664 lines
24 KiB
Python
664 lines
24 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=protected-access
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"""Wrapper layers: layers that augment the functionality of another layer.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras.engine.base_layer import InputSpec
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from tensorflow.python.keras.engine.base_layer import Layer
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from tensorflow.python.keras.layers.recurrent import _standardize_args
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.keras.utils import tf_utils
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from tensorflow.python.ops import array_ops
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('keras.layers.Wrapper')
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class Wrapper(Layer):
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"""Abstract wrapper base class.
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Wrappers take another layer and augment it in various ways.
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Do not use this class as a layer, it is only an abstract base class.
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Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.
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Arguments:
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layer: The layer to be wrapped.
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"""
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def __init__(self, layer, **kwargs):
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assert isinstance(layer, Layer)
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self.layer = layer
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self._track_checkpointable(layer, name='layer')
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# Tracks mapping of Wrapper inputs to inner layer inputs. Useful when
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# the inner layer has update ops that depend on its inputs (as opposed
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# to the inputs to the Wrapper layer).
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self._input_map = {}
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super(Wrapper, self).__init__(**kwargs)
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def build(self, input_shape=None):
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self.built = True
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@property
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def activity_regularizer(self):
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if hasattr(self.layer, 'activity_regularizer'):
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return self.layer.activity_regularizer
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else:
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return None
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@property
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def trainable(self):
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return self.layer.trainable
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@trainable.setter
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def trainable(self, value):
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self.layer.trainable = value
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@property
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def trainable_weights(self):
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return self.layer.trainable_weights
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@property
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def non_trainable_weights(self):
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return self.layer.non_trainable_weights
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@property
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def updates(self):
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return self.layer.updates + self._updates
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@property
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def losses(self):
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return self.layer.losses + self._losses
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def get_weights(self):
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return self.layer.get_weights()
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def set_weights(self, weights):
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self.layer.set_weights(weights)
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def get_config(self):
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config = {
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'layer': {
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'class_name': self.layer.__class__.__name__,
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'config': self.layer.get_config()
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}
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}
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base_config = super(Wrapper, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@classmethod
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def from_config(cls, config, custom_objects=None):
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from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
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layer = deserialize_layer(
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config.pop('layer'), custom_objects=custom_objects)
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return cls(layer, **config)
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@tf_export('keras.layers.TimeDistributed')
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class TimeDistributed(Wrapper):
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"""This wrapper allows to apply a layer to every temporal slice of an input.
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The input should be at least 3D, and the dimension of index one
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will be considered to be the temporal dimension.
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Consider a batch of 32 samples,
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where each sample is a sequence of 10 vectors of 16 dimensions.
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The batch input shape of the layer is then `(32, 10, 16)`,
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and the `input_shape`, not including the samples dimension, is `(10, 16)`.
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You can then use `TimeDistributed` to apply a `Dense` layer
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to each of the 10 timesteps, independently:
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```python
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# as the first layer in a model
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model = Sequential()
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model.add(TimeDistributed(Dense(8), input_shape=(10, 16)))
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# now model.output_shape == (None, 10, 8)
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```
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The output will then have shape `(32, 10, 8)`.
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In subsequent layers, there is no need for the `input_shape`:
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```python
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model.add(TimeDistributed(Dense(32)))
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# now model.output_shape == (None, 10, 32)
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```
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The output will then have shape `(32, 10, 32)`.
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`TimeDistributed` can be used with arbitrary layers, not just `Dense`,
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for instance with a `Conv2D` layer:
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```python
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model = Sequential()
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model.add(TimeDistributed(Conv2D(64, (3, 3)),
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input_shape=(10, 299, 299, 3)))
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```
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Arguments:
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layer: a layer instance.
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Raises:
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ValueError: If not initialized with a `Layer` instance.
