Recommending music on Spotify with deep learning

This summer, I’m interning at Spotify in New York City, where I’m working on content-based music recommendation using convolutional neural networks. In this post, I’ll explain my approach and show some preliminary results.


This is going to be a long post, so here’s an overview of the different sections. If you want to skip ahead, just click the section title to go there.


Collaborative filtering

Traditionally, Spotify has relied mostly on collaborative filtering approaches to power their recommendations. The idea of collaborative filtering is to determine the users’ preferences from historical usage data. For example, if two users listen to largely the same set of songs, their tastes are probably similar. Conversely, if two songs are listened to by the same group of users, they probably sound similar. This kind of information can be exploited to make recommendations.

Pure collaborative filtering approaches do not use any kind of information about the items that are being recommended, except for the consumption patterns associated with them: they are content-agnostic. This makes these approaches widely applicable: the same type of model can be used to recommend books, movies or music, for example.

Unfortunately, this also turns out to be their biggest flaw. Because of their reliance on usage data, popular items will be much easier to recommend than unpopular items, as there is more usage data available for them. This is usually the opposite of what we want. For the same reason, the recommendations can often be rather boring and predictable.

Another issue that is more specific to music, is the heterogeneity of content with similar usage patterns. For example, users may listen to entire albums in one go, but albums may contain intro tracks, outro tracks, interludes, cover songs and remixes. These items are atypical for the artist in question, so they aren’t good recommendations. Collaborative filtering algorithms will not pick up on this.

But perhaps the biggest problem is that new and unpopular songs cannot be recommended: if there is no usage data to analyze, the collaborative filtering approach breaks down. This is the so-called cold-start problem. We want to be able to recommend new music right when it is released, and we want to tell listeners about awesome bands they have never heard of. To achieve these goals, we will need to use a different approach.

Content-based recommendation

Recently, Spotify has shown considerable interest in incorporating other sources of information into their recommendation pipeline to mitigate some of these problems, as evidenced by their acquisition of music intelligence platform company The Echo Nest a few months back. There are many different kinds of information associated with music that could aid recommendation: tags, artist and album information, lyrics, text mined from the web (reviews, interviews, …), and the audio signal itself.

Of all these information sources, the audio signal is probably the most difficult to use effectively. There is quite a large semantic gap between music audio on the one hand, and the various aspects of music that affect listener preferences on the other hand. Some of these are fairly easy to extract from audio signals, such as the genre of the music and the instruments used. Others are a little more challenging, such as the mood of the music, and the year (or time period) of release. A couple are practically impossible to obtain from audio: the geographical location of the artist and lyrical themes, for example.

Despite all these challenges, it is clear that the actual sound of a song will play a very big role in determining whether or not you enjoy listening to it - so it seems like a good idea to try to predict who will enjoy a song by analyzing the audio signal.

Predicting listening preferences with deep learning

In December last year, my colleague Aäron van den Oord and I published a paper on this topic at NIPS, titled Deep content-based music recommendation. We tried to tackle the problem of predicting listening preferences from audio signals by training a regression model to predict the latent representations of songs that were obtained from a collaborative filtering model. This way, we could predict the representation of a song in the collaborative filtering space, even if no usage data was available. (As you can probably infer from the title of the paper, the regression model in question was a deep neural network.)

The underlying idea of this approach is that many collaborative filtering models work by projecting both the listeners and the songs into a shared low-dimensional latent space. The position of a song in this space encodes all kinds of information that affects listening preferences. If two songs are close together in this space, they are probably similar. If a song is close to a user, it is probably a good recommendation for that user (provided that they haven’t heard it yet). If we can predict the position of a song in this space from audio, we can recommend it to the right audience without having to rely on historical usage data.

We visualized this in the paper by projecting the predictions of our model in the latent space down to two dimensions using the t-SNE algorithm. As you can see below on the resulting map, similar songs cluster together. Rap music can be found mostly in the top left corner, whereas electronic artists congregate at the bottom of the map.

t-SNE visualization of user listening patterns predicted from audio.
t-SNE visualization of the latent space (middle). A few close-ups show artists whose songs are projected in specific areas. Taken from Deep content-based music recommendation, Aäron van den Oord, Sander Dieleman and Benjamin Schrauwen, NIPS 2013.

Scaling up

The deep neural network that we trained for the paper consisted of two convolutional layers and two fully connected layers. The input consisted of spectrograms of 3 second fragments of audio. To get a prediction for a longer clip, we just split it up into 3 second windows and averaged the predictions across these windows.

At Spotify, I have access to a larger dataset of songs, and a bunch of different latent factor representations obtained from various collaborative filtering models. They also got me a nice GPU to run my experiments on. This has allowed me to scale things up quite a bit. I am currently training convolutional neural networks (convnets) with 7 or 8 layers in total, using much larger intermediate representations and many more parameters.


Below is an example of an architecture that I’ve tried out, which I will describe in more detail. It has four convolutional layers and three dense layers. As you will see, there are some important differences between convnets designed for audio signals and their more traditional counterparts used for computer vision tasks.

Warning: gory details ahead! Feel free to skip ahead to ‘Analysis’ if you don’t care about things like ReLUs, max-pooling and minibatch gradient descent.

One of the convolutional neural network architectures I've tried out.
One of the convolutional neural network architectures I've tried out for latent factor prediction. The time axis (which is convolved over) is vertical.

The input to the network consists of mel-spectrograms, with 599 frames and 128 frequency bins. A mel-spectrograms is a kind of time-frequency representation. It is obtained from an audio signal by computing the Fourier transforms of short, overlapping windows. Each of these Fourier transforms constitutes a frame. These successive frames are then concatenated into a matrix to form the spectrogram. Finally, the frequency axis is changed from a linear scale to a mel scale to reduce the dimensionality, and the magnitudes are scaled logarithmically.

