I had been trying to train a version of VAE-GAN for a few weeks and it wasn't working as well as I had hoped it would. I had added an auxiliary output to the discriminator which was attempting to predict the 40 features of each image provided with the celeb-a dataset as suggested in the VAE-GAN paper and I was scaling that loss to try to bring it in line with the GAN discriminator loss, but I was doing that incorrectly so that loss ended up overwhelming the GAN loss. (I was summing, rather than averaging the losses, and the lambda I was using to scale the loss was appropriate for a mean loss, but with 40 features the auxiliary loss was 40x the GAN loss at base, so I needed to divide the lambda by 40 to get the effect I wanted.)

After having corrected that error I am finally making some progress with these models. Below are sample images from two models I am training. The first outputs images at 160x160, the second at 128x128.

I guess the moral of this story is if something isn't working the way you expect it to, double check your math before you continue training it!

Libellés: python, machine_learning, pytorch, gan
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This paper was released over the summer which describes a newly discovered method for obtaining eigenvectors from eigenvalues. While this method only works for Hermitian matrices, previous methods for computing eigenvectors were far more complicated and costly. While relatively, easy, it can be quite costly to determine the dominant eigenvector of a matrix, and this process had to be repeated after removing the dominant eigenvector of the matrix in order to compute additional eigenvectors.

This new method shows that there is a straightforward relationship between the normed squared eigenvalues of a matrix, the eigenvalues of submatrices, and the eigenvectors. I can't stress enough how amazing this is. This will require that all linear algebra textbooks be revised.

I have a numpy implementation of this new method available here.

Libellés: python, linear_algebra
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In one of my classes last semester we had to make a variational auto-encoder for the MNIST dataset. MNIST is a pretty simple and small dataset so that wasn't very difficult. Looking for something more challenging I decided to try to make a face autoencoder.

The first challenge was finding a suitable dataset. There are several public datasets, but they are inconsistent in terms of image size, shape and other important details. I ended up using several datasets - the celebA was the easiest one to use without having to do much work on it. But it is relatively small, consisting of only 200,000 images. I ended up using the ETHZ dataset from Wikipedia and IMDB, which are quite large but the images are all different shapes and sizes. So I had to do some pre-processing of the data to remove unusable images. I removed any images small than the input size I was using of 162x190, and I also removed any images that were wider than they were higher or bigger than 500x500. This dataset also contains some images which have been stretched out at the edges to bizarre proportions. I removed these by deleting any images where the 10th row or column was identical to the first row or column. Finally I resized the large images down to a more reasonable size. This resulted in a dataset of about 390,000 faces, all of which were roughly the right size and shape.

I decided to train my autoencoder as a normal autoencoder rather than a variational one, mostly due to the extra overhead required for the variational layers. I used a latent space of size 4096, and after training for 12 hours a day for a few weeks on Google CoLab the results were surprisingly accurate. Once the model seemed to start overfitting the training data I stopped training it so I could play around with it.

I wanted to try to do interpolation between faces, which was when I realized what the advantage of making the auto-encoder variational was. When I tried to interpolate between faces, because the latent space was not continuous, rather than working as one would expect it was more like adding the faces together. Training the autoencoders as variational forces the latent space to be continuous which makes interpolation possible, so I am currently trying to retrain the model as variational.

Since the non-variational autoencoder had started to overfit the training data I wanted to try to find other ways to improve the quality, so I added an discriminative network which I am also currently training as a GAN, using the autoencoder as the generator. I will update with results of that when I have results worth reporting.

The notebooks used are available on GitHub, and the datasets I used are on Google Cloud Storage, although due to their size and the cost of downloading them they are not publicly available.

Libellés: python, pytorch, autoencoders
3 commentaires

There is one thing that absolutely drives me crazy in Python and that is the fact that you can access a variable that was defined outside of a function from within the function without passing it as an argument. I'm not going to lie, that does come in handy at times - especially when you are working with APIs; but it is still a terrible way to do things.

I like the way scoping is done in C++ - each variable is only valid within the block in which it is declared, but obviously that doesn't work in Python since we don't declare variables. Even so, I think that the only variables that should be available in a function are the ones which are created in it or passed to it as arguments. While we can't modify variables that were declared outside of a function in Python, only access them, if the variable is an object you can modify it using it's methods. So if we have a list and we append to it in a function using list.append() it will actually modify the list, which is crazy.

Being able to access variables that were declared outside of the function makes it easy to write bugs and makes it hard to find them. There have been several times when I have declared a variable outside of a function and then I pass it in to the function with a different name and then use the external variable instead of the one passed into the function. The code will work, but not as expected, and it is difficult to track things like this down.

This makes me see much of the merit in functional programming languages like Scala.

Libellés: coding, python
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After another couple of weeks using PyTorch my initial enthusiasm has somewhat faded. I still like it a lot, but I have encountered many disadvantages. For one I can now see the advantage of TensorFlows static graphs - it makes the API easier to use. Since the graph is completely defined and then compiled you can just tell each layer how many units it should have and it will infer the number of inputs from whatever it's input is. In PyTorch you need to manually specify the inputs and outputs, which isn't a big deal, but makes it more difficult to tune networks since to change the number of units in a layer you need to change the inputs to the next layer, the batch normalization, etc. whereas with TensorFlow you can just change one number and everything is magically adjusted.

I also think that the TensorFlow API is better than PyTorch. There are some things which are very easy to do in TensorFlow which become incredibly complicated with PyTorch, like adding different regularization amounts to different layers. In TensorFlow there is a parameter to the layer that controls the regularization, in PyTorch you apparently need to loop through all of the parameters and know which ones to add what amount of regularization to.

I suppose one could easily get around these limitations with custom functions and such, and it shouldn't be surprising that TensorFlow seems more mature given that it has the weight of Google behind it, is considered the "industry standard", and has been around for longer. But I now see that TensorFlow has some advantages over PyTorch.

Libellés: python, machine_learning, tensorflow, pytorch
1 commentaires