如何在Keras中向输入数据添加均匀分布的噪声? [英] How to add a noise with uniform distribution to input data in Keras?

问题描述

我需要在输入数据中添加量化噪声.我经常阅读将这些噪声建模为具有均匀分布的噪声.

I need to add quantization noise to my input data. I read often these kinds of noises are modeled as noise with uniform distribution.

我有一个用Keras实现的编码/解码网络(输入数据是时间序列原始数据)，在Keras中实现了一个可以添加高斯噪声的层(GaussianNoise层)，我可以使用这一层来创建统一的噪音?

I have an encoding/decoding network implemented with Keras (input data is time series raw data), there is a layer implemented in Keras with which you can add Gaussian noise (GaussianNoise layer), can I use this layer to create uniform noise?

如果没有，我还可以使用其他实现的层吗?

If not, are there other implemented layers that I can use?

推荐答案

您可以这样创建自己的图层，

You can create your own layer as such,

import tensorflow as tf

class noiseLayer(tf.keras.layers.Layer):

    def __init__(self,mean,std):
        super(noiseLayer, self).__init__()
        self.mean = mean
        self.std  = std

    def call(self, input):

        mean = self.mean
        std  = self.std

        return input + tf.random.normal(tf.shape(input).numpy(), 
                                    mean = mean,
                                    stddev = std)

X = tf.ones([10,10,10]) * 100
Y = noiseLayer(mean = 0, std = 0.1)(X)

此代码可在最新的Tensorflow 2.0中使用.

This code works in the latest Tensorflow 2.0.

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