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Update rnn_cell.DeviceWrapper to be 2.0 compatible.

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This was the last symbol under tf.nn.rnn_cell, which is now deleted.

PiperOrigin-RevId: 236138948
This commit is contained in:
Scott Zhu 2019-02-28 09:41:49 -08:00 committed by TensorFlower Gardener
parent 8b91877f66
commit 42a1de008f
12 changed files with 117 additions and 128 deletions
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40
tensorflow/python/kernel_tests/rnn_cell_test.py
View File

@ -2861,21 +2861,20 @@ class RNNCellTest(test.TestCase, parameterized.TestCase):
# States are left untouched
self.assertAllClose(res_m_new, res_m_new_res)
@test_util.run_v1_only("b/124229375")
def testDeviceWrapper(self):
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)):
x = array_ops.zeros([1, 3])
m = array_ops.zeros([1, 3])
wrapped = rnn_cell_impl.GRUCell(3)
cell = rnn_cell_impl.DeviceWrapper(wrapped, "/cpu:14159")
(name, dep), = cell._checkpoint_dependencies
cell.get_config() # Should not throw an error
self.assertIs(dep, wrapped)
self.assertEqual("cell", name)
@parameterized.parameters(
[rnn_cell_impl.DeviceWrapper, rnn_cell_impl.DeviceWrapperV2])
def testDeviceWrapper(self, wrapper_type):
x = array_ops.zeros([1, 3])
m = array_ops.zeros([1, 3])
cell = rnn_cell_impl.GRUCell(3)
wrapped_cell = wrapper_type(cell, "/cpu:0")
(name, dep), = wrapped_cell._checkpoint_dependencies
wrapped_cell.get_config() # Should not throw an error
self.assertIs(dep, cell)
self.assertEqual("cell", name)
outputs, _ = cell(x, m)
self.assertTrue("cpu:14159" in outputs.device.lower())
outputs, _ = wrapped_cell(x, m)
self.assertIn("cpu:0", outputs.device.lower())
def _retrieve_cpu_gpu_stats(self, run_metadata):
cpu_stats = None
@ -2975,7 +2974,7 @@ class RNNCellTest(test.TestCase, parameterized.TestCase):
def testWrapperKerasStyle(self, wrapper, wrapper_v2):
"""Tests if wrapper cell is instantiated in keras style scope."""
wrapped_cell_v2 = wrapper_v2(rnn_cell_impl.BasicRNNCell(1))
self.assertTrue(wrapped_cell_v2._keras_style)
self.assertIsNone(getattr(wrapped_cell_v2, "_keras_style", None))
wrapped_cell = wrapper(rnn_cell_impl.BasicRNNCell(1))
self.assertFalse(wrapped_cell._keras_style)
@ -3014,22 +3013,21 @@ class RNNCellTest(test.TestCase, parameterized.TestCase):
@test_util.run_in_graph_and_eager_modes
def testWrapperWeights(self, wrapper):
"""Tests that wrapper weights contain wrapped cells weights."""
with base_layer.keras_style_scope():
base_cell = rnn_cell_impl.BasicRNNCell(1, name="basic_rnn_cell")
base_cell = keras_layers.SimpleRNNCell(1, name="basic_rnn_cell")
rnn_cell = wrapper(base_cell)
rnn_layer = keras_layers.RNN(rnn_cell)
inputs = ops.convert_to_tensor([[[1]]], dtype=dtypes.float32)
rnn_layer(inputs)
expected_weights = ["rnn/" + var for var in ("kernel:0", "bias:0")]
self.assertEqual(len(rnn_cell.weights), 2)
expected_weights = ["rnn/" + var for var in
("kernel:0", "recurrent_kernel:0", "bias:0")]
self.assertEqual(len(rnn_cell.weights), 3)
self.assertCountEqual([v.name for v in rnn_cell.weights], expected_weights)
self.assertCountEqual([v.name for v in rnn_cell.trainable_variables],
expected_weights)
self.assertCountEqual([v.name for v in rnn_cell.non_trainable_variables],
[])
self.assertCountEqual([v.name for v in rnn_cell._cell.weights],
self.assertCountEqual([v.name for v in rnn_cell.cell.weights],
expected_weights)
@parameterized.parameters(