The convolutional layers are displayed as red rectangles delineating the shape of the filters that slide across their inputs. They have rectified linear units (ReLUs, with activation function max(0, x)). Note that all these convolutions are one-dimensional; the convolution happens only in the time dimension, not in the frequency dimension. Although it is technically possible to convolve along both axes of the spectrogram, I am not currently doing this. It is important to realize that the two axes of a spectrogram have different meanings (time vs. frequency), which is not the case for images. As a result, it doesn’t really make sense to use square filters, which is what is typically done in convnets for image data.

Between the convolutional layers, there are max-pooling operations to downsample the intermediate representations in time, and to add some time invariance in the process. These are indicated with ‘MP’. As you can see I used a filter size of 4 frames in every convolutional layer, with max-pooling with a pool size of 4 between the first and second convolutional layers (mainly for performance reasons), and with a pool size of 2 between the other layers.

After the last convolutional layer, I added a global temporal pooling layer. This layer pools across the entire time axis, effectively computing statistics of the learned features across time. I included three different pooling functions: the mean, the maximum and the L2-norm.

I did this because the absolute location of features detected in the audio signal is not particularly relevant for the task at hand. This is not the case in image classification: in an image, it can be useful to know roughly where a particular feature was detected. For example, a feature detecting clouds would be more likely to activate for the top half of an image. If it activates in the bottom half, maybe it is actually detecting a sheep instead. For music recommendation, we are typically only interested in the overall presence or absence of certain features in the music, so it makes sense to perform pooling across time.

Another way to approach this problem would be to train the network on short audio fragments, and average the outputs across windows for longer fragments, as we did in the NIPS paper. However, incorporating the pooling into the model seems like a better idea, because it allows for this step to be taken into account during learning.

The globally pooled features are fed into a series of fully-connected layers with 2048 rectified linear units. In this network, I have two of them. The last layer of the network is the output layer, which predicts 40 latent factors obtained from the vector_exp algorithm, one of the various collaborative filtering algorithms that are used at Spotify.


The network is trained to minimize the mean squared error (MSE) between the latent factor vectors from the collaborative filtering model and the predictions from audio. These vectors are first normalized so they have a unit norm. This is done to reduce the influence of song popularity (the norms of latent factor vectors tend to be correlated with song popularity for many collaborative filtering models). Dropout is used in the dense layers for regularization.

The dataset I am currently using consists of mel-spectrograms of 30 second excerpts extracted from the middle of the 1 million most popular tracks on Spotify. I am using about half of these for training (0.5M), about 5000 for online validation, and the remainder for testing. During training, the data is augmented by slightly cropping the spectrograms along the time axis with a random offset.

The network is implemented in Theano, and trained using minibatch gradient descent with Nesterov momentum on a NVIDIA GeForce GTX 780Ti GPU. Data loading and augmentation happens in a separate process, so while the GPU is training on a chunk of data, the next one can be loaded in parallel. About 750000 gradient updates are performed in total. I don’t remember exactly how long this particular architecture took to train, but all of the ones I’ve tried have taken between 18 and 36 hours.


As I mentioned before, this is just one example of an architecture that I’ve tried. Some other things I have tried / will try include:

  • More layers!
  • Using maxout units instead of rectified linear units.
  • Using stochastic pooling instead of max-pooling.
  • Incorporating L2 normalization into the output layer of the network.
  • Data augmentation by stretching or compressing the spectrograms across time.
  • Concatenating multiple latent factor vectors obtained from different collaborative filtering models.

Here are some things that didn’t work quite as well as I’d hoped:

  • Adding ‘bypass’ connections from all convolutional layers to the fully connected part of the network, with global temporal pooling in between. The underlying assumption was that statistics about low-level features could also be useful for recommendation, but unfortunately this hampered learning too much.
  • Predicting the conditional variance of the factors as in mixture density networks, to get confidence estimates for the predictions and to identify songs for which latent factor prediction is difficult. Unfortunately this seemed to make training quite a lot harder, and the resulting confidence estimates did not behave as expected.

Analysis: what is it learning?

Now for the cool part: what are these networks learning? What do the features look like? The main reason I chose to tackle this problem with convnets, is because I believe that music recommendation from audio signals is a pretty complex problem bridging many levels of abstraction. My hope was that successive layers of the network would learn progressively more complex and invariant features, as they do for image classification problems.

It looks like that’s exactly what is happening. First, let’s take a look at the first convolutional layer, which learns a set of filters that are applied directly to the input spectrograms. These filters are easy to visualize. They are shown in the image below. Click for a high resolution version (5584x562, ~600kB). Negative values are red, positive values are blue and white is zero. Note that each filter is only four frames wide. The individual filters are separated by dark red vertical lines.

Filters learned in the first convolutional layer.
Visualization of the filters learned in the first convolutional layer. The time axis is horizontal, the frequency axis is vertical (frequency increases from top to bottom). Click for a high resolution version (5584x562, ~600kB).

From this representation, we can see that a lot of the filters pick up harmonic content, which manifests itself as parallel red and blue bands at different frequencies. Sometimes, these bands are are slanted up or down, indicating the presence of rising and falling pitches. It turns out that these filters tend to detect human voices.

Playlists for low-level features: maximal activation

To get a better idea of what the filters learn, I made some playlists with songs from the test set that maximally activate them. Below are a few examples. There are 256 filters in the first layer of the network, which I numbered from 0 to 255. Note that this numbering is arbitrary, as they are unordered.

