neural-network - Searching a deep autoencoder example for dimensionality reduction

Question

I want to configure a deep autoencoder in order to reduce the dimensionality of my input data as described in this paper. The layer sizes should be 2000-500-250-125-2-125-250-500-2000 and I want to be able to pull out the activation of the layer in the middle (as described in the paper, I want to use the values as coordinates). The input data consists of binary vectors with a length of 2000 each. Now I'm searching for a working example which I can use as a starting point. I already tried DeepLearning4J but wasn't able to build a satisfying autoencoder. I would be thankful for any suggestions.

Answer 1

你应该看看deeplearning.net上的一些教程。他们有一个带有代码的Stacked Denoising Autoencoder示例。所有的教程都是用 Theano 编写的，Theano是一个可以为你生成 GPU 代码的科学计算库。

这是在LFW上训练的 200x200x200 SdA 的第 3 层上的学习权重的可视化示例。您可以简单地修改上面链接的 SdA 教程代码以获得相同的结果。