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"""
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def __init__(self, layer, **kwargs):
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if not isinstance(layer, Layer):
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raise ValueError(
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'Please initialize `TimeDistributed` layer with a '
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'`Layer` instance. You passed: {input}'.format(input=layer))
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super(TimeDistributed, self).__init__(layer, **kwargs)
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self.supports_masking = True
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def _get_shape_tuple(self, init_tuple, tensor, start_idx, int_shape=None):
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"""Finds non-specific dimensions in the static shapes.
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The static shapes are replaced with the corresponding dynamic shapes of the
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tensor.
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Arguments:
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init_tuple: a tuple, the first part of the output shape
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tensor: the tensor from which to get the (static and dynamic) shapes
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as the last part of the output shape
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start_idx: int, which indicate the first dimension to take from
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the static shape of the tensor
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int_shape: an alternative static shape to take as the last part
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of the output shape
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Returns:
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The new int_shape with the first part from init_tuple
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and the last part from either `int_shape` (if provided)
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or `tensor.shape`, where every `None` is replaced by
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the corresponding dimension from `tf.shape(tensor)`.
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"""
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# replace all None in int_shape by K.shape
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if int_shape is None:
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int_shape = K.int_shape(tensor)[start_idx:]
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if not any(not s for s in int_shape):
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return init_tuple + tuple(int_shape)
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shape = K.shape(tensor)
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int_shape = list(int_shape)
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for i, s in enumerate(int_shape):
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if not s:
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int_shape[i] = shape[start_idx + i]
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return init_tuple + tuple(int_shape)
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def build(self, input_shape):
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input_shape = tensor_shape.TensorShape(input_shape).as_list()
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assert len(input_shape) >= 3
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self.input_spec = InputSpec(shape=input_shape)
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child_input_shape = [input_shape[0]] + input_shape[2:]
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if not self.layer.built:
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# The base layer class calls a conversion function on the input shape to
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# convert it to a TensorShape. The conversion function requires a
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# tuple which is why we cast the shape.
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self.layer.build(tuple(child_input_shape))
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self.layer.built = True
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super(TimeDistributed, self).build()
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self.built = True
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def compute_output_shape(self, input_shape):
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input_shape = tensor_shape.TensorShape(input_shape).as_list()
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child_input_shape = tensor_shape.TensorShape([input_shape[0]] +
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input_shape[2:])
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child_output_shape = self.layer.compute_output_shape(
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child_input_shape).as_list()
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timesteps = input_shape[1]
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return tensor_shape.TensorShape([child_output_shape[0], timesteps] +
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child_output_shape[1:])
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def call(self, inputs, training=None, mask=None):
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kwargs = {}
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if generic_utils.has_arg(self.layer.call, 'training'):
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kwargs['training'] = training
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uses_learning_phase = False # pylint: disable=redefined-outer-name
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input_shape = K.int_shape(inputs)
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if input_shape[0]:
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# batch size matters, use rnn-based implementation
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def step(x, _):
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global uses_learning_phase # pylint: disable=global-variable-undefined
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output = self.layer.call(x, **kwargs)
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if hasattr(output, '_uses_learning_phase'):
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uses_learning_phase = (output._uses_learning_phase or
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uses_learning_phase)
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return output, []
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_, outputs, _ = K.rnn(
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step,
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inputs,
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initial_states=[],
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input_length=input_shape[1],
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unroll=False)
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y = outputs
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else:
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# No batch size specified, therefore the layer will be able
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# to process batches of any size.
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# We can go with reshape-based implementation for performance.
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input_length = input_shape[1]
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if not input_length:
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input_length = array_ops.shape(inputs)[1]
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inner_input_shape = self._get_shape_tuple((-1,), inputs, 2)
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# Shape: (num_samples * timesteps, ...). And track the
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# transformation in self._input_map.
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input_uid = generic_utils.object_list_uid(inputs)
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inputs = array_ops.reshape(inputs, inner_input_shape)
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self._input_map[input_uid] = inputs
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# (num_samples * timesteps, ...)
