Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/pooling/average_pooling1d.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.
# ==============================================================================
"""Average pooling 1D layer."""
import functools
from keras import backend
from keras.layers.pooling.base_pooling1d import Pooling1D
# isort: off
from tensorflow.python.util.tf_export import keras_export
@keras_export("keras.layers.AveragePooling1D", "keras.layers.AvgPool1D")
class AveragePooling1D(Pooling1D):
"""Average pooling for temporal data.
Downsamples the input representation by taking the average value over the
window defined by `pool_size`. The window is shifted by `strides`. The
resulting output when using "valid" padding option has a shape of:
`output_shape = (input_shape - pool_size + 1) / strides)`
The resulting output shape when using the "same" padding option is:
`output_shape = input_shape / strides`
For example, for strides=1 and padding="valid":
>>> x = tf.constant([1., 2., 3., 4., 5.])
>>> x = tf.reshape(x, [1, 5, 1])
>>> x
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
array([[[1.],
[2.],
[3.],
[4.],
[5.]], dtype=float32)>
>>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
... strides=1, padding='valid')
>>> avg_pool_1d(x)
<tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy=
array([[[1.5],
[2.5],
[3.5],
[4.5]]], dtype=float32)>
For example, for strides=2 and padding="valid":
>>> x = tf.constant([1., 2., 3., 4., 5.])
>>> x = tf.reshape(x, [1, 5, 1])
>>> x
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
array([[[1.],
[2.],
[3.],
[4.],
[5.]], dtype=float32)>
>>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
... strides=2, padding='valid')
>>> avg_pool_1d(x)
<tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy=
array([[[1.5],
[3.5]]], dtype=float32)>
For example, for strides=1 and padding="same":
>>> x = tf.constant([1., 2., 3., 4., 5.])
>>> x = tf.reshape(x, [1, 5, 1])
>>> x
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
array([[[1.],
[2.],
[3.],
[4.],
[5.]], dtype=float32)>
>>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
... strides=1, padding='same')
>>> avg_pool_1d(x)
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
array([[[1.5],
[2.5],
[3.5],
[4.5],
[5.]]], dtype=float32)>
Args:
pool_size: Integer, size of the average pooling windows.
strides: Integer, or None. Factor by which to downscale.
E.g. 2 will halve the input.
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, steps, features)` while `channels_first`
corresponds to inputs with shape
`(batch, features, steps)`.
Input shape:
- If `data_format='channels_last'`:
3D tensor with shape `(batch_size, steps, features)`.
- If `data_format='channels_first'`:
3D tensor with shape `(batch_size, features, steps)`.
Output shape:
- If `data_format='channels_last'`:
3D tensor with shape `(batch_size, downsampled_steps, features)`.
- If `data_format='channels_first'`:
3D tensor with shape `(batch_size, features, downsampled_steps)`.
"""
def __init__(
self,
pool_size=2,
strides=None,
padding="valid",
data_format="channels_last",
**kwargs
):
super().__init__(
functools.partial(backend.pool2d, pool_mode="avg"),
pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
**kwargs
)
# Alias
AvgPool1D = AveragePooling1D