119
tensorflow/python/ops/rnn_cell_impl.py
View File

@ -1097,9 +1097,9 @@ class _RNNCellWrapperV1(RNNCell):
def __init__(self, cell):
super(_RNNCellWrapperV1, self).__init__()
self._cell = cell
self.cell = cell
if isinstance(cell, trackable.Trackable):
self._track_trackable(self._cell, name="cell")
self._track_trackable(self.cell, name="cell")
def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
"""Calls the wrapped cell and performs the wrapping logic.
@ -1143,29 +1143,19 @@ class _RNNCellWrapperV1(RNNCell):
- New state: A tensor or tuple of tensors with new wrapped cell's state.
"""
return self._call_wrapped_cell(
inputs, state, cell_call_fn=self._cell.__call__, scope=scope)
inputs, state, cell_call_fn=self.cell.__call__, scope=scope)
class _RNNCellWrapperV2(keras_layer.AbstractRNNCell, _RNNCellWrapperV1):
class _RNNCellWrapperV2(keras_layer.AbstractRNNCell):
"""Base class for cells wrappers V2 compatibility.
This class along with `_RNNCellWrapperV1` allows to define cells wrappers that
are compatible with V1 and V2, and defines helper methods for this purpose.
"""
def __init__(self, *args, **kwargs):
def __init__(self, cell, *args, **kwargs):
super(_RNNCellWrapperV2, self).__init__(*args, **kwargs)
self._layers = [self._cell]
# Force the keras_style to be true so that it will suppress all the legacy
# behavior from base_layer.
self._keras_style = True
def __call__(self, inputs, *args, **kwargs):
# Override the __call__() so that it does not fall to
# _RNNCellWrapperV1.__call__(), instead, directly use keras base_layer so
# that it will properly invoke the self.call() here.
return keras_layer.Layer.__call__(self, inputs, *args, **kwargs)
self.cell = cell
def call(self, inputs, state, **kwargs):
"""Runs the RNN cell step computation.
@ -1189,16 +1179,15 @@ class _RNNCellWrapperV2(keras_layer.AbstractRNNCell, _RNNCellWrapperV1):
- New state: A tensor or tuple of tensors with new wrapped cell's state.
"""
return self._call_wrapped_cell(
inputs, state, cell_call_fn=self._cell.call, **kwargs)
inputs, state, cell_call_fn=self.cell.call, **kwargs)
def build(self, inputs_shape):
"""Builds the wrapped cell."""
self._cell.build(inputs_shape)
self.cell.build(inputs_shape)
self.built = True
@tf_export(v1=["nn.rnn_cell.DropoutWrapper"])
class DropoutWrapper(_RNNCellWrapperV1):
class DropoutWrapperBase(object):
"""Operator adding dropout to inputs and outputs of the given cell."""
def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
@ -1268,7 +1257,7 @@ class DropoutWrapper(_RNNCellWrapperV1):
but not `callable`.
ValueError: if any of the keep_probs are not between 0 and 1.
"""
super(DropoutWrapper, self).__init__(cell)
super(DropoutWrapperBase, self).__init__(cell)
assert_like_rnncell("cell", cell)
if (dropout_state_filter_visitor is not None
@ -1345,19 +1334,19 @@ class DropoutWrapper(_RNNCellWrapperV1):
@property
def wrapped_cell(self):
return self._cell
return self.cell
@property
def state_size(self):
return self._cell.state_size
return self.cell.state_size
@property
def output_size(self):
return self._cell.output_size
return self.cell.output_size
def zero_state(self, batch_size, dtype):
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
return self._cell.zero_state(batch_size, dtype)
return self.cell.zero_state(batch_size, dtype)
def _variational_recurrent_dropout_value(
self, index, value, noise, keep_prob):
@ -1440,18 +1429,27 @@ class DropoutWrapper(_RNNCellWrapperV1):
return output, new_state
@tf_export("nn.DropoutWrapper", v1=[])
class DropoutWrapperV2(DropoutWrapper, _RNNCellWrapperV2):
@tf_export(v1=["nn.rnn_cell.DropoutWrapper"])
class DropoutWrapper(DropoutWrapperBase, _RNNCellWrapperV1):
"""Operator adding dropout to inputs and outputs of the given cell."""
def __init__(self, *args, **kwargs):
super(DropoutWrapper, self).__init__(*args, **kwargs)
__init__.__doc__ = DropoutWrapperBase.__init__.__doc__
@tf_export("nn.RNNCellDropoutWrapper", v1=[])
class DropoutWrapperV2(DropoutWrapperBase, _RNNCellWrapperV2):
"""Operator adding dropout to inputs and outputs of the given cell."""