These four playlists were obtained by finding songs that maximally activate a given filter in the 30 seconds that were analyzed. I selected a few interesting looking filters from the first convolutional layer and computed the feature representations for each of these, and then searched for the maximal activations across the entire test set. Note that you should listen to the middle of the tracks to hear what the filters are picking up on, as this is the part of the audio signal that was analyzed.

All of the Spotify playlists below should have 10 tracks. Some of them may not be available in all countries due to licensing issues.

Filter 14: vibrato singing
Filter 242: ambience
Filter 250: vocal thirds
Filter 253: bass drums
Closeup of filters 14, 242, 250 and 253.
Closeup of filters 14, 242, 250 and 253. Click for a larger version.
  • Filter 14 seems to pick up vibrato singing.
  • Filter 242 picks up some kind of ringing ambience.
  • Filter 250 picks up vocal thirds, i.e. multiple singers singing the same thing, but the notes are a major third (4 semitones) apart.
  • Filter 253 picks up various types of bass drum sounds.

The genres of the tracks in these playlists are quite varied, which indicates that these features are picking up mainly low-level properties of the audio signals.

Playlists for low-level features: average activation

The next four playlists were obtained in a slightly different way: I computed the average activation of each feature across time for each track, and then found the maximum across those. This means that for these playlists, the filter in question is constantly active in the 30 seconds that were analyzed (i.e. it’s not just one ‘peak’). This is more useful for detecting harmonic patterns.

Filter 1: noise, distortion
Filter 2: pitch (A, Bb)
Filter 4: drones
Filter 28: chord (A, Am)
Closeup of filters 1, 2, 4 and 28.
Closeup of filters 1, 2, 4 and 28. Click for a larger version.
  • Filter 1 picks up noise and (guitar) distortion.
  • Filter 2 seems to pick up a specific pitch: a low Bb. It also picks up A sometimes (a semitone below) because the frequency resolution of the mel-spectrograms is not high enough to distinguish them.
  • Filter 4 picks up various low-pitched drones.
  • Filter 28 picks up the A chord. It seems to pick up both the minor and major versions, so it might just be detecting the pitches A and E (the fifth).

I thought it was very interesting that the network is learning to detect specific pitches and chords. I had previously assumed that the exact pitches or chords occurring in a song would not really affect listener preference. I have two hypotheses for why this might be happening:

  • The network is just learning to detect harmonicity, by learning various filters for different kinds of harmonics. These are then pooled together at a higher level to detect harmonicity across different pitches.
  • The network is learning that some chords and chord progressions are more common than others in certain genres of music.

I have not tried to verify either of these, but it seems like the latter would be pretty challenging for the network to pick up on, so I think the former is more likely.

Playlists for high-level features

Each layer in the network takes the feature representation from the layer below, and extracts a set of higher-level features from it. At the topmost fully-connected layer of the network, just before the output layer, the learned filters turn out to be very selective for certain subgenres. For obvious reasons, it is non-trivial to visualize what these filters pick up on at the spectrogram level. Below are six playlists with songs from the test set that maximally activate some of these high-level filters.

Filter 3: christian rock
Filter 15: choirs / a cappella + smooth jazz
Filter 26: gospel
Filter 37: Chinese pop
Filter 49: chiptune, 8-bit
Filter 1024: deep house

It is clear that each of these filters is identifying specific genres. Interestingly some filters, like #15 for example, seem to be multimodal: they activate strongly for two or more styles of music, and those styles are often completely unrelated. Presumably the output of these filters is disambiguated when viewed in combination with the activations of all other filters.

Filter 37 is interesting because it almost seems like it is identifying the Chinese language. This is not entirely impossible, since the phoneme inventory of Chinese is quite distinct from other languages. A few other seemingly language-specific filters seem to be learned: there is one that detects rap music in Spanish, for example. Another possibility is that Chinese pop music has some other characteristic that sets it apart, and the model is picking up on that instead.

I spent some time analyzing the first 50 or so filters in detail. Some other filter descriptions I came up with are: lounge, reggae, darkwave, country, metalcore, salsa, Dutch and German carnival music, children’s songs, dance, vocal trance, punk, Turkish pop, and my favorite, ‘exclusively Armin van Buuren’. Apparently he has so many tracks that he gets his own filter.

The filters learned by Alex Krizhevsky’s ImageNet network have been reused for various other computer vision tasks with great success. Based on their diversity and invariance properties, it seems that these filters learned from audio signals may also be useful for other music information retrieval tasks besides predicting latent factors.

Similarity-based playlists

Predicted latent factor vectors can be used to find songs that sound similar. Below are a couple of playlists that were generated by predicting the factor vector for a given song, and then finding other songs in the test set whose predicted factor vectors are close to it in terms of cosine distance. As a result, the first track in the playlist is always the query track itself.

The Notorious B.I.G. - Juicy (hip hop)
Cloudkicker - He would be riding on the subway... (post-rock, post-metal)
Architects - Numbers Count For Nothing (metalcore, hardcore)
Neophyte - Army of Hardcore (hardcore techno, gabber)
Fleet Foxes - Sun It Rises (indie folk)
John Coltrane - My Favorite Things (jazz)

Most of the similar tracks are pretty decent recommendations for fans of the query tracks. Of course these lists are far from perfect, but considering that they were obtained based only on the audio signals, the results are pretty decent. One example where things go wrong is the list for ‘My Favorite Things’ by John Coltrane, which features a couple of strange outliers, most notably ‘Crawfish’ by Elvis Presley. This is probably because the part of the audio signal that was analyzed (8:40 to 9:10) contains a crazy sax solo. Analyzing the whole song might give better results.

