191 lines
8.7 KiB
Python
191 lines
8.7 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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"""3D Convolutional LSTM layer."""
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from keras.layers.rnn.base_conv_lstm import ConvLSTM
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.ConvLSTM3D")
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class ConvLSTM3D(ConvLSTM):
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"""3D Convolutional LSTM.
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Similar to an LSTM layer, but the input transformations
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and recurrent transformations are both convolutional.
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Args:
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filters: Integer, the dimensionality of the output space (i.e. the number
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of output filters in the convolution).
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kernel_size: An integer or tuple/list of n integers, specifying the
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dimensions of the convolution window.
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strides: An integer or tuple/list of n integers, specifying the strides of
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the convolution. Specifying any stride value != 1 is incompatible with
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specifying any `dilation_rate` value != 1.
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padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means
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no padding. `"same"` results in padding evenly to the left/right or
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up/down of the input such that output has the same height/width
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dimension as the input.
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data_format: A string, one of `channels_last` (default) or
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`channels_first`. The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape `(batch, time, ...,
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channels)` while `channels_first` corresponds to inputs with shape
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`(batch, time, channels, ...)`. It defaults to the `image_data_format`
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value found in your Keras config file at `~/.keras/keras.json`. If you
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never set it, then it will be "channels_last".
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dilation_rate: An integer or tuple/list of n integers, specifying the
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dilation rate to use for dilated convolution. Currently, specifying any
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`dilation_rate` value != 1 is incompatible with specifying any `strides`
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value != 1.
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activation: Activation function to use. By default hyperbolic tangent
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activation function is applied (`tanh(x)`).
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recurrent_activation: Activation function to use for the recurrent step.
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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, used for
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the linear transformation of the inputs.
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recurrent_initializer: Initializer for the `recurrent_kernel` weights
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matrix, 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. If True, add 1 to the bias of the forget gate
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at initialization. Use in combination with `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 the `kernel` weights
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matrix.
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recurrent_regularizer: Regularizer function applied to the
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`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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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix.
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recurrent_constraint: Constraint function applied to the
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`recurrent_kernel` weights matrix.
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bias_constraint: Constraint function applied to the bias vector.
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return_sequences: Boolean. Whether to return the last output in the output
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sequence, or the full sequence. (default False)
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return_state: Boolean Whether to return the last state in addition to the
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output. (default False)
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go_backwards: Boolean (default False). If True, process the input sequence
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backwards.
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stateful: Boolean (default False). If True, the last state for each sample
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at index i in a batch will be used as initial state for the sample of
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index i in the following batch.
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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.
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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.
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Call arguments:
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inputs: A 6D tensor.
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mask: Binary tensor of shape `(samples, timesteps)` indicating whether a
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given timestep should be masked.
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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` are set.
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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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Input shape: - If data_format='channels_first'
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6D tensor with shape: `(samples, time, channels, rows, cols, depth)` -
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If data_format='channels_last'
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5D tensor with shape: `(samples, time, rows, cols, depth, channels)`
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Output shape:
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- If `return_state`: a list of tensors. The first tensor is the output.
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The remaining tensors are the last states,
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each 5D tensor with shape: `(samples, filters, new_rows, new_cols,
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new_depth)` if data_format='channels_first'
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or shape: `(samples, new_rows, new_cols, new_depth, filters)` if
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data_format='channels_last'. `rows`, `cols`, and `depth` values might
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have changed due to padding.
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- If `return_sequences`: 6D tensor with shape: `(samples, timesteps,
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filters, new_rows, new_cols, new_depth)` if data_format='channels_first'
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or shape: `(samples, timesteps, new_rows, new_cols, new_depth, filters)`
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if data_format='channels_last'.
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- Else, 5D tensor with shape: `(samples, filters, new_rows, new_cols,
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new_depth)` if data_format='channels_first'
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or shape: `(samples, new_rows, new_cols, new_depth, filters)` if
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data_format='channels_last'.
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Raises:
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ValueError: in case of invalid constructor arguments.
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References:
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- [Shi et al., 2015](http://arxiv.org/abs/1506.04214v1)
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(the current implementation does not include the feedback loop on the
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cells output).
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"""
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def __init__(
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self,
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filters,
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kernel_size,
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strides=(1, 1, 1),
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padding="valid",
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data_format=None,
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dilation_rate=(1, 1, 1),
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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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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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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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rank=3,
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filters=filters,
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kernel_size=kernel_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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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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activity_regularizer=activity_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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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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dropout=dropout,
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recurrent_dropout=recurrent_dropout,
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**kwargs
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)
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