Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/merging/average.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.
# ==============================================================================
"""Layer that averages several inputs."""
from keras.layers.merging.base_merge import _Merge
# isort: off
from tensorflow.python.util.tf_export import keras_export
@keras_export("keras.layers.Average")
class Average(_Merge):
"""Layer that averages a list of inputs element-wise.
It takes as input a list of tensors, all of the same shape, and returns
a single tensor (also of the same shape).
Example:
>>> x1 = np.ones((2, 2))
>>> x2 = np.zeros((2, 2))
>>> y = tf.keras.layers.Average()([x1, x2])
>>> y.numpy().tolist()
[[0.5, 0.5], [0.5, 0.5]]
Usage in a functional model:
>>> input1 = tf.keras.layers.Input(shape=(16,))
>>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
>>> input2 = tf.keras.layers.Input(shape=(32,))
>>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
>>> avg = tf.keras.layers.Average()([x1, x2])
>>> out = tf.keras.layers.Dense(4)(avg)
>>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
Raises:
ValueError: If there is a shape mismatch between the inputs and the shapes
cannot be broadcasted to match.
"""
def _merge_function(self, inputs):
output = inputs[0]
for i in range(1, len(inputs)):
output += inputs[i]
return output / len(inputs)
@keras_export("keras.layers.average")
def average(inputs, **kwargs):
"""Functional interface to the `tf.keras.layers.Average` layer.
Example:
>>> x1 = np.ones((2, 2))
>>> x2 = np.zeros((2, 2))
>>> y = tf.keras.layers.Average()([x1, x2])
>>> y.numpy().tolist()
[[0.5, 0.5], [0.5, 0.5]]
Usage in a functional model:
>>> input1 = tf.keras.layers.Input(shape=(16,))
>>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
>>> input2 = tf.keras.layers.Input(shape=(32,))
>>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
>>> avg = tf.keras.layers.Average()([x1, x2])
>>> out = tf.keras.layers.Dense(4)(avg)
>>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
Args:
inputs: A list of input tensors.
**kwargs: Standard layer keyword arguments.
Returns:
A tensor, the average of the inputs.
Raises:
ValueError: If there is a shape mismatch between the inputs and the shapes
cannot be broadcasted to match.
"""
return Average(**kwargs)(inputs)