.')
@tf_export(v1=['layers.separable_conv1d']) @tf_export(v1=['layers.separable_conv1d'])
def separable_conv1d(inputs, def separable_conv1d(inputs,
filters, filters,
@ -971,6 +969,9 @@ def separable_conv1d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.separable_conv1d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.SeparableConv1D` instead.')
layer = SeparableConv1D( layer = SeparableConv1D(
filters=filters, filters=filters,
kernel_size=kernel_size, kernel_size=kernel_size,
@ -998,9 +999,6 @@ def separable_conv1d(inputs,
return layer.apply(inputs) return layer.apply(inputs)
@deprecation.deprecated(
date=None,
instructions='Use `tf.keras.layers.SeparableConv2D` instead.')
@tf_export(v1=['layers.separable_conv2d']) @tf_export(v1=['layers.separable_conv2d'])
def separable_conv2d(inputs, def separable_conv2d(inputs,
filters, filters,
@ -1099,6 +1097,9 @@ def separable_conv2d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.separable_conv2d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.SeparableConv2D` instead.')
layer = SeparableConv2D( layer = SeparableConv2D(
filters=filters, filters=filters,
kernel_size=kernel_size, kernel_size=kernel_size,
@ -1214,9 +1215,6 @@ class Conv2DTranspose(keras_layers.Conv2DTranspose, base.Layer):
**kwargs) **kwargs)
@deprecation.deprecated(
date=None,
instructions='Use `tf.keras.layers.Conv2DTranspose` instead.')
@tf_export(v1=['layers.conv2d_transpose']) @tf_export(v1=['layers.conv2d_transpose'])
def conv2d_transpose(inputs, def conv2d_transpose(inputs,
filters, filters,
@ -1293,6 +1291,9 @@ def conv2d_transpose(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.conv2d_transpose` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv2DTranspose` instead.')
layer = Conv2DTranspose( layer = Conv2DTranspose(
filters=filters, filters=filters,
kernel_size=kernel_size, kernel_size=kernel_size,
@ -1400,9 +1401,6 @@ class Conv3DTranspose(keras_layers.Conv3DTranspose, base.Layer):
**kwargs) **kwargs)
@deprecation.deprecated(
date=None,
instructions='Use `tf.keras.layers.Conv3DTranspose` instead.')
@tf_export(v1=['layers.conv3d_transpose']) @tf_export(v1=['layers.conv3d_transpose'])
def conv3d_transpose(inputs, def conv3d_transpose(inputs,
filters, filters,
@ -1473,6 +1471,9 @@ def conv3d_transpose(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.conv3d_transpose` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv3DTranspose` instead.')
layer = Conv3DTranspose( layer = Conv3DTranspose(
filters=filters, filters=filters,
kernel_size=kernel_size, kernel_size=kernel_size,

19
tensorflow/python/keras/legacy_tf_layers/core.py
View File

@ -21,11 +21,11 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import warnings
from tensorflow.python.keras import layers as keras_layers from tensorflow.python.keras import layers as keras_layers
from tensorflow.python.keras.legacy_tf_layers import base from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.ops import init_ops from tensorflow.python.ops import init_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export from tensorflow.python.util.tf_export import tf_export
@ -110,8 +110,6 @@ class Dense(keras_layers.Dense, base.Layer):
**kwargs) **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.Dense instead.')
@tf_export(v1=['layers.dense']) @tf_export(v1=['layers.dense'])
def dense( def dense(
inputs, units, inputs, units,
@ -170,6 +168,9 @@ def dense(
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.dense` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.Dense` instead.')
layer = Dense(units, layer = Dense(units,
activation=activation, activation=activation,
use_bias=use_bias, use_bias=use_bias,
@ -226,9 +227,6 @@ class Dropout(keras_layers.Dropout, base.Layer):
return super(Dropout, self).call(inputs, training=training) return super(Dropout, self).call(inputs, training=training)
@deprecation.deprecated(
date=None,
instructions='Use keras.layers.dropout instead.')
@tf_export(v1=['layers.dropout']) @tf_export(v1=['layers.dropout'])
def dropout(inputs, def dropout(inputs,
rate=0.5, rate=0.5,
@ -267,6 +265,9 @@ def dropout(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.dropout` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.Dropout` instead.')
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name) layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
return layer.apply(inputs, training=training) return layer.apply(inputs, training=training)
@ -297,9 +298,6 @@ class Flatten(keras_layers.Flatten, base.Layer):
pass pass
@deprecation.deprecated(
date=None,
instructions='Use keras.layers.Flatten instead.')
@tf_export(v1=['layers.flatten']) @tf_export(v1=['layers.flatten'])
def flatten(inputs, name=None, data_format='channels_last'): def flatten(inputs, name=None, data_format='channels_last'):
"""Flattens an input tensor while preserving the batch axis (axis 0). """Flattens an input tensor while preserving the batch axis (axis 0).