What will this be used for?

Spotify already uses a bunch of different information sources and algorithms in their recommendation pipeline, so the most obvious application of my work is simply to include it as an extra signal. However, it could also be used to filter outliers from recommendations made by other algorithms. As I mentioned earlier, collaborative filtering algorithms will tend to include intro tracks, outro tracks, cover songs and remixes in their recommendations. These could be filtered out effectively using an audio-based approach.

One of my main goals with this work is to make it possible to recommend new and unpopular music. I hope that this will help lesser known and up and coming bands, and that it will level the playing field somewhat by enabling Spotify to recommend their music to the right audience. (Promoting up and coming bands also happens to be one of the main objectives of my non-profit website

Hopefully some of this will be ready for A/B testing soon, so we can find out if these audio-based recommendations actually make a difference in practice. This is something I’m very excited about, as it’s not something you can easily do in academia.

Future work

Another type of user feedback that Spotify collects are the thumbs up and thumbs down that users can give to tracks played on radio stations. This type of information is very useful to determine which tracks are similar. Unfortunately, it is also quite noisy. I am currently trying to use this data in a ‘learning to rank’ setting. I’ve also been experimenting with various distance metric learning schemes, such as DrLIM. If anything cool comes out of that I might write another post about it.


In this post I’ve given an overview of my work so far as a machine learning intern at Spotify. I’ve explained my approach to using convnets for audio-based music recommendation and I’ve tried to provide some insight into what the networks actually learn. For more details about the approach, please refer to the NIPS 2013 paper ‘Deep content-based music recommendation’ by Aäron van den Oord and myself.

If you are interested in deep learning, feature learning and its applications to music, have a look at my research page for an overview of some other work I have done in this domain. If you’re interested in Spotify’s approach to music recommendation, check out these presentations on Slideshare and Erik Bernhardsson’s blog.

Spotify is a really cool place to work at. They are very open about their methods (and they let me write this blog post), which is not something you come across often in industry. If you are interested in recommender systems, collaborative filtering and/or music information retrieval, and you’re looking for an internship or something more permanent, don’t hesitate to get in touch with them.

If you have any questions or feedback about this post, feel free to leave a comment!

View of NYC from the Spotify deck.
View of NYC from the Spotify deck.

Yesterday, I gave talk at the Deep Learning London Meetup about my PhD research and my approach to winning the Galaxy Zoo challenge on Kaggle. The slides for my talk are available for download:

The three papers I discussed in the first part of the talk are described here, download links to the PDFs are included. A detailed description of my solution for the Galaxy Challenge is available in an earlier post on this blog. The code for all 17 models included in the winning ensemble is available on GitHub.

Last month I wrote about how you can use the cuda-convnet wrappers in pylearn2 to get up to 3x faster GPU convolutions in Theano. Since then I’ve been working on an FFT-based convolution implementation for Theano. Preliminary tests indicate that this approach is again 2-4x faster than the cuda-convnet wrappers.

I wrote the code in pure Python, using scikits.cuda and PyCUDA to do the heavy lifting. The Theano team is currently working on integrating this code into Theano. They also plan to create a proper C/CUDA implementation to guarantee the best performance.

I put everything up on GitHub, you can find the code there, or clone it and try it yourself:

FFT-based convolution

The Fourier transform of a convolution of two functions is the product of the Fourier transforms of those functions. This is the convolution theorem. This result can be used to quickly compute convolutions in the Fourier domain, since an elementwise product is much less computationally intensive than a convolution.

However, there is a price to be paid: the inputs need to be transformed using the Fast Fourier Transform (FFT), and the product of these transformed inputs needs to be transformed again using the inverse FFT. Depending on the sizes of the inputs, these costs can be pretty significant, so sometimes it is a better idea to just compute the convolution in the original domain.

I was somewhat surprised to learn that all popular implementations of convolutional neural networks (CNNs) use the latter approach, including that of Theano and cuda-convnet. The reason is that typically, convolutions in CNNs involve relatively small filters, so I think people just assumed it wasn’t worth it.

However, a paper published at ICLR 2014 recently caught my eye: Fast Training of Convolutional Networks through FFTs by Mathieu, Henaff and LeCun. They implemented the FFT-based approach in the Torch7 framework and compared its performance to Torch7’s own ‘classical’ implementation. They concluded that it is actually advantageous to use FFT-based convolutions in CNNs in many cases.

The reason is actually quite straightforward: compared to the general case, the overhead of computing the FFTs of the inputs is drastically reduced. We need to compute the convolution of each input example in a given minibatch with each filter. If there are m examples in the minibatch with k input channels, and n filters, this means we need to compute m * n * k convolutions. In the Fourier domain, this turns into m * n * k elementwise products. However, we only need to compute the FFT of each input example and each filter once. So the total number of FFTs to compute is not 2 * m * n * k, but (m + n) * k.

But that’s not everything: the output of a convolutional layer in a CNN is actually a sum of convolutions across all k input channels. Because the FFT is a linear operator, we can compute this sum in the Fourier domain, and then take the IFFT of this sum (instead of the other way around). This means we only need to compute m * n IFFTs, instead of m * n * k. It turns out that these savings can be very significant.

A CUDA/C-less Theano implementation

So this got me thinking that it should be possible to do the same thing in Theano. Theano already intelligently replaces convolution operators in computational graphs with their GPU-based counterparts in the optimization phase. If an FFT-based implementation was added, it could do the same with that version instead.

