172 lines
6.2 KiB
Python
172 lines
6.2 KiB
Python
![]() |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||
|
#
|
||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
# you may not use this file except in compliance with the License.
|
||
|
# You may obtain a copy of the License at
|
||
|
#
|
||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
#
|
||
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
# See the License for the specific language governing permissions and
|
||
|
# limitations under the License.
|
||
|
# ==============================================================================
|
||
|
"""Max pooling 2D layer."""
|
||
|
|
||
|
|
||
|
import tensorflow.compat.v2 as tf
|
||
|
|
||
|
from keras.layers.pooling.base_pooling2d import Pooling2D
|
||
|
|
||
|
# isort: off
|
||
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
||
|
|
||
|
@keras_export("keras.layers.MaxPool2D", "keras.layers.MaxPooling2D")
|
||
|
class MaxPooling2D(Pooling2D):
|
||
|
"""Max pooling operation for 2D spatial data.
|
||
|
|
||
|
Downsamples the input along its spatial dimensions (height and width)
|
||
|
by taking the maximum value over an input window
|
||
|
(of size defined by `pool_size`) for each channel of the input.
|
||
|
The window is shifted by `strides` along each dimension.
|
||
|
|
||
|
The resulting output,
|
||
|
when using the `"valid"` padding option, has a spatial shape
|
||
|
(number of rows or columns) of:
|
||
|
`output_shape = math.floor((input_shape - pool_size) / strides) + 1`
|
||
|
(when `input_shape >= pool_size`)
|
||
|
|
||
|
The resulting output shape when using the `"same"` padding option is:
|
||
|
`output_shape = math.floor((input_shape - 1) / strides) + 1`
|
||
|
|
||
|
For example, for `strides=(1, 1)` and `padding="valid"`:
|
||
|
|
||
|
>>> x = tf.constant([[1., 2., 3.],
|
||
|
... [4., 5., 6.],
|
||
|
... [7., 8., 9.]])
|
||
|
>>> x = tf.reshape(x, [1, 3, 3, 1])
|
||
|
>>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
|
||
|
... strides=(1, 1), padding='valid')
|
||
|
>>> max_pool_2d(x)
|
||
|
<tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
|
||
|
array([[[[5.],
|
||
|
[6.]],
|
||
|
[[8.],
|
||
|
[9.]]]], dtype=float32)>
|
||
|
|
||
|
For example, for `strides=(2, 2)` and `padding="valid"`:
|
||
|
|
||
|
>>> x = tf.constant([[1., 2., 3., 4.],
|
||
|
... [5., 6., 7., 8.],
|
||
|
... [9., 10., 11., 12.]])
|
||
|
>>> x = tf.reshape(x, [1, 3, 4, 1])
|
||
|
>>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
|
||
|
... strides=(2, 2), padding='valid')
|
||
|
>>> max_pool_2d(x)
|
||
|
<tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
|
||
|
array([[[[6.],
|
||
|
[8.]]]], dtype=float32)>
|
||
|
|
||
|
Usage Example:
|
||
|
|
||
|
>>> input_image = tf.constant([[[[1.], [1.], [2.], [4.]],
|
||
|
... [[2.], [2.], [3.], [2.]],
|
||
|
... [[4.], [1.], [1.], [1.]],
|
||
|
... [[2.], [2.], [1.], [4.]]]])
|
||
|
>>> output = tf.constant([[[[1], [0]],
|
||
|
... [[0], [1]]]])
|
||
|
>>> model = tf.keras.models.Sequential()
|
||
|
>>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
|
||
|
... input_shape=(4, 4, 1)))
|
||
|
>>> model.compile('adam', 'mean_squared_error')
|
||
|
>>> model.predict(input_image, steps=1)
|
||
|
array([[[[2.],
|
||
|
[4.]],
|
||
|
[[4.],
|
||
|
[4.]]]], dtype=float32)
|
||
|
|
||
|
For example, for stride=(1, 1) and padding="same":
|
||
|
|
||
|
>>> x = tf.constant([[1., 2., 3.],
|
||
|
... [4., 5., 6.],
|
||
|
... [7., 8., 9.]])
|
||
|
>>> x = tf.reshape(x, [1, 3, 3, 1])
|
||
|
>>> max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
|
||
|
... strides=(1, 1), padding='same')
|
||
|
>>> max_pool_2d(x)
|
||
|
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
|
||
|
array([[[[5.],
|
||
|
[6.],
|
||
|
[6.]],
|
||
|
[[8.],
|
||
|
[9.],
|
||
|
[9.]],
|
||
|
[[8.],
|
||
|
[9.],
|
||
|
[9.]]]], dtype=float32)>
|
||
|
|
||
|
Args:
|
||
|
pool_size: integer or tuple of 2 integers,
|
||
|
window size over which to take the maximum.
|
||
|
`(2, 2)` will take the max value over a 2x2 pooling window.
|
||
|
If only one integer is specified, the same window length
|
||
|
will be used for both dimensions.
|
||
|
strides: Integer, tuple of 2 integers, or None.
|
||
|
Strides values. Specifies how far the pooling window moves
|
||
|
for each pooling step. If None, it will default to `pool_size`.
|
||
|
padding: One of `"valid"` or `"same"` (case-insensitive).
|
||
|
`"valid"` means no padding. `"same"` results in padding evenly to
|
||
|
the left/right or up/down of the input such that output has the same
|
||
|
height/width dimension as the input.
|
||
|
data_format: A string,
|
||
|
one of `channels_last` (default) or `channels_first`.
|
||
|
The ordering of the dimensions in the inputs.
|
||
|
`channels_last` corresponds to inputs with shape
|
||
|
`(batch, height, width, channels)` while `channels_first`
|
||
|
corresponds to inputs with shape
|
||
|
`(batch, channels, height, width)`.
|
||
|
It defaults to the `image_data_format` value found in your
|
||
|
Keras config file at `~/.keras/keras.json`.
|
||
|
If you never set it, then it will be "channels_last".
|
||
|
|
||
|
Input shape:
|
||
|
- If `data_format='channels_last'`:
|
||
|
4D tensor with shape `(batch_size, rows, cols, channels)`.
|
||
|
- If `data_format='channels_first'`:
|
||
|
4D tensor with shape `(batch_size, channels, rows, cols)`.
|
||
|
|
||
|
Output shape:
|
||
|
- If `data_format='channels_last'`:
|
||
|
4D tensor with shape `(batch_size, pooled_rows, pooled_cols, channels)`.
|
||
|
- If `data_format='channels_first'`:
|
||
|
4D tensor with shape `(batch_size, channels, pooled_rows, pooled_cols)`.
|
||
|
|
||
|
Returns:
|
||
|
A tensor of rank 4 representing the maximum pooled values. See above for
|
||
|
output shape.
|
||
|
"""
|
||
|
|
||
|
def __init__(
|
||
|
self,
|
||
|
pool_size=(2, 2),
|
||
|
strides=None,
|
||
|
padding="valid",
|
||
|
data_format=None,
|
||
|
**kwargs
|
||
|
):
|
||
|
super().__init__(
|
||
|
tf.compat.v1.nn.max_pool,
|
||
|
pool_size=pool_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
**kwargs
|
||
|
)
|
||
|
|
||
|
|
||
|
# Alias
|
||
|
|
||
|
MaxPool2D = MaxPooling2D
|