def __init__(self, *args, **kwargs):
super(DropoutWrapperV2, self).__init__(*args, **kwargs)
__init__.__doc__ = DropoutWrapper.__init__.__doc__
__init__.__doc__ = DropoutWrapperBase.__init__.__doc__
@tf_export(v1=["nn.rnn_cell.ResidualWrapper"])
class ResidualWrapper(_RNNCellWrapperV1):
class ResidualWrapperBase(object):
"""RNNCell wrapper that ensures cell inputs are added to the outputs."""
def __init__(self, cell, residual_fn=None):
@ -1464,20 +1462,20 @@ class ResidualWrapper(_RNNCellWrapperV1):
Defaults to calling nest.map_structure on (lambda i, o: i + o), inputs
and outputs.
"""
super(ResidualWrapper, self).__init__(cell)
super(ResidualWrapperBase, self).__init__(cell)
self._residual_fn = residual_fn
@property
def state_size(self):
return self._cell.state_size
return self.cell.state_size
@property
def output_size(self):
return self._cell.output_size
return self.cell.output_size
def zero_state(self, batch_size, dtype):
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
return self._cell.zero_state(batch_size, dtype)
return self.cell.zero_state(batch_size, dtype)
def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
"""Run the cell and then apply the residual_fn on its inputs to its outputs.
@ -1508,18 +1506,27 @@ class ResidualWrapper(_RNNCellWrapperV1):
return (res_outputs, new_state)
@tf_export("nn.ResidualWrapper", v1=[])
class ResidualWrapperV2(ResidualWrapper, _RNNCellWrapperV2):
@tf_export(v1=["nn.rnn_cell.ResidualWrapper"])
class ResidualWrapper(ResidualWrapperBase, _RNNCellWrapperV1):
"""RNNCell wrapper that ensures cell inputs are added to the outputs."""
def __init__(self, *args, **kwargs):
super(ResidualWrapper, self).__init__(*args, **kwargs)
__init__.__doc__ = ResidualWrapperBase.__init__.__doc__
@tf_export("nn.RNNCellResidualWrapper", v1=[])
class ResidualWrapperV2(ResidualWrapperBase, _RNNCellWrapperV2):
"""RNNCell wrapper that ensures cell inputs are added to the outputs."""
def __init__(self, *args, **kwargs):
super(ResidualWrapperV2, self).__init__(*args, **kwargs)
__init__.__doc__ = ResidualWrapper.__init__.__doc__
__init__.__doc__ = ResidualWrapperBase.__init__.__doc__
@tf_export("nn.rnn_cell.DeviceWrapper")
class DeviceWrapper(RNNCell):
class DeviceWrapperBase(object):
"""Operator that ensures an RNNCell runs on a particular device."""
def __init__(self, cell, device):
@ -1531,29 +1538,45 @@ class DeviceWrapper(RNNCell):
cell: An instance of `RNNCell`.
device: A device string or function, for passing to `tf.device`.
"""
super(DeviceWrapper, self).__init__()
self._cell = cell
if isinstance(cell, trackable.Trackable):
self._track_trackable(self._cell, name="cell")
super(DeviceWrapperBase, self).__init__(cell)
self._device = device
@property
def state_size(self):
return self._cell.state_size
return self.cell.state_size
@property
def output_size(self):
return self._cell.output_size
return self.cell.output_size
def zero_state(self, batch_size, dtype):
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
with ops.device(self._device):
return self._cell.zero_state(batch_size, dtype)
return self.cell.zero_state(batch_size, dtype)
def __call__(self, inputs, state, scope=None):
def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
"""Run the cell on specified device."""
with ops.device(self._device):
return self._cell(inputs, state, scope=scope)
return cell_call_fn(inputs, state, **kwargs)
@tf_export(v1=["nn.rnn_cell.DeviceWrapper"])
class DeviceWrapper(DeviceWrapperBase, _RNNCellWrapperV1):
def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation
super(DeviceWrapper, self).__init__(*args, **kwargs)
__init__.__doc__ = DeviceWrapperBase.__init__.__doc__
@tf_export("nn.RNNCellDeviceWrapper", v1=[])
class DeviceWrapperV2(DeviceWrapperBase, _RNNCellWrapperV2):
"""Operator that ensures an RNNCell runs on a particular device."""
def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation
super(DeviceWrapperV2, self).__init__(*args, **kwargs)
__init__.__doc__ = DeviceWrapperBase.__init__.__doc__
@tf_export(v1=["nn.rnn_cell.MultiRNNCell"])