I set out to implement this, but unfortunately my knowledge of CUDA is nonexistent, and my knowledge of C can be called rusty at best. So I sought to avoid both. Enter scikits.cuda, which offers all the necessary primitives: forward and inverse FFTs, and complex products (the FFT of a real signal is complex and symmetric).

Luckily, scikits.cuda is built on top of PyCUDA, and the Theano docs have some examples of how to implement PyCUDA-based operators. Essentially I just had to glue everything together.

Implementation details

As mentioned earlier, an FFT-based convolution can be broken up into 3 parts: an FFT of the input images and the filters, a bunch of elementwise products followed by a sum across input channels, and then an IFFT of the outputs. I decided to implement each of these as a separate Theano operator. That way, the optimizer could detect if the same inputs or filters are used in multiple convolutions, and only compute them once. At the moment I’m still unsure whether this is beneficial - perhaps some additional performance could be gained by combining everything into a single, monolithic FFT-convolution operator. But that’s a discussion for another time.

The FFT and IFFT operators were the easiest. scikits.cuda exposes a nice API to perform batched FFTs. This allows for GPU-parallelism to be exploited when many FFTs of the same size have to be computed. This is precisely our use case. The API uses the cuFFT implementation internally, which is a part of CUDA.

Interestingly, the authors of the paper I mentioned earlier claim that using cuFFT is not an option because it does not allow to exploit this type of parallelism, so they made their own CUDA FFT implementation instead. However, I got pretty good results using cuFFT, so I don’t know what lead them to make this claim. Perhaps the batched FFT is a recent addition to cuFFT. The same batched approach can be used for the IFFT.

The tough part was performing the actual convolution in the Fourier domain, by computing the complex elementwise products and summing across the input channels. Theano does not have support for complex numbers, so some trickery was required to convert complex arrays into real arrays with an extra trailing dimension of size 2, to contain the real and imaginary parts of the numbers.

I tried a number of different approaches, but what worked best in the end is interpreting the operation as a dot product. A dot product is precisely that: an elementwise product with some broadcasting, followed by summing out a particular dimension. So by reshaping the Fourier-transformed inputs and filters, the multiply-and-sum operation could be translated into a set of dot products. This is great, because GPUs are really good at computing dot products quickly.

It turns out that recent versions of cuBLAS also support batched dot products, which offer the same performance advantages as batched FFTs. Since we need to perform a large number of dot products with the same shapes, this was again a perfect match for our use case. The particular function I needed to compute a batched complex-valued dot product is cublasCgemmBatched. Unfortunately this wasn’t available through scikits.cuda yet, but it wasn’t hard to add the necessary wrappers. I sent a pull request and it is now included (so make sure to get the latest version of scikits.cuda from git if you want to try this).

Proof of concept

So far I’ve only implemented the valid convolution. Using the implementation in the context of a CNN will also require support for full convolutions - but this is easy to mimic by padding the input with zeros. I have not implemented an optimization that swaps out Theano’s own convolution operator with the FFT-based version, but that is something the Theano team is currently working on.

Preliminary benchmarks show that this implementation is typically faster than cuda-convnet. The table below shows the duration of a single valid convolution computation with the given input and filter shapes, measured on a GeForce GTX 680, averaged across 10 runs, and not taking into account the warmup that the FFT-based implementation requires (the first run will be a bit slower because the FFT plans need to be created).

Following Theano conventions, the input shape is given as (batch size, number of input channels, width, height) and the filter shape is given as (number of filters, number of input channels, width, height). Durations are given for Theano’s own conv2d implementation, the cuda-convnet wrappers from pylearn2, and the FFT-based implementation. The speedup of the FFT-based implementation over the cuda-convnet wrappers is also given.

input shape filter shape Theano’s own cuda-convnet FFT-based speedup
(64, 3, 96, 96) (128, 3, 16, 16) 388.9 ms 156.9 ms 117.3 ms 1.34x
(64, 128, 32, 32) (64, 128, 8, 8) 233.9 ms 87.4 ms 27.1 ms 3.23x
(128, 32, 54, 54) (64, 32, 6, 6) 457.5 ms 107.6 ms 52.2 ms 2.06x
(128, 128, 16, 16) (128, 128, 8, 8) 133.4 ms 43.5 ms 18.6 ms 2.34x
(128, 1024, 32, 32) (128, 1024, 4, 4) 6246.2 ms 1283.5 ms 357.8 ms 3.59x

In all cases we get a nice speedup. This approach seems to be the most beneficial when the number of input channels is large - this makes sense, as this is the dimension that is summed over in the batched dot product. But even when this number is small (e.g. 3) it’s still faster.

Try it out

As mentioned in the introduction, you can grab the code for this at:

All the relevant code is in the file The file was mainly used for experimentation, and contains some alternative implementations of the multiply-and-sum step.

Note that the latest revision of scikits.cuda is required, to ensure that the cublasCgemmBatched function is available. You’ll also need a working installation of PyCUDA, as this is a dependency of scikits.cuda. And of course, you’ll need Theano and a working CUDA installation.

If you’re patient, you can also wait until the code is available in Theano. Chances are you’ll be able to use it without modifying your existing code, as they are also building an optimization that will replace Theano’s own convolutions with the FFT-based implementation. And if you’re very patient, you can wait until they build the CUDA/C version, which will eliminate the scikits.cuda and PyCUDA dependencies, and hopefully it will be a bit faster as well due to the reduced overhead.

The code to compute the numbers in the table above is in the file This script also checks whether the output of all three implementations is the same (up to a given tolerance). More numbers for different input/filter shapes and different GPUs are welcome, so if you run this script on your own machine(s), feel free to send me the results.

