510 lines
19 KiB
Python
510 lines
19 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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"""Fully connected RNN layer."""
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import tensorflow.compat.v2 as tf
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from keras import activations
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from keras import backend
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from keras import constraints
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from keras import initializers
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from keras import regularizers
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from keras.engine import base_layer
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from keras.engine.input_spec import InputSpec
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from keras.layers.rnn import rnn_utils
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from keras.layers.rnn.base_rnn import RNN
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from keras.layers.rnn.dropout_rnn_cell_mixin import DropoutRNNCellMixin
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from keras.utils import tf_utils
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# isort: off
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.SimpleRNNCell")
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class SimpleRNNCell(DropoutRNNCellMixin, base_layer.BaseRandomLayer):
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"""Cell class for SimpleRNN.
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See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
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for details about the usage of RNN API.
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This class processes one step within the whole time sequence input, whereas
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`tf.keras.layer.SimpleRNN` processes the whole sequence.
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Args:
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units: Positive integer, dimensionality of the output space.
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activation: Activation function to use.
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Default: hyperbolic tangent (`tanh`).
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If you pass `None`, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, (default `True`), whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix,
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used for the linear transformation of the inputs. Default:
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`glorot_uniform`.
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix, used for the linear transformation of the recurrent
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state. Default: `orthogonal`.
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bias_initializer: Initializer for the bias vector. Default: `zeros`.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_regularizer: Regularizer function applied to the bias vector.
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Default: `None`.
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_constraint: Constraint function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_constraint: Constraint function applied to the bias vector. Default:
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`None`.
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dropout: Float between 0 and 1. Fraction of the units to drop for the
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linear transformation of the inputs. Default: 0.
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recurrent_dropout: Float between 0 and 1. Fraction of the units to drop
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for the linear transformation of the recurrent state. Default: 0.
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Call arguments:
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inputs: A 2D tensor, with shape of `[batch, feature]`.
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states: A 2D tensor with shape of `[batch, units]`, which is the state
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from the previous time step. For timestep 0, the initial state provided
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by user will be feed to cell.
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training: Python boolean indicating whether the layer should behave in
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training mode or in inference mode. Only relevant when `dropout` or
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`recurrent_dropout` is used.
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Examples:
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```python
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inputs = np.random.random([32, 10, 8]).astype(np.float32)
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rnn = tf.keras.layers.RNN(tf.keras.layers.SimpleRNNCell(4))
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output = rnn(inputs) # The output has shape `[32, 4]`.
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rnn = tf.keras.layers.RNN(
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tf.keras.layers.SimpleRNNCell(4),
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return_sequences=True,
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return_state=True)
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# whole_sequence_output has shape `[32, 10, 4]`.
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# final_state has shape `[32, 4]`.
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whole_sequence_output, final_state = rnn(inputs)
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```
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"""
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def __init__(
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self,
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units,
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activation="tanh",
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use_bias=True,
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kernel_initializer="glorot_uniform",
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recurrent_initializer="orthogonal",
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bias_initializer="zeros",
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kernel_regularizer=None,
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recurrent_regularizer=None,
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bias_regularizer=None,
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kernel_constraint=None,
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recurrent_constraint=None,
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bias_constraint=None,
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dropout=0.0,
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recurrent_dropout=0.0,
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**kwargs,
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):
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if units <= 0:
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raise ValueError(
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"Received an invalid value for argument `units`, "
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f"expected a positive integer, got {units}."
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)
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# By default use cached variable under v2 mode, see b/143699808.
