Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/convolutional/conv1d.py: 93%
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1# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Keras 1D convolution layer."""
18from keras.src import activations
19from keras.src import constraints
20from keras.src import initializers
21from keras.src import regularizers
22from keras.src.dtensor import utils
23from keras.src.layers.convolutional.base_conv import Conv
25# isort: off
26from tensorflow.python.util.tf_export import keras_export
29@keras_export("keras.layers.Conv1D", "keras.layers.Convolution1D")
30class Conv1D(Conv):
31 """1D convolution layer (e.g. temporal convolution).
33 This layer creates a convolution kernel that is convolved
34 with the layer input over a single spatial (or temporal) dimension
35 to produce a tensor of outputs.
36 If `use_bias` is True, a bias vector is created and added to the outputs.
37 Finally, if `activation` is not `None`,
38 it is applied to the outputs as well.
40 When using this layer as the first layer in a model,
41 provide an `input_shape` argument
42 (tuple of integers or `None`, e.g.
43 `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
44 or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
46 Examples:
48 >>> # The inputs are 128-length vectors with 10 timesteps, and the
49 >>> # batch size is 4.
50 >>> input_shape = (4, 10, 128)
51 >>> x = tf.random.normal(input_shape)
52 >>> y = tf.keras.layers.Conv1D(
53 ... 32, 3, activation='relu',input_shape=input_shape[1:])(x)
54 >>> print(y.shape)
55 (4, 8, 32)
57 >>> # With extended batch shape [4, 7] (e.g. weather data where batch
58 >>> # dimensions correspond to spatial location and the third dimension
59 >>> # corresponds to time.)
60 >>> input_shape = (4, 7, 10, 128)
61 >>> x = tf.random.normal(input_shape)
62 >>> y = tf.keras.layers.Conv1D(
63 ... 32, 3, activation='relu', input_shape=input_shape[2:])(x)
64 >>> print(y.shape)
65 (4, 7, 8, 32)
67 Args:
68 filters: Integer, the dimensionality of the output space
69 (i.e. the number of output filters in the convolution).
70 kernel_size: An integer or tuple/list of a single integer,
71 specifying the length of the 1D convolution window.
72 strides: An integer or tuple/list of a single integer,
73 specifying the stride length of the convolution.
74 Specifying any stride value != 1 is incompatible with specifying
75 any `dilation_rate` value != 1.
76 padding: One of `"valid"`, `"same"` or `"causal"` (case-insensitive).
77 `"valid"` means no padding. `"same"` results in padding with zeros
78 evenly to the left/right or up/down of the input such that output has
79 the same height/width dimension as the input.
80 `"causal"` results in causal (dilated) convolutions, e.g. `output[t]`
81 does not depend on `input[t+1:]`. Useful when modeling temporal data
82 where the model should not violate the temporal order.
83 See [WaveNet: A Generative Model for Raw Audio, section
84 2.1](https://arxiv.org/abs/1609.03499).
85 data_format: A string, one of `channels_last` (default) or
86 `channels_first`. The ordering of the dimensions in the inputs.
87 `channels_last` corresponds to inputs with shape `(batch_size, width,
88 channels)` while `channels_first` corresponds to inputs with shape
89 `(batch_size, channels, width)`. Note that the `channels_first` format
90 is currently not supported by TensorFlow on CPU.
91 dilation_rate: an integer or tuple/list of a single integer, specifying
92 the dilation rate to use for dilated convolution.
93 Currently, specifying any `dilation_rate` value != 1 is
94 incompatible with specifying any `strides` value != 1.
95 groups: A positive integer specifying the number of groups in which the
96 input is split along the channel axis. Each group is convolved
97 separately with `filters / groups` filters. The output is the
98 concatenation of all the `groups` results along the channel axis.
99 Input channels and `filters` must both be divisible by `groups`.
100 activation: Activation function to use.
101 If you don't specify anything, no activation is applied
102 (see `keras.activations`).
103 use_bias: Boolean, whether the layer uses a bias vector.
104 kernel_initializer: Initializer for the `kernel` weights matrix
105 (see `keras.initializers`). Defaults to 'glorot_uniform'.
106 bias_initializer: Initializer for the bias vector
107 (see `keras.initializers`). Defaults to 'zeros'.
108 kernel_regularizer: Regularizer function applied to
109 the `kernel` weights matrix (see `keras.regularizers`).
110 bias_regularizer: Regularizer function applied to the bias vector
111 (see `keras.regularizers`).
112 activity_regularizer: Regularizer function applied to
113 the output of the layer (its "activation")
114 (see `keras.regularizers`).
115 kernel_constraint: Constraint function applied to the kernel matrix
116 (see `keras.constraints`).
117 bias_constraint: Constraint function applied to the bias vector
118 (see `keras.constraints`).
120 Input shape:
121 3+D tensor with shape: `batch_shape + (steps, input_dim)`
123 Output shape:
124 3+D tensor with shape: `batch_shape + (new_steps, filters)`
125 `steps` value might have changed due to padding or strides.
127 Returns:
128 A tensor of rank 3 representing
129 `activation(conv1d(inputs, kernel) + bias)`.
131 Raises:
132 ValueError: when both `strides > 1` and `dilation_rate > 1`.
133 """
135 @utils.allow_initializer_layout
136 def __init__(
137 self,
138 filters,
139 kernel_size,
140 strides=1,
141 padding="valid",
142 data_format="channels_last",
143 dilation_rate=1,
144 groups=1,
145 activation=None,
146 use_bias=True,
147 kernel_initializer="glorot_uniform",
148 bias_initializer="zeros",
149 kernel_regularizer=None,
150 bias_regularizer=None,
151 activity_regularizer=None,
152 kernel_constraint=None,
153 bias_constraint=None,
154 **kwargs
155 ):
156 super().__init__(
157 rank=1,
158 filters=filters,
159 kernel_size=kernel_size,
160 strides=strides,
161 padding=padding,
162 data_format=data_format,
163 dilation_rate=dilation_rate,
164 groups=groups,
165 activation=activations.get(activation),
166 use_bias=use_bias,
167 kernel_initializer=initializers.get(kernel_initializer),
168 bias_initializer=initializers.get(bias_initializer),
169 kernel_regularizer=regularizers.get(kernel_regularizer),
170 bias_regularizer=regularizers.get(bias_regularizer),
171 activity_regularizer=regularizers.get(activity_regularizer),
172 kernel_constraint=constraints.get(kernel_constraint),
173 bias_constraint=constraints.get(bias_constraint),
174 **kwargs
175 )
178# Alias
180Convolution1D = Conv1D