172 lines
6.2 KiB
Python
172 lines
6.2 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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"""Max pooling 2D layer."""
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import tensorflow.compat.v2 as tf
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from keras.layers.pooling.base_pooling2d import Pooling2D
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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.MaxPool2D", "keras.layers.MaxPooling2D")
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class MaxPooling2D(Pooling2D):
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"""Max pooling operation for 2D spatial data.
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Downsamples the input along its spatial dimensions (height and width)
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by taking the maximum value over an input window
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(of size defined by `pool_size`) for each channel of the input.
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The window is shifted by `strides` along each dimension.
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The resulting output,
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when using the `"valid"` padding option, has a spatial shape
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(number of rows or columns) of:
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`output_shape = math.floor((input_shape - pool_size) / strides) + 1`
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(when `input_shape >= pool_size`)
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The resulting output shape when using the `"same"` padding option is:
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`output_shape = math.floor((input_shape - 1) / strides) + 1`
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For example, for `strides=(1, 1)` and `padding="valid"`:
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>>> x = tf.constant([[1., 2., 3.],
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... [4., 5., 6.],
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... [7., 8., 9.]])
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>>> x = tf.reshape(x, [1, 3, 3, 1])
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>>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
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... strides=(1, 1), padding='valid')
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>>> max_pool_2d(x)
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<tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
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array([[[[5.],
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[6.]],
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[[8.],
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[9.]]]], dtype=float32)>
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For example, for `strides=(2, 2)` and `padding="valid"`:
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>>> x = tf.constant([[1., 2., 3., 4.],
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... [5., 6., 7., 8.],
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... [9., 10., 11., 12.]])
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>>> x = tf.reshape(x, [1, 3, 4, 1])
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>>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
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... strides=(2, 2), padding='valid')
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>>> max_pool_2d(x)
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<tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
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array([[[[6.],
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[8.]]]], dtype=float32)>
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Usage Example:
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>>> input_image = tf.constant([[[[1.], [1.], [2.], [4.]],
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... [[2.], [2.], [3.], [2.]],
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... [[4.], [1.], [1.], [1.]],
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... [[2.], [2.], [1.], [4.]]]])
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>>> output = tf.constant([[[[1], [0]],
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... [[0], [1]]]])
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>>> model = tf.keras.models.Sequential()
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>>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
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... input_shape=(4, 4, 1)))
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>>> model.compile('adam', 'mean_squared_error')
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>>> model.predict(input_image, steps=1)
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array([[[[2.],
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[4.]],
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[[4.],
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[4.]]]], dtype=float32)
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For example, for stride=(1, 1) and padding="same":
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>>> x = tf.constant([[1., 2., 3.],
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... [4., 5., 6.],
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... [7., 8., 9.]])
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>>> x = tf.reshape(x, [1, 3, 3, 1])
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>>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
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... strides=(1, 1), padding='same')
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>>> max_pool_2d(x)
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<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
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array([[[[5.],
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[6.],
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[6.]],
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[[8.],
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[9.],
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[9.]],
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[[8.],
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[9.],
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[9.]]]], dtype=float32)>
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Args:
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pool_size: integer or tuple of 2 integers,
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window size over which to take the maximum.
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`(2, 2)` will take the max value over a 2x2 pooling window.
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If only one integer is specified, the same window length
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will be used for both dimensions.
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strides: Integer, tuple of 2 integers, or None.
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Strides values. Specifies how far the pooling window moves
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for each pooling step. 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, 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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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 `data_format='channels_last'`:
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4D tensor with shape `(batch_size, pooled_rows, pooled_cols, channels)`.
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- If `data_format='channels_first'`:
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4D tensor with shape `(batch_size, channels, pooled_rows, pooled_cols)`.
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Returns:
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A tensor of rank 4 representing the maximum pooled values. See above for
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output shape.
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"""
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def __init__(
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self,
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pool_size=(2, 2),
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strides=None,
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padding="valid",
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data_format=None,
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**kwargs
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):
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super().__init__(
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tf.compat.v1.nn.max_pool,
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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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MaxPool2D = MaxPooling2D
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