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if generic_utils.has_arg(self.layer.call, 'mask') and mask is not None:
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inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
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kwargs['mask'] = K.reshape(mask, inner_mask_shape)
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y = self.layer.call(inputs, **kwargs)
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if hasattr(y, '_uses_learning_phase'):
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uses_learning_phase = y._uses_learning_phase
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# Shape: (num_samples, timesteps, ...)
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output_shape = self.compute_output_shape(input_shape).as_list()
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output_shape = self._get_shape_tuple(
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(-1, input_length), y, 1, output_shape[2:])
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y = array_ops.reshape(y, output_shape)
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# Apply activity regularizer if any:
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if (hasattr(self.layer, 'activity_regularizer') and
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self.layer.activity_regularizer is not None):
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regularization_loss = self.layer.activity_regularizer(y)
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self.add_loss(regularization_loss, inputs)
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if uses_learning_phase:
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y._uses_learning_phase = True
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return y
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def compute_mask(self, inputs, mask=None):
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"""Computes an output mask tensor for Embedding layer.
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This is based on the inputs, mask, and the inner layer.
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If batch size is specified:
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Simply return the input `mask`. (An rnn-based implementation with
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more than one rnn inputs is required but not supported in tf.keras yet.)
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Otherwise we call `compute_mask` of the inner layer at each time step.
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If the output mask at each time step is not `None`:
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(E.g., inner layer is Masking or RNN)
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Concatenate all of them and return the concatenation.
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If the output mask at each time step is `None` and the input mask is not
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`None`:(E.g., inner layer is Dense)
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Reduce the input_mask to 2 dimensions and return it.
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Otherwise (both the output mask and the input mask are `None`):
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(E.g., `mask` is not used at all)
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Return `None`.
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Arguments:
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inputs: Tensor with shape [batch size, timesteps, ...] indicating the
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input to TimeDistributed. If static shape information is available for
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"batch size", `mask` is returned unmodified.
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mask: Either None (indicating no masking) or a Tensor indicating the
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input mask for TimeDistributed. The shape can be static or dynamic.
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Returns:
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Either None (no masking), or a [batch size, timesteps, ...] Tensor with
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an output mask for the TimeDistributed layer with the shape beyond the
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second dimension being the value of the input mask shape(if the computed
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output mask is none), an output mask with the shape beyond the first
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dimension being the value of the mask shape(if mask is not None) or
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output mask with the shape beyond the first dimension being the
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value of the computed output shape.
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"""
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# cases need to call the layer.compute_mask when input_mask is None:
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# Masking layer and Embedding layer with mask_zero
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input_shape = K.int_shape(inputs)
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if input_shape[0]:
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# batch size matters, we currently do not handle mask explicitly
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return mask
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inner_mask = mask
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if inner_mask is not None:
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inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
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inner_mask = K.reshape(inner_mask, inner_mask_shape)
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input_uid = generic_utils.object_list_uid(inputs)
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inner_inputs = self._input_map[input_uid]
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output_mask = self.layer.compute_mask(inner_inputs, inner_mask)
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if output_mask is None:
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if mask is None:
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return None
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# input_mask is not None, and output_mask is None:
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# we should return a not-None mask
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output_mask = mask
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for _ in range(2, len(K.int_shape(mask))):
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output_mask = K.any(output_mask, axis=-1)
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else:
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# output_mask is not None. We need to reshape it
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input_length = input_shape[1]
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if not input_length:
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input_length = K.shape(inputs)[1]
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output_mask_int_shape = K.int_shape(output_mask)
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if output_mask_int_shape is None:
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# if the output_mask does not have a static shape,
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# its shape must be the same as mask's
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if mask is not None:
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output_mask_int_shape = K.int_shape(mask)
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else:
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output_mask_int_shape = K.compute_output_shape(input_shape)[:-1]
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output_mask_shape = self._get_shape_tuple(
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(-1, input_length), output_mask, 1, output_mask_int_shape[1:])
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output_mask = K.reshape(output_mask, output_mask_shape)
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return output_mask
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@tf_export('keras.layers.Bidirectional')
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class Bidirectional(Wrapper):
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"""Bidirectional wrapper for RNNs.
