95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Layer that averages several inputs."""
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from keras.layers.merging.base_merge import _Merge
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.Average")
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class Average(_Merge):
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"""Layer that averages a list of inputs element-wise.
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It takes as input a list of tensors, all of the same shape, and returns
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a single tensor (also of the same shape).
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Example:
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>>> x1 = np.ones((2, 2))
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>>> x2 = np.zeros((2, 2))
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>>> y = tf.keras.layers.Average()([x1, x2])
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>>> y.numpy().tolist()
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[[0.5, 0.5], [0.5, 0.5]]
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Usage in a functional model:
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>>> input1 = tf.keras.layers.Input(shape=(16,))
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>>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
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>>> input2 = tf.keras.layers.Input(shape=(32,))
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>>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
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>>> avg = tf.keras.layers.Average()([x1, x2])
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>>> out = tf.keras.layers.Dense(4)(avg)
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>>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
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Raises:
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ValueError: If there is a shape mismatch between the inputs and the shapes
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cannot be broadcasted to match.
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"""
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def _merge_function(self, inputs):
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output = inputs[0]
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for i in range(1, len(inputs)):
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output += inputs[i]
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return output / len(inputs)
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@keras_export("keras.layers.average")
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def average(inputs, **kwargs):
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"""Functional interface to the `tf.keras.layers.Average` layer.
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Example:
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>>> x1 = np.ones((2, 2))
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>>> x2 = np.zeros((2, 2))
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>>> y = tf.keras.layers.Average()([x1, x2])
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>>> y.numpy().tolist()
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[[0.5, 0.5], [0.5, 0.5]]
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Usage in a functional model:
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>>> input1 = tf.keras.layers.Input(shape=(16,))
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>>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
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>>> input2 = tf.keras.layers.Input(shape=(32,))
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>>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
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>>> avg = tf.keras.layers.Average()([x1, x2])
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>>> out = tf.keras.layers.Dense(4)(avg)
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>>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
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Args:
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inputs: A list of input tensors.
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**kwargs: Standard layer keyword arguments.
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Returns:
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A tensor, the average of the inputs.
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Raises:
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ValueError: If there is a shape mismatch between the inputs and the shapes
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cannot be broadcasted to match.
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"""
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return Average(**kwargs)(inputs)
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