Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/pooling/average_pooling1d.py

# 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