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if tf.compat.v1.executing_eagerly_outside_functions():
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self._enable_caching_device = kwargs.pop(
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"enable_caching_device", True
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)
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else:
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self._enable_caching_device = kwargs.pop(
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"enable_caching_device", False
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)
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super().__init__(**kwargs)
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self.units = units
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.recurrent_initializer = initializers.get(recurrent_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.recurrent_constraint = constraints.get(recurrent_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.dropout = min(1.0, max(0.0, dropout))
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self.recurrent_dropout = min(1.0, max(0.0, recurrent_dropout))
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self.state_size = self.units
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self.output_size = self.units
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@tf_utils.shape_type_conversion
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def build(self, input_shape):
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super().build(input_shape)
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default_caching_device = rnn_utils.caching_device(self)
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self.kernel = self.add_weight(
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shape=(input_shape[-1], self.units),
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name="kernel",
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initializer=self.kernel_initializer,
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint,
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caching_device=default_caching_device,
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)
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self.recurrent_kernel = self.add_weight(
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shape=(self.units, self.units),
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name="recurrent_kernel",
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initializer=self.recurrent_initializer,
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regularizer=self.recurrent_regularizer,
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constraint=self.recurrent_constraint,
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caching_device=default_caching_device,
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)
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if self.use_bias:
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self.bias = self.add_weight(
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shape=(self.units,),
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name="bias",
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initializer=self.bias_initializer,
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint,
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caching_device=default_caching_device,
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)
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else:
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self.bias = None
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self.built = True
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def call(self, inputs, states, training=None):
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prev_output = states[0] if tf.nest.is_nested(states) else states
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dp_mask = self.get_dropout_mask_for_cell(inputs, training)
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rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
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prev_output, training
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)
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if dp_mask is not None:
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h = backend.dot(inputs * dp_mask, self.kernel)
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else:
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h = backend.dot(inputs, self.kernel)
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if self.bias is not None:
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h = backend.bias_add(h, self.bias)
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if rec_dp_mask is not None:
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prev_output = prev_output * rec_dp_mask
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output = h + backend.dot(prev_output, self.recurrent_kernel)
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if self.activation is not None:
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output = self.activation(output)
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new_state = [output] if tf.nest.is_nested(states) else output
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return output, new_state
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def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
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return rnn_utils.generate_zero_filled_state_for_cell(
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self, inputs, batch_size, dtype
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)
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def get_config(self):
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config = {
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"units": self.units,
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"activation": activations.serialize(self.activation),
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"use_bias": self.use_bias,
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"kernel_initializer": initializers.serialize(
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self.kernel_initializer
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),
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"recurrent_initializer": initializers.serialize(
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self.recurrent_initializer
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),
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"bias_initializer": initializers.serialize(self.bias_initializer),
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"kernel_regularizer": regularizers.serialize(
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self.kernel_regularizer
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),
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"recurrent_regularizer": regularizers.serialize(
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self.recurrent_regularizer
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),
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"bias_regularizer": regularizers.serialize(self.bias_regularizer),
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"kernel_constraint": constraints.serialize(self.kernel_constraint),
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"recurrent_constraint": constraints.serialize(
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self.recurrent_constraint
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),
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"bias_constraint": constraints.serialize(self.bias_constraint),
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"dropout": self.dropout,
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"recurrent_dropout": self.recurrent_dropout,
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}
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config.update(rnn_utils.config_for_enable_caching_device(self))
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export("keras.layers.SimpleRNN")
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class SimpleRNN(RNN):
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"""Fully-connected RNN where the output is to be fed back to input.
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See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
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for details about the usage of RNN API.
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Args:
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units: Positive integer, dimensionality of the output space.
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activation: Activation function to use.
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Default: hyperbolic tangent (`tanh`).
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If you pass None, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, (default `True`), whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix,
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used for the linear transformation of the inputs. Default:
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`glorot_uniform`.
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix, used for the linear transformation of the recurrent
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state. Default: `orthogonal`.
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bias_initializer: Initializer for the bias vector. Default: `zeros`.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_regularizer: Regularizer function applied to the bias vector.
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Default: `None`.
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activity_regularizer: Regularizer function applied to the output of the
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layer (its "activation"). Default: `None`.
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_constraint: Constraint function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_constraint: Constraint function applied to the bias vector. Default:
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`None`.
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dropout: Float between 0 and 1.
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Fraction of the units to drop for the linear transformation of the
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inputs. Default: 0.
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recurrent_dropout: Float between 0 and 1.
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Fraction of the units to drop for the linear transformation of the
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recurrent state. Default: 0.
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return_sequences: Boolean. Whether to return the last output
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in the output sequence, or the full sequence. Default: `False`.
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return_state: Boolean. Whether to return the last state
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in addition to the output. Default: `False`
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go_backwards: Boolean (default False).
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If True, process the input sequence backwards and return the
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reversed sequence.
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stateful: Boolean (default False). If True, the last state
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for each sample at index i in a batch will be used as initial
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state for the sample of index i in the following batch.
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unroll: Boolean (default False).
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If True, the network will be unrolled,
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else a symbolic loop will be used.
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Unrolling can speed-up a RNN,
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although it tends to be more memory-intensive.
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Unrolling is only suitable for short sequences.
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Call arguments:
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inputs: A 3D tensor, with shape `[batch, timesteps, feature]`.
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mask: Binary tensor of shape `[batch, timesteps]` indicating whether
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a given timestep should be masked. An individual `True` entry indicates
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that the corresponding timestep should be utilized, while a `False`
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entry indicates that the corresponding timestep should be ignored.
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training: Python boolean indicating whether the layer should behave in
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training mode or in inference mode. This argument is passed to the cell
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when calling it. This is only relevant if `dropout` or
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`recurrent_dropout` is used.
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initial_state: List of initial state tensors to be passed to the first
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call of the cell.
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Examples:
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```python
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inputs = np.random.random([32, 10, 8]).astype(np.float32)
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simple_rnn = tf.keras.layers.SimpleRNN(4)
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output = simple_rnn(inputs) # The output has shape `[32, 4]`.
