在凯拉斯修剪 [英] Pruning in Keras

问题描述

我正在尝试使用优先考虑预测性能的Keras设计神经网络，并且通过进一步减少每层的层数和节点数，我无法获得足够高的精度.我注意到我的很大一部分权重实际上为零(> 95％).有没有一种方法可以修剪密集的层以减少预测时间?

I'm trying to design a neural network using Keras with priority on prediction performance, and I cannot get sufficiently high accuracy by further reducing the number of layers and nodes per layer. I have noticed that very large portion of my weights are effectively zero (>95%). Is there a way to prune dense layers in hope of reducing prediction time?

推荐答案

不是专用方法:(

目前，使用Keras尚无简便(专用)的方法.

There's currently no easy (dedicated) way of doing this with Keras.

https://groups.google.com上正在进行讨论. /forum/#！topic/keras-users/oEecCWayJrM .

您可能对本文也有兴趣: https://arxiv.org/pdf/1608.04493v1 .pdf .

You may also be interested in this paper: https://arxiv.org/pdf/1608.04493v1.pdf.

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