83 lines
3.0 KiB
Python
83 lines
3.0 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 average pooling 2D layer."""
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from keras import backend
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from keras.layers.pooling.base_global_pooling2d import GlobalPooling2D
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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(
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"keras.layers.GlobalAveragePooling2D", "keras.layers.GlobalAvgPool2D"
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)
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class GlobalAveragePooling2D(GlobalPooling2D):
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"""Global average pooling operation for spatial data.
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Examples:
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>>> input_shape = (2, 4, 5, 3)
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>>> x = tf.random.normal(input_shape)
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>>> y = tf.keras.layers.GlobalAveragePooling2D()(x)
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>>> print(y.shape)
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(2, 3)
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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, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, height, width)`.
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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "channels_last".
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keepdims: A boolean, whether to keep the spatial dimensions 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 spatial dimensions are retained with
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length 1.
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The behavior is the same as for `tf.reduce_mean` or `np.mean`.
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Input shape:
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- If `data_format='channels_last'`:
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4D tensor with shape `(batch_size, rows, cols, channels)`.
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- If `data_format='channels_first'`:
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4D tensor with shape `(batch_size, channels, rows, cols)`.
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Output shape:
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- If `keepdims`=False:
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2D tensor with shape `(batch_size, channels)`.
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- If `keepdims`=True:
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- If `data_format='channels_last'`:
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4D tensor with shape `(batch_size, 1, 1, channels)`
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- If `data_format='channels_first'`:
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4D tensor with shape `(batch_size, channels, 1, 1)`
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"""
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def call(self, inputs):
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if self.data_format == "channels_last":
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return backend.mean(inputs, axis=[1, 2], keepdims=self.keepdims)
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else:
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return backend.mean(inputs, axis=[2, 3], keepdims=self.keepdims)
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# Alias
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GlobalAvgPool2D = GlobalAveragePooling2D
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