@ -328,6 +326,9 @@ def flatten(inputs, name=None, data_format='channels_last'):
# now `y` has shape `(None, None)` # now `y` has shape `(None, None)`
``` ```
""" """
warnings.warn('`tf.layers.flatten` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.Flatten` instead.')
layer = Flatten(name=name, data_format=data_format) layer = Flatten(name=name, data_format=data_format)
return layer.apply(inputs) return layer.apply(inputs)

14
tensorflow/python/keras/legacy_tf_layers/normalization.py
View File

@ -19,11 +19,11 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import warnings
from tensorflow.python.keras.layers import normalization as keras_normalization from tensorflow.python.keras.layers import normalization as keras_normalization
from tensorflow.python.keras.legacy_tf_layers import base from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.ops import init_ops from tensorflow.python.ops import init_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export from tensorflow.python.util.tf_export import tf_export
@ -172,11 +172,6 @@ class BatchNormalization(keras_normalization.BatchNormalization, base.Layer):
return super(BatchNormalization, self).call(inputs, training=training) return super(BatchNormalization, self).call(inputs, training=training)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.BatchNormalization instead. In '
'particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not '
'be used (consult the `tf.keras.layers.BatchNormalization` '
'documentation).')
@tf_export(v1=['layers.batch_normalization']) @tf_export(v1=['layers.batch_normalization'])
def batch_normalization(inputs, def batch_normalization(inputs,
axis=-1, axis=-1,
@ -309,6 +304,13 @@ def batch_normalization(inputs,
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models) 2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf)) ([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
""" """
warnings.warn(
'`tf.layers.batch_normalization` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.BatchNormalization` instead. '
'In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` '
'should not be used (consult the `tf.keras.layers.BatchNormalization` '
'documentation).')
layer = BatchNormalization( layer = BatchNormalization(
axis=axis, axis=axis,
momentum=momentum, momentum=momentum,

33
tensorflow/python/keras/legacy_tf_layers/pooling.py
View File

@ -19,9 +19,10 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import warnings
from tensorflow.python.keras import layers as keras_layers from tensorflow.python.keras import layers as keras_layers
from tensorflow.python.keras.legacy_tf_layers import base from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export from tensorflow.python.util.tf_export import tf_export
@ -58,8 +59,6 @@ class AveragePooling1D(keras_layers.AveragePooling1D, base.Layer):
**kwargs) **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling1D instead.')
@tf_export(v1=['layers.average_pooling1d']) @tf_export(v1=['layers.average_pooling1d'])
def average_pooling1d(inputs, pool_size, strides, def average_pooling1d(inputs, pool_size, strides,
padding='valid', data_format='channels_last', padding='valid', data_format='channels_last',
@ -87,6 +86,9 @@ def average_pooling1d(inputs, pool_size, strides,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.average_pooling1d` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.AveragePooling1D` instead.')
layer = AveragePooling1D(pool_size=pool_size, layer = AveragePooling1D(pool_size=pool_size,
strides=strides, strides=strides,
padding=padding, padding=padding,
@ -128,8 +130,6 @@ class MaxPooling1D(keras_layers.MaxPooling1D, base.Layer):
**kwargs) **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling1D instead.')
@tf_export(v1=['layers.max_pooling1d']) @tf_export(v1=['layers.max_pooling1d'])
def max_pooling1d(inputs, pool_size, strides, def max_pooling1d(inputs, pool_size, strides,
padding='valid', data_format='channels_last', padding='valid', data_format='channels_last',
@ -157,6 +157,9 @@ def max_pooling1d(inputs, pool_size, strides,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.max_pooling1d` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.MaxPooling1D` instead.')
layer = MaxPooling1D(pool_size=pool_size, layer = MaxPooling1D(pool_size=pool_size,
strides=strides, strides=strides,
padding=padding, padding=padding,
@ -198,8 +201,6 @@ class AveragePooling2D(keras_layers.AveragePooling2D, base.Layer):
padding=padding, data_format=data_format, name=name, **kwargs) padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling2D instead.')
@tf_export(v1=['layers.average_pooling2d']) @tf_export(v1=['layers.average_pooling2d'])
def average_pooling2d(inputs, def average_pooling2d(inputs,
pool_size, strides, pool_size, strides,
@ -232,6 +233,9 @@ def average_pooling2d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.average_pooling2d` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.AveragePooling2D` instead.')
layer = AveragePooling2D(pool_size=pool_size, strides=strides, layer = AveragePooling2D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, padding=padding, data_format=data_format,
name=name) name=name)
@ -271,8 +275,6 @@ class MaxPooling2D(keras_layers.MaxPooling2D, base.Layer):
padding=padding, data_format=data_format, name=name, **kwargs) padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling2D instead.')
