404 lines
15 KiB
Python
404 lines
15 KiB
Python
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# 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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"""Long Short-Term Memory V1 layer."""
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from keras import activations
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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.input_spec import InputSpec
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from keras.layers.rnn import lstm
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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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# 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(v1=["keras.layers.LSTMCell"])
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class LSTMCell(lstm.LSTMCell):
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"""Cell class for the LSTM layer.
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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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recurrent_activation: Activation function to use
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for the recurrent step.
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Default: hard sigmoid (`hard_sigmoid`).
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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, 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.
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix,
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used for the linear transformation of the recurrent state.
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bias_initializer: Initializer for the bias vector.
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unit_forget_bias: Boolean.
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If True, add 1 to the bias of the forget gate at initialization.
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Setting it to true will also force `bias_initializer="zeros"`.
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This is recommended in [Jozefowicz et al., 2015](
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http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix.
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recurrent_regularizer: Regularizer function applied to
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the `recurrent_kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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kernel_constraint: Constraint function applied to
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the `kernel` weights matrix.
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recurrent_constraint: Constraint function applied to
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the `recurrent_kernel` weights matrix.
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bias_constraint: Constraint function applied to the bias vector.
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dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the inputs.
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recurrent_dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the recurrent state.
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Call arguments:
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inputs: A 2D tensor.
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states: List of state tensors corresponding to the previous timestep.
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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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"""
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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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recurrent_activation="hard_sigmoid",
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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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unit_forget_bias=True,
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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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super().__init__(
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units,
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activation=activation,
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recurrent_activation=recurrent_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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unit_forget_bias=unit_forget_bias,
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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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implementation=kwargs.pop("implementation", 1),
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**kwargs
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)
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@keras_export(v1=["keras.layers.LSTM"])
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class LSTM(RNN):
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"""Long Short-Term Memory layer - Hochreiter 1997.
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Note that this cell is not optimized for performance on GPU. Please use
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`tf.compat.v1.keras.layers.CuDNNLSTM` for better performance on GPU.
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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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recurrent_activation: Activation function to use
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for the recurrent step.
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Default: hard sigmoid (`hard_sigmoid`).
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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, 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..
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix,
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used for the linear transformation of the recurrent state.
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bias_initializer: Initializer for the bias vector.
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unit_forget_bias: Boolean.
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If True, add 1 to the bias of the forget gate at initialization.
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Setting it to true will also force `bias_initializer="zeros"`.
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This is recommended in [Jozefowicz et al., 2015](
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http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf).
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix.
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recurrent_regularizer: Regularizer function applied to
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the `recurrent_kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation").
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kernel_constraint: Constraint function applied to
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the `kernel` weights matrix.
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recurrent_constraint: Constraint function applied to
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the `recurrent_kernel` weights matrix.
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bias_constraint: Constraint function applied to the bias vector.
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dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the inputs.
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recurrent_dropout: Float between 0 and 1.
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Fraction of the units to drop for
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the linear transformation of the recurrent state.
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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.
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return_state: Boolean. Whether to return the last state
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in addition to the output.
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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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time_major: The shape format of the `inputs` and `outputs` tensors.
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If True, the inputs and outputs will be in shape
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`(timesteps, batch, ...)`, whereas in the False case, it will be
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`(batch, timesteps, ...)`. Using `time_major = True` is a bit more
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efficient because it avoids transposes at the beginning and end of the
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RNN calculation. However, most TensorFlow data is batch-major, so by
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default this function accepts input and emits output in batch-major
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form.
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Call arguments:
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inputs: A 3D tensor.
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mask: Binary tensor of shape `(samples, 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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"""
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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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recurrent_activation="hard_sigmoid",
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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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unit_forget_bias=True,
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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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implementation = kwargs.pop("implementation", 1)
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if implementation == 0:
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logging.warning(
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"`implementation=0` has been deprecated, "
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"and now defaults to `implementation=1`."
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"Please update 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 = LSTMCell(
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units,
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activation=activation,
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recurrent_activation=recurrent_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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unit_forget_bias=unit_forget_bias,
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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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implementation=implementation,
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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 recurrent_activation(self):
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return self.cell.recurrent_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 unit_forget_bias(self):
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return self.cell.unit_forget_bias
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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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@property
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def implementation(self):
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return self.cell.implementation
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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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"recurrent_activation": activations.serialize(
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self.recurrent_activation
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),
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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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"unit_forget_bias": self.unit_forget_bias,
|
||
|
|
"kernel_regularizer": regularizers.serialize(
|
||
|
|
self.kernel_regularizer
|
||
|
|
),
|
||
|
|
"recurrent_regularizer": regularizers.serialize(
|
||
|
|
self.recurrent_regularizer
|
||
|
|
),
|
||
|
|
"bias_regularizer": regularizers.serialize(self.bias_regularizer),
|
||
|
|
"activity_regularizer": regularizers.serialize(
|
||
|
|
self.activity_regularizer
|
||
|
|
),
|
||
|
|
"kernel_constraint": constraints.serialize(self.kernel_constraint),
|
||
|
|
"recurrent_constraint": constraints.serialize(
|
||
|
|
self.recurrent_constraint
|
||
|
|
),
|
||
|
|
"bias_constraint": constraints.serialize(self.bias_constraint),
|
||
|
|
"dropout": self.dropout,
|
||
|
|
"recurrent_dropout": self.recurrent_dropout,
|
||
|
|
"implementation": self.implementation,
|
||
|
|
}
|
||
|
|
config.update(rnn_utils.config_for_enable_caching_device(self.cell))
|
||
|
|
base_config = super().get_config()
|
||
|
|
del base_config["cell"]
|
||
|
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_config(cls, config):
|
||
|
|
if "implementation" in config and config["implementation"] == 0:
|
||
|
|
config["implementation"] = 1
|
||
|
|
return cls(**config)
|