Feedback is welcome, and if you’d like to help with integrating this into Theano, join the conversation at the theano-users group!

Galaxy Zoo Challenge: code published

Some two weeks ago I posted about my solution for the Galaxy Zoo challenge on Kaggle. Today I’ve published the code with documentation on GitHub.

Get it here:

git clone git://

Have a look at the README file for instructions on how to generate the winning solution, or check out doc/documentation.pdf for more information.

The code is available under a BSD 3-clause licence. If you’ve found it useful, dropping me a line to let me know how you used it is appreciated. Have fun with it!

My solution for the Galaxy Zoo challenge

The Galaxy Zoo challenge on Kaggle has just finished. The goal of the competition was to predict how Galaxy Zoo users (zooites) would classify images of galaxies from the Sloan Digital Sky Survey. I finished in 1st place and in this post I’m going to explain how my solution works.


The problem

Galaxy Zoo is a crowdsourcing project, where users are asked to describe the morphology of galaxies based on images. They are asked questions such as “How rounded is the galaxy” and “Does it have a central bulge”, and the users’ answers determine which question will be asked next. The questions form a decision tree which is shown in the figure below, taken from Willett et al. 2013.

The Galaxy Zoo decision tree, taken from Willett et al. 2013.

When many users have classified the same image, their answers can be aggregated into a set of probabilities for each answer. Often, not all users will agree on all of their answers, so it’s useful to quantify this uncertainty.

The goal of the Galaxy Zoo challenge is to predict these probabilities from the galaxy images that are shown to the users. In other words, build a model of how “the crowd” perceive and classify these images.

This means that we’re looking at a regression problem, not a classification problem: we don’t have to determine which classes the galaxies belong to, but rather the fraction of people who would classify them as such.

My solution: convnets

I suppose this won’t surprise anyone: my solution is based around convolutional neural networks (convnets). I believe they’re an excellent match for this problem: it’s image data, but it is different enough from typical image data (i.e. “natural” images such as those used in object recognition, scene parsing, etc.) for traditional features from computer vision to be suboptimal. Learning the features just seems to make sense.

Transfer learning by pre-training a deep neural network on another dataset (say, ImageNet), chopping off the top layer and then training a new classifier, a popular approach for the recently finished Dogs vs. Cats competition, is not really viable either. There were no requests to use external data in the competition forums (a requirement to be allowed to use it), so I guess nobody tried this approach.

During the contest, I frequently referred to Krizhevsky et al.’s seminal 2012 paper on ImageNet classification for guidance. Asking myself “What would Krizhevsky do?” usually resulted in improved performance.


As Geoffrey Hinton has been known to say, if you’re not overfitting, your network isn’t big enough. My main objective during the competition was avoiding overfitting. My models were significantly overfitting throughout the entire competition, and most of the progress I attained came from finding new ways to mitigate that problem.

I tackled this problem with three orthogonal approaches:

  • data augmentation
  • dropout and weight norm constraints
  • modifying the network architecture to increase parameter sharing

The best model I found has about 42 million parameters. It overfits significantly, but it’s still the best despite that. There seems to be a lot of room for improvement there.

As is customary in Kaggle competitions, I also improved my score quite a bit by averaging the predictions of a number of different models. Please refer to the “Model averaging” section below for more details.

Software and hardware

I used Python, NumPy and Theano to implement my solution. I also used the Theano wrappers for the cuda-convnet convolution implementation that are part of pylearn2. They provided me with a speed boost of almost 3x over Theano’s own implementation. I wrote a guide on how to use them, because their documentation is limited.

I used scikit-image for preprocessing and augmentation. I also used sextractor and pysex to extract some parameters of the galaxies from the images.

The networks were trained on workstations with a hexacore CPU, 32GB RAM and two NVIDIA GeForce GTX 680 GPUs each.

Preprocessing and data augmentation

Cropping and downsampling

The data consisted of 424x424 colour JPEG images, along with 37 weighted probabilities that have to be predicted for each image (for details on the weighting scheme, please refer to this page).

For almost all of the images, the interesting part was in the center. The void of space around the galaxies was not very discriminative, so I cropped all images to 207x207. I then downsampled them 3x to 69x69, to keep the input size of the network manageable.

Exploiting spatial invariances

Images of galaxies are rotation invariant: there is no up or down in space. They are also scale invariant and translation invariant to a limited extent. All of these invariances could be exploited to do data augmentation: creating new training data by perturbing the existing data points.

Each training example was perturbed before presenting it to the network by randomly scaling it, rotating it, translating it and optionally flipping it. I used the following parameter ranges:

  • rotation: random with angle between 0° and 360° (uniform)
  • translation: random with shift between -4 and 4 pixels (relative to the original image size of 424x424) in the x and y direction (uniform)
  • zoom: random with scale factor between 1/1.3 and 1.3 (log-uniform)
  • flip: yes or no (bernoulli)

Because both the initial downsampling to 69x69 and the random perturbation are affine transforms, they could be combined into one affine transformation step (I used scikit-image for this). This sped up things significantly and reduced information loss.

Colour perturbation

After this, the colour of the images was changed as described in Krizhevsky et al. 2012, with two differences: the first component had a much larger eigenvalue than the other two, so only this one was used, and the standard deviation for the scale factor alpha was set to 0.5.

“Realtime” augmentation

Combining downsampling and perturbation into a single affine transform made it possible to do data augmentation in realtime, i.e. during training. This significantly reduced overfitting because the network would never see the exact same image twice. While the network was being trained on a chunk of data on the GPU, the next chunk would be generated on the CPU in multiple processes, to ensure that all the available cores were used.