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Arguments:
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layer: `Recurrent` instance.
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merge_mode: Mode by which outputs of the
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forward and backward RNNs will be combined.
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One of {'sum', 'mul', 'concat', 'ave', None}.
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If None, the outputs will not be combined,
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they will be returned as a list.
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Raises:
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ValueError: If not initialized with a `Layer` instance or
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In case of invalid `merge_mode` argument.
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Examples:
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```python
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model = Sequential()
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model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(5,
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10)))
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model.add(Bidirectional(LSTM(10)))
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model.add(Dense(5))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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```
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"""
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def __init__(self, layer, merge_mode='concat', weights=None, **kwargs):
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if not isinstance(layer, Layer):
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raise ValueError(
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'Please initialize `Bidirectional` layer with a '
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'`Layer` instance. You passed: {input}'.format(input=layer))
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if merge_mode not in ['sum', 'mul', 'ave', 'concat', None]:
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raise ValueError('Invalid merge mode. '
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'Merge mode should be one of '
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'{"sum", "mul", "ave", "concat", None}')
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self.forward_layer = copy.copy(layer)
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config = layer.get_config()
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config['go_backwards'] = not config['go_backwards']
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self.backward_layer = layer.__class__.from_config(config)
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self.forward_layer._name = 'forward_' + self.forward_layer.name
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self.backward_layer._name = 'backward_' + self.backward_layer.name
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self.merge_mode = merge_mode
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if weights:
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nw = len(weights)
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self.forward_layer.initial_weights = weights[:nw // 2]
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self.backward_layer.initial_weights = weights[nw // 2:]
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self.stateful = layer.stateful
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self.return_sequences = layer.return_sequences
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self.return_state = layer.return_state
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self.supports_masking = True
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self._trainable = True
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self._num_constants = None
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super(Bidirectional, self).__init__(layer, **kwargs)
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self.input_spec = layer.input_spec
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@property
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def trainable(self):
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return self._trainable
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@trainable.setter
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def trainable(self, value):
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self._trainable = value
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self.forward_layer.trainable = value
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self.backward_layer.trainable = value
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def get_weights(self):
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return self.forward_layer.get_weights() + self.backward_layer.get_weights()
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def set_weights(self, weights):
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nw = len(weights)
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self.forward_layer.set_weights(weights[:nw // 2])
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self.backward_layer.set_weights(weights[nw // 2:])
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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output_shape = tuple(self.forward_layer.compute_output_shape(
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input_shape).as_list())
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if self.return_state:
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state_shape = output_shape[1:]
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output_shape = output_shape[0]
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if self.merge_mode == 'concat':
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output_shape = list(output_shape)
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output_shape[-1] *= 2
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output_shape = tuple(output_shape)
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elif self.merge_mode is None:
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output_shape = [output_shape, copy.copy(output_shape)]
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if self.return_state:
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if self.merge_mode is None:
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return output_shape + state_shape + copy.copy(state_shape)
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return [output_shape] + state_shape + copy.copy(state_shape)
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return output_shape
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def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
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"""`Bidirectional.__call__` implements the same API as the wrapped `RNN`."""
