84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
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# 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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"""Contains the GaussianDropout layer."""
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import numpy as np
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.engine import base_layer
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from keras.utils import tf_utils
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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.GaussianDropout")
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class GaussianDropout(base_layer.BaseRandomLayer):
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"""Apply multiplicative 1-centered Gaussian noise.
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As it is a regularization layer, it is only active at training time.
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Args:
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rate: Float, drop probability (as with `Dropout`).
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The multiplicative noise will have
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standard deviation `sqrt(rate / (1 - rate))`.
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seed: Integer, optional random seed to enable deterministic behavior.
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Call arguments:
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inputs: Input tensor (of any rank).
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training: Python boolean indicating whether the layer should behave in
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training mode (adding dropout) or in inference mode (doing nothing).
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Input shape:
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Arbitrary. Use the keyword argument `input_shape`
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(tuple of integers, does not include the samples axis)
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when using this layer as the first layer in a model.
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Output shape:
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Same shape as input.
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"""
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def __init__(self, rate, seed=None, **kwargs):
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super().__init__(seed=seed, **kwargs)
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self.supports_masking = True
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self.rate = rate
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self.seed = seed
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def call(self, inputs, training=None):
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if 0 < self.rate < 1:
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def noised():
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stddev = np.sqrt(self.rate / (1.0 - self.rate))
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return inputs * self._random_generator.random_normal(
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shape=tf.shape(inputs),
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mean=1.0,
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stddev=stddev,
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dtype=inputs.dtype,
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)
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return backend.in_train_phase(noised, inputs, training=training)
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return inputs
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def get_config(self):
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config = {"rate": self.rate, "seed": self.seed}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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return input_shape
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