Centering and rescaling

I experimented with centering and rescaling the galaxy images based on parameters extracted with sextractor. Although this didn’t improve performance, including a few models that used it in the final ensemble helped to increase variance (see “Model averaging” for more information).

I extracted the center of the galaxies, as well as the Petrosian radius. A number of different radii can be extracted, but the Petrosian radius seemed to give the best size estimate. I then centered each image by shifting the estimated center pixel to (212, 212), and rescaled it so that its Petrosian radius would be equal to 160 pixels. The scale factor was limited to the range (1/1.5, 1.5), because there were some outliers.

This rescaling and centering could also be collapsed into the affine transform doing downsampling and perturbation, so it did not slow things down at all.

Input = raw pixels

With these pre-processing and augmentation steps, the network input still consisted of raw pixels. No features were extracted apart from those learned by the network itself.

Network architecture

Exploiting rotation invariance to increase parameter sharing

I increased parameter sharing in the network by cutting the galaxy images into multiple parts that could be treated in the same fashion, i.e. processed by the same convolutional architecture. For this I exploited the rotation invariance of the images.

As mentioned before, the images were cropped to 207x207 and downsampled by a factor of 3. This was done with two different orientations: a regular crop, as well as one that is rotated 45°. Both of these crops were also flipped horizontally, resulting in four 69x69 “views” of the image. This is visualised below.

Four different views were extracted from each image: a regular view (red), a 45° rotated view (blue), and mirrored versions of both.

Each of the four views was again split into four partially overlapping “parts” of size 45x45. Each part was rotated so that they are all aligned, with the galaxy in the bottom right corner. This is visualised below. In total, 16 parts were extracted from the original image.

Each view was then split into four partially overlapping parts. Each part was rotated so that they are all aligned, with the galaxy in the bottom right corner. In total, 16 parts were extracted from the original image.

This results in 16 smaller 45x45 images which appear very similar. They can be expected to have the same topological structure due to rotation invariance, so they can be processed by the same convolutional architecture, which results in a 16x increase in parameter sharing, and thus less overfitting. At the top of the network, the features extracted from these 16 parts are concatenated and connected to one or more dense layers, so the information can be aggregated.

A nice side-effect of this approach is that the effective minibatch size for the convolutional part of the network increases 16-fold because the 16 parts are stacked on top of each other, which makes it easier to exploit GPU parallelism.

Due to the overlap of the parts, a lot of information is available about the center of the galaxy, because it is processed by the convnet in 16 different orientations. This is useful because a few important properties of the galaxies are expected to be in the center of the image (the presence of a bar or a bulge, for example). Reducing this overlap typically resulted in reduced performance. I chose not to make the parts fully overlap, because it would slow down training too much.

Incorporating output constraints

As described on this page, the 37 outputs to be predicted are weighted probabilities, adhering to a number of constraints. Incorporating these constraints into the model turned out to be quite useful.

In essence, the answers to each question should form a categorical distribution. Additionally, they are scaled by the probability of the question being asked, i.e. the total probability of answers given that would lead to this question being asked.

My initial reflex was to use a softmax output for each question, and then apply the scaling factors. This didn’t make much of a difference. I believe this is because the softmax function has difficulty predicting hard zeros and ones, of which there were quite a few in the training data (its input would have to be very large in magnitude).

If cross-entropy is the error metric, this is not a big issue, but for this competition, the metric by which submissions were judged was the root mean squared error (RMSE). As a result, being able to predict very low and very high probabilities was quite useful.

In the end I normalised the distribution for each question by adding a rectification nonlinearity in the top layer instead of the softmax functions, and then just using divisive normalisation. For example, if the raw, linear outputs of the top layer of the network for question one were z1, z2, z3, then the actual output for question one was given by max(z1, 0) / (max(z1, 0) + max(z2, 0) + max(z3, 0) + epsilon). The epsilon is a very small constant that prevented division by zero errors, I set it to 1e-12. This approach allowed the network to predict hard zeros more easily.

This is really where Theano shines: I could incorporate these constraints into the model simply by writing out what they were - no need to manually compute all the changed gradients. A big time saver! If I had to compute the gradients manually, I probably wouldn’t even have bothered to try incorporating the constraints in the first place.

Architecture of the best model

The best model I found is shown below in the form of a Krizhevsky-style diagram. All other models included in the final ensemble I submitted are slight variations of this model.

Krizhevsky-style diagram of the architecture of the best performing network.

The input is presented to the model in the form of RGB coloured 45x45 image parts.

The model has 7 layers: 4 convolutional layers and 3 dense layers. All convolutional layers include a ReLU nonlinearity (i.e. f(x) = max(x, 0)). The first, second and fourth convolutional layers are followed by 2x2 max-pooling. The sizes of the layers, as well as the sizes of the filters, are indicated in the figure.

As mentioned before, the convolutional part of the network is applied to 16 different parts of the input image. The extracted features for all these parts are then aggregated and connected to the dense part of the network.

The dense part consists of two maxout layers with 2048 units (Goodfellow et al. 2013), both of which take the maximum over pairs of linear filters (so 4096 linear filters in total). Using maxout here instead of regular dense layers with ReLUs helped to reduce overfitting a lot, compared to dense layers with 4096 linear filters. Using maxout in the convolutional part of the network as well proved too computationally intensive.

Training this model took 67 hours.


