Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/pooling/base_pooling1d.py
2023-06-19 00:49:18 +02:00

110 lines
4.0 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.
# ==============================================================================
"""Private base class for pooling 1D layers."""
import tensorflow.compat.v2 as tf
from keras import backend
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
from keras.utils import conv_utils
class Pooling1D(Layer):
"""Pooling layer for arbitrary pooling functions, for 1D inputs.
This class only exists for code reuse. It will never be an exposed API.
Args:
pool_function: The pooling function to apply, e.g. `tf.nn.max_pool2d`.
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
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)`.
name: A string, the name of the layer.
"""
def __init__(
self,
pool_function,
pool_size,
strides,
padding="valid",
data_format="channels_last",
name=None,
**kwargs
):
super().__init__(name=name, **kwargs)
if data_format is None:
data_format = backend.image_data_format()
if strides is None:
strides = pool_size
self.pool_function = pool_function
self.pool_size = conv_utils.normalize_tuple(pool_size, 1, "pool_size")
self.strides = conv_utils.normalize_tuple(
strides, 1, "strides", allow_zero=True
)
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.input_spec = InputSpec(ndim=3)
def call(self, inputs):
pad_axis = 2 if self.data_format == "channels_last" else 3
inputs = tf.expand_dims(inputs, pad_axis)
outputs = self.pool_function(
inputs,
self.pool_size + (1,),
strides=self.strides + (1,),
padding=self.padding,
data_format=self.data_format,
)
return tf.squeeze(outputs, pad_axis)
def compute_output_shape(self, input_shape):
input_shape = tf.TensorShape(input_shape).as_list()
if self.data_format == "channels_first":
steps = input_shape[2]
features = input_shape[1]
else:
steps = input_shape[1]
features = input_shape[2]
length = conv_utils.conv_output_length(
steps, self.pool_size[0], self.padding, self.strides[0]
)
if self.data_format == "channels_first":
return tf.TensorShape([input_shape[0], features, length])
else:
return tf.TensorShape([input_shape[0], length, features])
def get_config(self):
config = {
"strides": self.strides,
"pool_size": self.pool_size,
"padding": self.padding,
"data_format": self.data_format,
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))