Autoencoders can't learn meaningful features

Question

I have 50,000 images such as these two:

They depict graphs of data. I wanted to extract features from these images so I used autoencoder code provided by Theano (deeplearning.net).

The problem is, these autoencoders don't seem to learn any features. I've tried RBM and it's the same.

MNIST dataset provides nice features but my data doesn't seem to yield any. I enclose examples below:

Filters created on MNIST:

Filters created by training on my data:

I've used many different permutations of hidden layer sizes and training epochs but the results are always the same.

Why doesn't it work? Why can't autoencoders extract features from these images?

EDIT:

For anyone that has a similar problem. The solution was really simple and the cause really dumb. I just forgot to rescale the pixel values from RGB encoding to float in range 0 - 1.

Rescaling values solved the problem.

Answer 1

Debugging neural networks usually involves tweaking hyperparameters, visualizing the learned filters, and plotting important metrics. Could you share what hyperparameters you've been using?

• What's your batch size?
• What's your learning rate?
• What type of autoencoder are you're using?
• Have you tried using a Denoising Autoencoder? (What corruption values have you tried?)
• How many hidden layers and of what size?
• What are the dimensions of your input images?

Analyzing the training logs is also useful. Plot a graph of your reconstruction loss (Y-axis) as a function of epoch (X-axis). Is your reconstruction loss converging or diverging?

Here's an example of an autoencoder for human gender classification that was diverging, was stopped after 1500 epochs, had hyperparameters tuned (in this case a reduction in the learning rate), and restarted with the same weights that were diverging and eventually converged.

Here's one that's converging: (we want this)

Vanilla "unconstrained"can run into a problem where they simply learn the identity mapping. That's one of the reasons why the community has created the Denoising, Sparse, and Contractive flavors.

Could you post a small subset of your data here? I'd be more than willing to show you the results from one of my autoencoders.

On a side note: you may want to ask yourself why you're using images of graphs in the first place when those graphs could easily be represented as a vector of data. I.e.,

[0, 13, 15, 11, 2, 9, 6, 5]

If you're able to reformulate the problem like above, you're essentially making the life of your auto-encoder easier. It doesn't first need to learn how to see images before it can try to learn the generating distribution.

Follow up answer (given the data.)

Here are the filters from a 1000 hidden unit, single layer Denoising Autoencoder. Note that some of the filters are seemingly random. That's because I stopped training so early and the network didn't have time to learn those filters.

Here are the hyperparameters that I trained it with:

batch_size = 4
epochs = 100
pretrain_learning_rate = 0.01
finetune_learning_rate = 0.01
corruption_level = 0.2

I stopped pre-training after the 58th epoch because the filters were sufficiently good to post here. If I were you, I would train a full 3-layer Stacked Denoising Autoencoder with a 1000x1000x1000 architecture to start off.

Here are the results from the fine-tuning step:

validation error 24.15 percent
test error 24.15 percent

So at first look, it seems better than chance, however, when we look at the data breakdown between the two labels we see that it has the exact same percent (75.85% profitable and 24.15% unprofitable). So that means the network has learned to simply respond "profitable", regardless of the signal. I would probably train this for a longer time with a larger net to see what happens. Also, it looks like this data is generated from some kind of underlying financial dataset. I would recommend that you look into Recurrent Neural Networks after reformulating your problem into the vectors as described above. RNNs can help capture some of the temporal dependencies that is found in timeseries data like this. Hope this helps.

Answer 2

I don't have enough rep to comment, so I will put this into in answer. I don't know exact reason, however:

1. The pattern in bottom left region looks similar to your second example, and pattern in right bottom corner seems very like to your first example, when inspected closely. The question is, how much variety is in your source data? If all 50 000 images are variations of same pattern, these 3 meaningful feature maps we see can be quite enough for autoencoder to explain and reconstruct all your data.

2. Second, you might want to look at reconstruction error and actual reconstructed images. How good results are? If reconstruction error is low, you have might have an overfit, perhaps due to resons described below (or maybe combination of these 3 patterns is just enough to describe all data in interested in). Otherwise, autoencoder just can't learn how to reconstruct your data and you need larger autoencoder or better training algorithm.