张量/角波中输入的自相关 [英] autocorrelation of the input in tensorflow/keras

问题描述

我有一个1D输入信号.我想将自相关计算为神经网络的一部分，以便在网络内部进一步使用. 我需要对输入本身进行输入的卷积. 在keras自定义层/张量流中执行卷积.我们需要以下参数 data shape is "[batch, in_height, in_width, in_channels]", filter shape is "[filter_height, filter_width, in_channels, out_channels]

I have a 1D input signal. I want to compute autocorrelation as the part of the neural net for further use inside the network. I need to perform convolution of input with input itself. To perform convolution in keras custom layer/ tensorflow. We need the following parameters data shape is "[batch, in_height, in_width, in_channels]", filter shape is "[filter_height, filter_width, in_channels, out_channels]

没有以过滤器形状存在的批次，在我的情况下需要输入

There is no batch present in filter shape, which needs to be input in my case

推荐答案

TensorFlow现在具有auto_correlation功能.它应该在版本1.6中.如果您从源代码构建，则可以立即使用它(请参见例如 github代码).

TensorFlow now has an auto_correlation function. It should be in release 1.6. If you build from source you can use it right now (see e.g. the github code).

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