1
tensorflow/python/tools/api/generator/api_init_files.bzl
View File

@ -38,7 +38,6 @@ TENSORFLOW_API_INIT_FILES = [
"math/__init__.py",
"nest/__init__.py",
"nn/__init__.py",
"nn/rnn_cell/__init__.py",
"quantization/__init__.py",
"ragged/__init__.py",
"random/__init__.py",

4
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
View File

@ -1,6 +1,8 @@
path: "tensorflow.nn.rnn_cell.DeviceWrapper"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DeviceWrapper\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DeviceWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
@ -104,7 +106,7 @@ tf_class {
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'cell\', \'device\'], varargs=None, keywords=None, defaults=None"
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "add_loss"

3
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
View File

@ -1,6 +1,7 @@
path: "tensorflow.nn.rnn_cell.DropoutWrapper"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DropoutWrapper\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DropoutWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
@ -109,7 +110,7 @@ tf_class {
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'cell\', \'input_keep_prob\', \'output_keep_prob\', \'state_keep_prob\', \'variational_recurrent\', \'input_size\', \'dtype\', \'seed\', \'dropout_state_filter_visitor\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1.0\', \'1.0\', \'False\', \'None\', \'None\', \'None\', \'None\'], "
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "add_loss"

3
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
View File

@ -1,6 +1,7 @@
path: "tensorflow.nn.rnn_cell.ResidualWrapper"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.ResidualWrapper\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.ResidualWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
@ -105,7 +106,7 @@ tf_class {
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'cell\', \'residual_fn\'], varargs=None, keywords=None, defaults=[\'None\'], "
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "add_loss"

25
tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt → tensorflow/tools/api/golden/v2/tensorflow.nn.-r-n-n-cell-device-wrapper.pbtxt
View File

@ -1,8 +1,9 @@
path: "tensorflow.nn.rnn_cell.DeviceWrapper"
path: "tensorflow.nn.RNNCellDeviceWrapper"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DeviceWrapper\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DeviceWrapperV2\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DeviceWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV2\'>"
is_instance: "<class \'tensorflow.python.keras.layers.recurrent.AbstractRNNCell\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
is_instance: "<type \'object\'>"
@ -18,10 +19,6 @@ tf_class {
name: "dynamic"
mtype: "<type \'property\'>"
}
member {
name: "graph"
mtype: "<type \'property\'>"
}
member {
name: "inbound_nodes"
mtype: "<type \'property\'>"
@ -74,10 +71,6 @@ tf_class {
name: "output_size"
mtype: "<type \'property\'>"
}
member {
name: "scope_name"
mtype: "<type \'property\'>"
}
member {
name: "state_size"
mtype: "<type \'property\'>"
@ -104,7 +97,7 @@ tf_class {
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'cell\', \'device\'], varargs=None, keywords=None, defaults=None"
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "add_loss"
@ -124,7 +117,7 @@ tf_class {
}
member_method {
name: "add_weight"
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
}
member_method {
name: "apply"
@ -132,11 +125,11 @@ tf_class {
}
member_method {
name: "build"
argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None"
argspec: "args=[\'self\', \'inputs_shape\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "call"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
argspec: "args=[\'self\', \'inputs\', \'state\'], varargs=None, keywords=kwargs, defaults=None"
}
member_method {
name: "compute_mask"