Variants of the best model were included in the final ensemble I submitted, to increase variance (see “Model averaging”). They include:

  • a network with two dense layers instead of three (just one maxout layer)
  • a network with one of the dense layers reduced in size and applied individually to each part (resulting in 16-way parameter sharing for this layer as well)
  • a network with a different filter size configuration: 8/4/3/3 instead of 6/5/3/3 (from bottom to top)
  • a network with centered and rescaled input images
  • a network with a ReLU dense layer instead of maxout
  • a network with 192 filters instead of 128 for the topmost convolutional layer
  • a network with 256 filters instead of 128 for the topmost convolutional layer
  • a network with norm constraint regularisation applied to the two maxout layers (as in Hinton et al. 2012)
  • combinations of the above variations



For validation purposes, I split the training set in two parts. I used the first 90% for training, and the remainder for validation. I noticed quite early on that the estimates on my validation set matched the public leaderboard pretty well. This implied that submitting frequently was unnecessary - but nevertheless I couldn’t resist :)

Near the end of the competition I tried retraining a model on the entire training set, including the validation data I split off, but I noticed no increase in performance on the public leaderboard, so I left it at that. The separate validation set came in handy for model averaging anyway.

Training algorithm

I trained the networks with stochastic gradient descent (SGD) and Nesterov momentum (fixed at 0.9). I used a minibatch size of 16 examples. This meant that the effective minibatch size for the convolutional part was 256 (see above). This worked well because the cuda-convnet convolution implementation is optimised for minibatch sizes that are multiples of 128.

I trained the networks for about 1.5 million gradient steps. I used a learning rate schedule with two discrete decreases. Initially it was set to 0.04. It was decreased tenfold to 0.004 after about 1.1 million steps, and again to 0.0004 after about 1.4 million steps.

For the first ~600 gradient steps, the divisive normalisation in the output layer was disabled. This was necessary to ensure convergence (otherwise it would get stuck at the start sometimes).


Some fiddling with the parameter initialisation was required to get the network to train properly. Most of the layer weights were initialised from a Gaussian distribution with mean zero and a standard deviation of 0.01, with biases initialised to 0.1. For the topmost convolutional layer, I increased the standard deviation to 0.1. For the dense layers, I reduced it to 0.001 and the biases were initialised to 0.01. These modifications were necessary presumably because these layers are much smaller resp. bigger than the others.


Dropout was used in all three dense layers, with a dropout probability of 0.5. This was absolutely essential to be able to train the network at all.

Near the very end of the competition I also experimented with norm constraint regularisation for the maxout layers. I chose the maximal norm for each layer based on a histogram of the norms of a network trained without norm constraint regularisation (I chose it so the tail of the histogram would be chopped off). I’m not entirely sure if this helped or not, since I was only able to do two runs with this setup.

Model averaging

Averaging across transformed images

For each individual model, I computed predictions for 60 affine transformations of the test set images: a combination of 10 rotations, spaced by 36°, 3 rescalings (with scale factors 1/1.2, 1 and 1.2) and flipping / no flipping. These were uniformly averaged. Even though the model architecture already incorporated a lot of invariances, this still helped quite a bit.

Computing these averaged test set predictions for a single model took just over 4 hours.

Averaging across architectures

The averaged predictions for each model were then uniformly blended again, across a number of different models (variants of the model described under “Architecture of the best model”). I also experimented with a weighted blend, optimised on the validation set I split off, but this turned out not to make a significant difference. However, I did use the learned weights to identify sets of predictions that were not contributing at all, and I removed those from the uniform blend as well.

My final submission was a blend of predictions from 17 different models, each of which were themselves blended across 60 transformations of the input. So in the end, I blended 1020 predictions for each test set image.

For comparison: my best single model achieved a score of 0.07671 on the public leaderboard. After averaging, I achieved a final score of 0.7467. This resulted in a score of 0.07492 on the private leaderboard.


Below are a bunch of things that I tried but ended up not using - either because they didn’t help my score, or because they slowed down training too much.

  • Adding Gaussian noise to the input images during training to reduce overfitting. This didn’t help.
  • Extracting crops from the input images at different scales, and training a multiscale convnet on them. It turned out that only the part of the network for the most detailed scale was actually learning anything. The other parts received no gradient and weren’t really learning.
  • Overlapping pooling. This seemed to help a little bit, but it slowed things down too much.
  • Downsampling the input images less (1.5x instead of 3x) and using a strided convolution in the first layer (with stride 2). This did not improve results, and dramatically increased memory usage.
  • Adding shearing to the data augmentation step. This didn’t help, but it didn’t hurt performance either. I assumed that it would hurt performance because question 7 pertains to the ellipticity of the galaxy (shearing would of course change this), but this didn’t seem to be the case.

Near the end of the competition I also toyed with a polar coordinate representation of the images. I suppose this could work well because rotations turn into translations in polar space, so the convnet’s inherent translation invariance would amount to rotation invariance in the original input space. Unfortunately I didn’t have enough time left to properly explore this approach, so I decided to focus on my initial approach instead.

I would also have liked to find a way to incorporate the test set images somehow (i.e. a transduction setup). Unsupervised pre-training seemed pointless, because it tends not to be beneficial when rectified linear units and dropout are used, except maybe when labeled training data is very scarce. I really like the pseudo-label approach of Dong-Hyun Lee (2013), but it could not be applied here because it only makes sense for classification problems, not regression problems. If anyone has ideas for this, I’m still interested!


This was a really cool competition, and even though I had some prior experience training convnets, I learnt a lot of new things about them.

If this problem interests you, be sure to check out the competition forum. Many of the participants will be posting overviews of their approaches in the coming days.

I would like to thank the organisers of the competition, as well as the authors of Theano, cuda-convnet and pylearn2 for providing me with the necessary tools.

I will clean up my code and I’ll put it on GitHub soon. If you have any questions or feedback about this post, feel free to leave a comment.

Update (April 6th):