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inputs, initial_state, constants = _standardize_args(
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inputs, initial_state, constants, self._num_constants)
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if isinstance(inputs, list):
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if len(inputs) > 1:
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initial_state = inputs[1:]
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inputs = inputs[0]
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if initial_state is None and constants is None:
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return super(Bidirectional, self).__call__(inputs, **kwargs)
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# Applies the same workaround as in `RNN.__call__`
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additional_inputs = []
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additional_specs = []
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if initial_state is not None:
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# Check if `initial_state` can be splitted into half
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num_states = len(initial_state)
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if num_states % 2 > 0:
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raise ValueError(
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'When passing `initial_state` to a Bidirectional RNN, '
|
|
'the state should be a list containing the states of '
|
|
'the underlying RNNs. '
|
|
'Found: ' + str(initial_state))
|
|
|
|
kwargs['initial_state'] = initial_state
|
|
additional_inputs += initial_state
|
|
state_specs = [InputSpec(shape=K.int_shape(state))
|
|
for state in initial_state]
|
|
self.forward_layer.state_spec = state_specs[:num_states // 2]
|
|
self.backward_layer.state_spec = state_specs[num_states // 2:]
|
|
additional_specs += state_specs
|
|
if constants is not None:
|
|
kwargs['constants'] = constants
|
|
additional_inputs += constants
|
|
constants_spec = [InputSpec(shape=K.int_shape(constant))
|
|
for constant in constants]
|
|
self.forward_layer.constants_spec = constants_spec
|
|
self.backward_layer.constants_spec = constants_spec
|
|
additional_specs += constants_spec
|
|
|
|
self._num_constants = len(constants)
|
|
self.forward_layer._num_constants = self._num_constants
|
|
self.backward_layer._num_constants = self._num_constants
|
|
|
|
is_keras_tensor = K.is_keras_tensor(additional_inputs[0])
|
|
for tensor in additional_inputs:
|
|
if K.is_keras_tensor(tensor) != is_keras_tensor:
|
|
raise ValueError('The initial state of a Bidirectional'
|
|
' layer cannot be specified with a mix of'
|
|
' Keras tensors and non-Keras tensors'
|
|
' (a "Keras tensor" is a tensor that was'
|
|
' returned by a Keras layer, or by `Input`)')
|
|
|
|
if is_keras_tensor:
|
|
# Compute the full input spec, including state
|
|
full_input = [inputs] + additional_inputs
|
|
full_input_spec = self.input_spec + additional_specs
|
|
|
|
# Perform the call with temporarily replaced input_spec
|
|
original_input_spec = self.input_spec
|
|
self.input_spec = full_input_spec
|
|
output = super(Bidirectional, self).__call__(full_input, **kwargs)
|
|
self.input_spec = original_input_spec
|
|
return output
|
|
else:
|
|
return super(Bidirectional, self).__call__(inputs, **kwargs)
|
|
|
|
def call(self, inputs,
|
|
training=None,
|
|
mask=None,
|
|
initial_state=None,
|
|
constants=None):
|
|
"""`Bidirectional.call` implements the same API as the wrapped `RNN`."""
|
|
kwargs = {}
|
|
if generic_utils.has_arg(self.layer.call, 'training'):
|
|
kwargs['training'] = training
|
|
if generic_utils.has_arg(self.layer.call, 'mask'):
|
|
kwargs['mask'] = mask
|
|
if generic_utils.has_arg(self.layer.call, 'constants'):
|
|
kwargs['constants'] = constants