17
tensorflow/tools/api/golden/v2/tensorflow.nn.-dropout-wrapper.pbtxt → tensorflow/tools/api/golden/v2/tensorflow.nn.-r-n-n-cell-dropout-wrapper.pbtxt
View File

@ -1,12 +1,9 @@
path: "tensorflow.nn.DropoutWrapper"
path: "tensorflow.nn.RNNCellDropoutWrapper"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DropoutWrapperV2\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DropoutWrapper\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DropoutWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV2\'>"
is_instance: "<class \'tensorflow.python.keras.layers.recurrent.AbstractRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
is_instance: "<type \'object\'>"
@ -22,10 +19,6 @@ tf_class {
name: "dynamic"
mtype: "<type \'property\'>"
}
member {
name: "graph"
mtype: "<type \'property\'>"
}
member {
name: "inbound_nodes"
mtype: "<type \'property\'>"
@ -78,10 +71,6 @@ tf_class {
name: "output_size"
mtype: "<type \'property\'>"
}
member {
name: "scope_name"
mtype: "<type \'property\'>"
}
member {
name: "state_size"
mtype: "<type \'property\'>"
@ -132,7 +121,7 @@ tf_class {
}
member_method {
name: "add_weight"
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
}
member_method {
name: "apply"

17
tensorflow/tools/api/golden/v2/tensorflow.nn.-residual-wrapper.pbtxt → tensorflow/tools/api/golden/v2/tensorflow.nn.-r-n-n-cell-residual-wrapper.pbtxt
View File

@ -1,12 +1,9 @@
path: "tensorflow.nn.ResidualWrapper"
path: "tensorflow.nn.RNNCellResidualWrapper"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.ResidualWrapperV2\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.ResidualWrapper\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.ResidualWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV2\'>"
is_instance: "<class \'tensorflow.python.keras.layers.recurrent.AbstractRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
is_instance: "<type \'object\'>"
@ -22,10 +19,6 @@ tf_class {
name: "dynamic"
mtype: "<type \'property\'>"
}
member {
name: "graph"
mtype: "<type \'property\'>"
}
member {
name: "inbound_nodes"
mtype: "<type \'property\'>"
@ -78,10 +71,6 @@ tf_class {
name: "output_size"
mtype: "<type \'property\'>"
}
member {
name: "scope_name"
mtype: "<type \'property\'>"
}
member {
name: "state_size"
mtype: "<type \'property\'>"
@ -128,7 +117,7 @@ tf_class {
}
member_method {
name: "add_weight"
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
}
member_method {
name: "apply"

8
tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt
View File

@ -1,16 +1,16 @@
path: "tensorflow.nn"
tf_module {
member {
name: "DropoutWrapper"
name: "RNNCellDeviceWrapper"
mtype: "<type \'type\'>"
}
member {
name: "ResidualWrapper"
name: "RNNCellDropoutWrapper"
mtype: "<type \'type\'>"
}
member {
name: "rnn_cell"
mtype: "<type \'module\'>"
name: "RNNCellResidualWrapper"
mtype: "<type \'type\'>"
}
member {
name: "swish"

7
tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.pbtxt
View File

@ -1,7 +0,0 @@
path: "tensorflow.nn.rnn_cell"
tf_module {
member {
name: "DeviceWrapper"
mtype: "<type \'type\'>"
}
}

1
tensorflow/tools/compatibility/renames_v2.py
View File

@ -441,6 +441,7 @@ renames = {
'tf.nn.relu_layer': 'tf.compat.v1.nn.relu_layer',
'tf.nn.rnn_cell.BasicLSTMCell': 'tf.compat.v1.nn.rnn_cell.BasicLSTMCell',
'tf.nn.rnn_cell.BasicRNNCell': 'tf.compat.v1.nn.rnn_cell.BasicRNNCell',
'tf.nn.rnn_cell.DeviceWrapper': 'tf.compat.v1.nn.rnn_cell.DeviceWrapper',
'tf.nn.rnn_cell.DropoutWrapper': 'tf.compat.v1.nn.rnn_cell.DropoutWrapper',
'tf.nn.rnn_cell.GRUCell': 'tf.compat.v1.nn.rnn_cell.GRUCell',
'tf.nn.rnn_cell.LSTMCell': 'tf.compat.v1.nn.rnn_cell.LSTMCell',