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simple_rnn = tf.keras.layers.SimpleRNN(
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4, return_sequences=True, return_state=True)
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# whole_sequence_output has shape `[32, 10, 4]`.
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# final_state has shape `[32, 4]`.
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whole_sequence_output, final_state = simple_rnn(inputs)
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```
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"""
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def __init__(
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self,
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units,
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activation="tanh",
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use_bias=True,
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kernel_initializer="glorot_uniform",
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recurrent_initializer="orthogonal",
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bias_initializer="zeros",
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kernel_regularizer=None,
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recurrent_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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recurrent_constraint=None,
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bias_constraint=None,
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dropout=0.0,
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recurrent_dropout=0.0,
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return_sequences=False,
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return_state=False,
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go_backwards=False,
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stateful=False,
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unroll=False,
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**kwargs,
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):
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if "implementation" in kwargs:
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kwargs.pop("implementation")
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logging.warning(
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"The `implementation` argument "
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"in `SimpleRNN` has been deprecated. "
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"Please remove it from your layer call."
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)
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if "enable_caching_device" in kwargs:
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cell_kwargs = {
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"enable_caching_device": kwargs.pop("enable_caching_device")
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}
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else:
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cell_kwargs = {}
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cell = SimpleRNNCell(
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units,
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activation=activation,
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use_bias=use_bias,
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kernel_initializer=kernel_initializer,
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recurrent_initializer=recurrent_initializer,
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bias_initializer=bias_initializer,
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kernel_regularizer=kernel_regularizer,
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recurrent_regularizer=recurrent_regularizer,
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bias_regularizer=bias_regularizer,
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kernel_constraint=kernel_constraint,
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recurrent_constraint=recurrent_constraint,
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bias_constraint=bias_constraint,
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dropout=dropout,
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recurrent_dropout=recurrent_dropout,
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dtype=kwargs.get("dtype"),
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trainable=kwargs.get("trainable", True),
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**cell_kwargs,
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)
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super().__init__(
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cell,
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return_sequences=return_sequences,
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return_state=return_state,
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go_backwards=go_backwards,
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stateful=stateful,
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unroll=unroll,
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**kwargs,
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)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.input_spec = [InputSpec(ndim=3)]
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def call(self, inputs, mask=None, training=None, initial_state=None):
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return super().call(
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inputs, mask=mask, training=training, initial_state=initial_state
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)
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@property
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def units(self):
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return self.cell.units
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@property
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def activation(self):
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return self.cell.activation
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@property
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def use_bias(self):
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return self.cell.use_bias
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@property
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def kernel_initializer(self):
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return self.cell.kernel_initializer
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@property
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def recurrent_initializer(self):
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return self.cell.recurrent_initializer
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@property
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def bias_initializer(self):
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return self.cell.bias_initializer
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@property
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def kernel_regularizer(self):
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return self.cell.kernel_regularizer
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@property
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def recurrent_regularizer(self):
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return self.cell.recurrent_regularizer
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@property
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def bias_regularizer(self):
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return self.cell.bias_regularizer
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@property
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def kernel_constraint(self):
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return self.cell.kernel_constraint
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@property
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def recurrent_constraint(self):
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return self.cell.recurrent_constraint
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@property
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def bias_constraint(self):
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return self.cell.bias_constraint
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@property
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def dropout(self):
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return self.cell.dropout
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@property
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def recurrent_dropout(self):
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return self.cell.recurrent_dropout
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def get_config(self):
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config = {
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"units": self.units,
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"activation": activations.serialize(self.activation),
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"use_bias": self.use_bias,
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"kernel_initializer": initializers.serialize(
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self.kernel_initializer
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),
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"recurrent_initializer": initializers.serialize(
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self.recurrent_initializer
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),
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"bias_initializer": initializers.serialize(self.bias_initializer),
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"kernel_regularizer": regularizers.serialize(
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self.kernel_regularizer
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),
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"recurrent_regularizer": regularizers.serialize(
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self.recurrent_regularizer
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),
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"bias_regularizer": regularizers.serialize(self.bias_regularizer),
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"activity_regularizer": regularizers.serialize(
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self.activity_regularizer
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),
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"kernel_constraint": constraints.serialize(self.kernel_constraint),
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"recurrent_constraint": constraints.serialize(
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self.recurrent_constraint
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),
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"bias_constraint": constraints.serialize(self.bias_constraint),
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"dropout": self.dropout,
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"recurrent_dropout": self.recurrent_dropout,
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}
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base_config = super().get_config()
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config.update(rnn_utils.config_for_enable_caching_device(self.cell))
|
|
del base_config["cell"]
|
|
return dict(list(base_config.items()) + list(config.items()))
|
|
|
|
@classmethod
|
|
def from_config(cls, config):
|
|
if "implementation" in config:
|
|
config.pop("implementation")
|
|
return cls(**config)
|