149 lines
4.9 KiB
Python
149 lines
4.9 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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"""Average pooling 1D layer."""
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import functools
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from keras import backend
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from keras.layers.pooling.base_pooling1d import Pooling1D
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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.AveragePooling1D", "keras.layers.AvgPool1D")
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class AveragePooling1D(Pooling1D):
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"""Average pooling for temporal data.
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Downsamples the input representation by taking the average value over the
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window defined by `pool_size`. The window is shifted by `strides`. The
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resulting output when using "valid" padding option has a shape of:
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`output_shape = (input_shape - pool_size + 1) / strides)`
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The resulting output shape when using the "same" padding option is:
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`output_shape = input_shape / strides`
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For example, for strides=1 and padding="valid":
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>>> x = tf.constant([1., 2., 3., 4., 5.])
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>>> x = tf.reshape(x, [1, 5, 1])
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>>> x
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<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
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array([[[1.],
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[2.],
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[3.],
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[4.],
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[5.]], dtype=float32)>
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>>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
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... strides=1, padding='valid')
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>>> avg_pool_1d(x)
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<tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy=
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array([[[1.5],
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[2.5],
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[3.5],
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[4.5]]], dtype=float32)>
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For example, for strides=2 and padding="valid":
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>>> x = tf.constant([1., 2., 3., 4., 5.])
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>>> x = tf.reshape(x, [1, 5, 1])
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>>> x
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<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
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array([[[1.],
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[2.],
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[3.],
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[4.],
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[5.]], dtype=float32)>
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>>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
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... strides=2, padding='valid')
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>>> avg_pool_1d(x)
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<tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy=
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array([[[1.5],
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[3.5]]], dtype=float32)>
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For example, for strides=1 and padding="same":
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>>> x = tf.constant([1., 2., 3., 4., 5.])
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>>> x = tf.reshape(x, [1, 5, 1])
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>>> x
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<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
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array([[[1.],
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[2.],
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[3.],
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[4.],
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[5.]], dtype=float32)>
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>>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
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... strides=1, padding='same')
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>>> avg_pool_1d(x)
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<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
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array([[[1.5],
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[2.5],
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[3.5],
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[4.5],
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[5.]]], dtype=float32)>
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Args:
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pool_size: Integer, size of the average pooling windows.
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strides: Integer, or None. Factor by which to downscale.
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E.g. 2 will halve the input.
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If None, it will default to `pool_size`.
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padding: One of `"valid"` or `"same"` (case-insensitive).
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`"valid"` means no padding. `"same"` results in padding evenly to
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the left/right or up/down of the input such that output has the same
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height/width dimension as the input.
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape
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`(batch, steps, features)` while `channels_first`
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corresponds to inputs with shape
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`(batch, features, steps)`.
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Input shape:
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- If `data_format='channels_last'`:
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3D tensor with shape `(batch_size, steps, features)`.
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- If `data_format='channels_first'`:
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3D tensor with shape `(batch_size, features, steps)`.
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Output shape:
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- If `data_format='channels_last'`:
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3D tensor with shape `(batch_size, downsampled_steps, features)`.
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- If `data_format='channels_first'`:
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3D tensor with shape `(batch_size, features, downsampled_steps)`.
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"""
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def __init__(
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self,
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pool_size=2,
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strides=None,
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padding="valid",
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data_format="channels_last",
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**kwargs
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):
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super().__init__(
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functools.partial(backend.pool2d, pool_mode="avg"),
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pool_size=pool_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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**kwargs
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)
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# Alias
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AvgPool1D = AveragePooling1D
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