95 lines
3.1 KiB
Python
95 lines
3.1 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.
|
||
|
|
# ==============================================================================
|
||
|
|
"""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)
|