|
|
|
|
if initial_state is not None and generic_utils.has_arg(
|
|
self.layer.call, 'initial_state'):
|
|
forward_state = initial_state[:len(initial_state) // 2]
|
|
backward_state = initial_state[len(initial_state) // 2:]
|
|
y = self.forward_layer.call(inputs, initial_state=forward_state, **kwargs)
|
|
y_rev = self.backward_layer.call(
|
|
inputs, initial_state=backward_state, **kwargs)
|
|
else:
|
|
y = self.forward_layer.call(inputs, **kwargs)
|
|
y_rev = self.backward_layer.call(inputs, **kwargs)
|
|
|
|
if self.return_state:
|
|
states = y[1:] + y_rev[1:]
|
|
y = y[0]
|
|
y_rev = y_rev[0]
|
|
|
|
if self.return_sequences:
|
|
y_rev = K.reverse(y_rev, 1)
|
|
if self.merge_mode == 'concat':
|
|
output = K.concatenate([y, y_rev])
|
|
elif self.merge_mode == 'sum':
|
|
output = y + y_rev
|
|
elif self.merge_mode == 'ave':
|
|
output = (y + y_rev) / 2
|
|
elif self.merge_mode == 'mul':
|
|
output = y * y_rev
|
|
elif self.merge_mode is None:
|
|
output = [y, y_rev]
|
|
|
|
# Properly set learning phase
|
|
if (getattr(y, '_uses_learning_phase', False) or
|
|
getattr(y_rev, '_uses_learning_phase', False)):
|
|
if self.merge_mode is None:
|
|
for out in output:
|
|
out._uses_learning_phase = True
|
|
else:
|
|
output._uses_learning_phase = True
|
|
|
|
if self.return_state:
|
|
if self.merge_mode is None:
|
|
return output + states
|
|
return [output] + states
|
|
return output
|
|
|
|
def reset_states(self):
|
|
self.forward_layer.reset_states()
|
|
self.backward_layer.reset_states()
|
|
|
|
def build(self, input_shape):
|
|
with K.name_scope(self.forward_layer.name):
|
|
self.forward_layer.build(input_shape)
|
|
with K.name_scope(self.backward_layer.name):
|
|
self.backward_layer.build(input_shape)
|
|
self.built = True
|
|
|
|
def compute_mask(self, inputs, mask):
|
|
if isinstance(mask, list):
|
|
mask = mask[0]
|
|
if self.return_sequences:
|
|
if not self.merge_mode:
|
|
output_mask = [mask, mask]
|
|
else:
|
|
output_mask = mask
|
|
else:
|
|
output_mask = [None, None] if not self.merge_mode else None
|
|
|
|
if self.return_state:
|
|
states = self.forward_layer.states
|
|
state_mask = [None for _ in states]
|
|
if isinstance(output_mask, list):
|
|
return output_mask + state_mask * 2
|
|
return [output_mask] + state_mask * 2
|
|
return output_mask
|
|
|
|
@property
|
|
def trainable_weights(self):
|
|
if hasattr(self.forward_layer, 'trainable_weights'):
|
|
return (self.forward_layer.trainable_weights +
|
|
self.backward_layer.trainable_weights)
|
|
return []
|
|
|
|
@property
|
|
def non_trainable_weights(self):
|
|
if hasattr(self.forward_layer, 'non_trainable_weights'):
|
|
return (self.forward_layer.non_trainable_weights +
|
|
self.backward_layer.non_trainable_weights)
|
|
return []
|
|
|
|
@property
|
|
def updates(self):
|
|
if hasattr(self.forward_layer, 'updates'):
|
|
return self.forward_layer.updates + self.backward_layer.updates
|
|
return []
|
|
|
|
@property
|
|
def losses(self):
|
|
if hasattr(self.forward_layer, 'losses'):
|
|
return self.forward_layer.losses + self.backward_layer.losses
|
|
return []
|
|
|
|
@property
|
|
def constraints(self):
|
|
constraints = {}
|
|
if hasattr(self.forward_layer, 'constraints'):
|
|
constraints.update(self.forward_layer.constraints)
|
|
constraints.update(self.backward_layer.constraints)
|
|
return constraints
|
|
|
|
def get_config(self):
|
|
config = {'merge_mode': self.merge_mode}
|
|
if self._num_constants is not None:
|
|
config['num_constants'] = self._num_constants
|
|
base_config = super(Bidirectional, self).get_config()
|
|
return dict(list(base_config.items()) + list(config.items()))
|
|
|
|
@classmethod
|
|
def from_config(cls, config, custom_objects=None):
|
|
num_constants = config.pop('num_constants', None)
|
|
layer = super(Bidirectional, cls).from_config(config,
|
|
custom_objects=custom_objects)
|
|
layer._num_constants = num_constants
|
|
return layer
|