89 lines
3.1 KiB
Python
89 lines
3.1 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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"""Global max pooling 1D layer."""
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from keras import backend
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from keras.layers.pooling.base_global_pooling1d import GlobalPooling1D
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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.GlobalMaxPool1D", "keras.layers.GlobalMaxPooling1D")
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class GlobalMaxPooling1D(GlobalPooling1D):
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"""Global max pooling operation for 1D temporal data.
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Downsamples the input representation by taking the maximum value over
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the time dimension.
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For example:
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>>> x = tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
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>>> x = tf.reshape(x, [3, 3, 1])
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>>> x
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<tf.Tensor: shape=(3, 3, 1), dtype=float32, numpy=
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array([[[1.], [2.], [3.]],
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[[4.], [5.], [6.]],
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[[7.], [8.], [9.]]], dtype=float32)>
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>>> max_pool_1d = tf.keras.layers.GlobalMaxPooling1D()
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>>> max_pool_1d(x)
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<tf.Tensor: shape=(3, 1), dtype=float32, numpy=
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array([[3.],
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[6.],
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[9.], dtype=float32)>
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Args:
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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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keepdims: A boolean, whether to keep the temporal dimension or not.
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If `keepdims` is `False` (default), the rank of the tensor is reduced
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for spatial dimensions.
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If `keepdims` is `True`, the temporal dimension are retained with
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length 1.
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The behavior is the same as for `tf.reduce_max` or `np.max`.
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Input shape:
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- If `data_format='channels_last'`:
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3D tensor with shape:
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`(batch_size, steps, features)`
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- If `data_format='channels_first'`:
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3D tensor with shape:
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`(batch_size, features, steps)`
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Output shape:
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- If `keepdims`=False:
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2D tensor with shape `(batch_size, features)`.
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- If `keepdims`=True:
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- If `data_format='channels_last'`:
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3D tensor with shape `(batch_size, 1, features)`
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- If `data_format='channels_first'`:
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3D tensor with shape `(batch_size, features, 1)`
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"""
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def call(self, inputs):
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steps_axis = 1 if self.data_format == "channels_last" else 2
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return backend.max(inputs, axis=steps_axis, keepdims=self.keepdims)
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# Alias
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GlobalMaxPool1D = GlobalMaxPooling1D
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