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# 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.
# ==============================================================================
"""Contains the UniformNoise layer."""
import tensorflow.compat.v2 as tf
from tensorflow.keras import backend
from tensorflow.keras.layers import Layer
class UniformNoise(Layer):
"""Apply additive zero-centered uniform noise.
This is useful to mitigate overfitting
(you could see it as a form of random data augmentation).
Gaussian Noise (GS) is a natural choice as corruption process
for real valued inputs.
As it is a regularization layer, it is only active at training time.
Args:
stddev: Float, standard deviation of the noise distribution.
seed: Integer, optional random seed to enable deterministic behavior.
Call arguments:
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in
training mode (adding noise) or in inference mode (doing nothing).
Input shape:
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape:
Same shape as input.
"""
def __init__(self, stddev=0.5, seed=None, **kwargs):
super().__init__(**kwargs)
self.supports_masking = True
self.stddev = stddev
def call(self, inputs, training=None):
def noised():
return inputs + backend.random_uniform(
shape=tf.shape(inputs),
minval=-self.stddev,
maxval=self.stddev,
dtype=inputs.dtype,
)
return backend.in_train_phase(noised, inputs, training=training)
def get_config(self):
config = {"stddev": self.stddev}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
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