@tf_export(v1=['layers.max_pooling2d']) @tf_export(v1=['layers.max_pooling2d'])
def max_pooling2d(inputs, def max_pooling2d(inputs,
pool_size, strides, pool_size, strides,
@ -305,6 +307,9 @@ def max_pooling2d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.max_pooling2d` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.MaxPooling2D` instead.')
layer = MaxPooling2D(pool_size=pool_size, strides=strides, layer = MaxPooling2D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, padding=padding, data_format=data_format,
name=name) name=name)
@ -346,8 +351,6 @@ class AveragePooling3D(keras_layers.AveragePooling3D, base.Layer):
padding=padding, data_format=data_format, name=name, **kwargs) padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling3D instead.')
@tf_export(v1=['layers.average_pooling3d']) @tf_export(v1=['layers.average_pooling3d'])
def average_pooling3d(inputs, def average_pooling3d(inputs,
pool_size, strides, pool_size, strides,
@ -382,6 +385,9 @@ def average_pooling3d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.average_pooling3d` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.AveragePooling3D` instead.')
layer = AveragePooling3D(pool_size=pool_size, strides=strides, layer = AveragePooling3D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, padding=padding, data_format=data_format,
name=name) name=name)
@ -423,8 +429,6 @@ class MaxPooling3D(keras_layers.MaxPooling3D, base.Layer):
padding=padding, data_format=data_format, name=name, **kwargs) padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling3D instead.')
@tf_export(v1=['layers.max_pooling3d']) @tf_export(v1=['layers.max_pooling3d'])
def max_pooling3d(inputs, def max_pooling3d(inputs,
pool_size, strides, pool_size, strides,
@ -457,6 +461,9 @@ def max_pooling3d(inputs,
Raises: Raises:
ValueError: if eager execution is enabled. ValueError: if eager execution is enabled.
""" """
warnings.warn('`tf.layers.max_pooling3d` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.MaxPooling3D` instead.')
layer = MaxPooling3D(pool_size=pool_size, strides=strides, layer = MaxPooling3D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, padding=padding, data_format=data_format,
name=name) name=name)

17
tensorflow/python/keras/saving/saved_model_experimental.py
View File

@ -18,6 +18,7 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import os import os
import warnings
import six import six
@ -41,7 +42,6 @@ from tensorflow.python.saved_model import utils_impl as saved_model_utils
from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import saver as saver_lib
from tensorflow.python.training.tracking import graph_view from tensorflow.python.training.tracking import graph_view
from tensorflow.python.util import compat from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import keras_export from tensorflow.python.util.tf_export import keras_export
@ -61,10 +61,6 @@ sequential = LazyLoader(
# pylint:enable=g-inconsistent-quotes # pylint:enable=g-inconsistent-quotes
@deprecation.deprecated(
date=None,
instructions=('Please use `model.save(..., save_format="tf")` or '
'`tf.keras.models.save_model(..., save_format="tf")`.'))
@keras_export(v1=['keras.experimental.export_saved_model']) @keras_export(v1=['keras.experimental.export_saved_model'])
def export_saved_model(model, def export_saved_model(model,
saved_model_path, saved_model_path,
@ -130,6 +126,10 @@ def export_saved_model(model,
ValueError: If the input signature cannot be inferred from the model. ValueError: If the input signature cannot be inferred from the model.
AssertionError: If the SavedModel directory already exists and isn't empty. AssertionError: If the SavedModel directory already exists and isn't empty.
""" """
warnings.warn('`tf.keras.experimental.export_saved_model` is deprecated'
'and will be removed in a future version. '
'Please use `model.save(..., save_format="tf")` or '
'`tf.keras.models.save_model(..., save_format="tf")`.')
if serving_only: if serving_only:
save_lib.save( save_lib.save(
model, model,
@ -372,10 +372,6 @@ def _assert_same_non_optimizer_objects(model, model_graph, clone, clone_graph):
return True return True
@deprecation.deprecated(
date=None,
instructions=('The experimental save and load functions have been '
'deprecated. Please switch to `tf.keras.models.load_model`.'))
@keras_export(v1=['keras.experimental.load_from_saved_model']) @keras_export(v1=['keras.experimental.load_from_saved_model'])
def load_from_saved_model(saved_model_path, custom_objects=None): def load_from_saved_model(saved_model_path, custom_objects=None):
"""Loads a keras Model from a SavedModel created by `export_saved_model()`. """Loads a keras Model from a SavedModel created by `export_saved_model()`.
@ -413,6 +409,9 @@ def load_from_saved_model(saved_model_path, custom_objects=None):
Returns: Returns:
a keras.Model instance. a keras.Model instance.
""" """
warnings.warn('`tf.keras.experimental.load_from_saved_model` is deprecated'
'and will be removed in a future version. '
'Please switch to `tf.keras.models.load_model`.')
# restore model topology from json string # restore model topology from json string
model_json_filepath = os.path.join( model_json_filepath = os.path.join(
compat.as_bytes(saved_model_path), compat.as_bytes(saved_model_path),