Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/pooling/max_pooling2d.py

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2023-06-19 00:49:18 +02:00
# 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