181 lines
7.4 KiB
Python
181 lines
7.4 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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"""Keras 1D convolution 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.dtensor import utils
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from keras.layers.convolutional.base_conv import Conv
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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.Conv1D", "keras.layers.Convolution1D")
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class Conv1D(Conv):
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"""1D convolution layer (e.g. temporal convolution).
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This layer creates a convolution kernel that is convolved
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with the layer input over a single spatial (or temporal) dimension
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to produce a tensor of outputs.
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If `use_bias` is True, a bias vector is created and added to the outputs.
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Finally, if `activation` is not `None`,
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it is applied to the outputs as well.
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When using this layer as the first layer in a model,
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provide an `input_shape` argument
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(tuple of integers or `None`, e.g.
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`(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
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or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
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Examples:
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>>> # The inputs are 128-length vectors with 10 timesteps, and the
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>>> # batch size is 4.
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>>> input_shape = (4, 10, 128)
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>>> x = tf.random.normal(input_shape)
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>>> y = tf.keras.layers.Conv1D(
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... 32, 3, activation='relu',input_shape=input_shape[1:])(x)
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>>> print(y.shape)
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(4, 8, 32)
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>>> # With extended batch shape [4, 7] (e.g. weather data where batch
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>>> # dimensions correspond to spatial location and the third dimension
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>>> # corresponds to time.)
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>>> input_shape = (4, 7, 10, 128)
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>>> x = tf.random.normal(input_shape)
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>>> y = tf.keras.layers.Conv1D(
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... 32, 3, activation='relu', input_shape=input_shape[2:])(x)
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>>> print(y.shape)
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(4, 7, 8, 32)
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Args:
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filters: Integer, the dimensionality of the output space
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(i.e. the number of output filters in the convolution).
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kernel_size: An integer or tuple/list of a single integer,
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specifying the length of the 1D convolution window.
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strides: An integer or tuple/list of a single integer,
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specifying the stride length of the convolution.
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Specifying any stride value != 1 is incompatible with specifying
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any `dilation_rate` value != 1.
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padding: One of `"valid"`, `"same"` or `"causal"` (case-insensitive).
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`"valid"` means no padding. `"same"` results in padding with zeros
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evenly to the left/right or up/down of the input such that output has
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the same height/width dimension as the input.
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`"causal"` results in causal (dilated) convolutions, e.g. `output[t]`
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does not depend on `input[t+1:]`. Useful when modeling temporal data
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where the model should not violate the temporal order.
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See [WaveNet: A Generative Model for Raw Audio, section
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2.1](https://arxiv.org/abs/1609.03499).
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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_size, width,
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channels)` while `channels_first` corresponds to inputs with shape
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`(batch_size, channels, width)`. Note that the `channels_first` format
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is currently not supported by TensorFlow on CPU.
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dilation_rate: an integer or tuple/list of a single integer, specifying
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the dilation rate to use for dilated convolution.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any `strides` value != 1.
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groups: A positive integer specifying the number of groups in which the
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input is split along the channel axis. Each group is convolved
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separately with `filters / groups` filters. The output is the
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concatenation of all the `groups` results along the channel axis.
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Input channels and `filters` must both be divisible by `groups`.
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activation: Activation function to use.
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If you don't specify anything, no activation is applied
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(see `keras.activations`).
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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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(see `keras.initializers`). Defaults to 'glorot_uniform'.
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bias_initializer: Initializer for the bias vector
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(see `keras.initializers`). Defaults to 'zeros'.
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix (see `keras.regularizers`).
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bias_regularizer: Regularizer function applied to the bias vector
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(see `keras.regularizers`).
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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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(see `keras.regularizers`).
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kernel_constraint: Constraint function applied to the kernel matrix
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(see `keras.constraints`).
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bias_constraint: Constraint function applied to the bias vector
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(see `keras.constraints`).
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Input shape:
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3+D tensor with shape: `batch_shape + (steps, input_dim)`
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Output shape:
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3+D tensor with shape: `batch_shape + (new_steps, filters)`
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`steps` value might have changed due to padding or strides.
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Returns:
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A tensor of rank 3 representing
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`activation(conv1d(inputs, kernel) + bias)`.
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Raises:
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ValueError: when both `strides > 1` and `dilation_rate > 1`.
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"""
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@utils.allow_initializer_layout
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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,
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padding="valid",
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data_format="channels_last",
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dilation_rate=1,
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groups=1,
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activation=None,
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use_bias=True,
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kernel_initializer="glorot_uniform",
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bias_initializer="zeros",
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kernel_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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bias_constraint=None,
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**kwargs
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):
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super().__init__(
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rank=1,
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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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groups=groups,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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bias_initializer=initializers.get(bias_initializer),
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kernel_regularizer=regularizers.get(kernel_regularizer),
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bias_regularizer=regularizers.get(bias_regularizer),
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activity_regularizer=regularizers.get(activity_regularizer),
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kernel_constraint=constraints.get(kernel_constraint),
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bias_constraint=constraints.get(bias_constraint),
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**kwargs
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)
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# Alias
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Convolution1D = Conv1D
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