Friday, October 14, 2016

Applying Auto-encoders to MNIST

Applying Auto-encoders to MNIST

This is a companion article to my new book, Practical Machine Learning with H2O, published by O’Reilly. Beyond the sheer, unadulterated pleasure you will get from reading it, I’m also recommending it for readers of this article, because I’m only going to lightly introduce topics such as H2O, MNIST, and even auto-encoders, that are covered in much more depth in the book.

That Light Introduction

H2O is a powerful, scalable and fast machine-learning server/framework, with APIs in R and Python (as well as Coffeescript, Scala via its Spark interface, and others). It has relatively few machine learning algorithms, but they are generally the ones you would have settled on using anyway, and each has been optimized to scale across clusters and big data, and each has many parameters for tuning.
MNIST is a machine learning problem to recognize which of the digits 0 to 9 a set of 784 pixels represents. There are 60,000 training samples, and 10,000 test samples. To avoid inadvertently over-fitting to the test samples, I split the 60K into 50K training data and 10K validation data.
Auto-encoders are the unsupervised version of deep-learning (neural nets, if you prefer). The Wikipedia article is a good introduction to the idea. By setting the input_dropout_ratio parameter H2O supports the “Denoising autoencoder” variation, and withhidden_dropout_ratios H2O supports the “Sparse autoencoder” variation.

The Aim

More layers in a supervised deep learning neural network can often give better results on complex problems. But the more layers you have, the harder it can be to train. Auto-encoders to the rescue! You run an auto-encoder on the raw inputs, and it will self-organize them - extract some information from them. You then take the middle hidden layer from the auto-encoder, and use that as the inputs to your supervised learning algorithm. Or possibly you do it again, with another auto-encoder, to extract (theoretically) an even higher-level abstraction.
I decided to try this on the MNIST data.
My initial approach was to treat it as a data compression problem: to see how few hidden neurons, in a single layer, I could get a perfect score with. I.e. this autoencoder had 784 input neurons, N hidden neurons, and 784 output neurons. Just one hidden layer, so to request, say, a 784x200x784 layout, in H2O I just do hidden=200, and the input layer is implicit from the data, and the output it implict because I specify autoencoder=TRUE. Unfortunately, even with N=784, I couldn’t get an MSE of 0.0.
(You should be able to see how there is one trivial way for such a network to get the perfect score: for each neuron in the middle layer, exactly one incoming weight should be 1.0 and all the others should be 0.0, and then the same for the outgoing weights. However, also appreciate how hard it would be for training to discover this if all 784 weights leading in and all 784 weights leading out of each neuron started off life as a random number.)

Getting Practical

So, under time pressure, I took an “educated guess” approach, and also an “ensemble” approach. I made three autoencoders (one of them being two-step, so four models in total), and used them together. The code to make them, and their outputs, is wrapped up in a couple of R functions, which I’ll show in a moment, but first a look at the parameters of the four models:
AE200: This uses a single layer of 200 hidden neurons. I set input_dropout_ratio = 0.3, which means as each training sample was used for training it would be setting a random 30% of the pixels to 0. This should make it more robust, less likely to over-fit. I also use L2 regularization, set to 1e-4 (which is fairly high).
AE32: This uses just 32 hidden neurons, so is going to be less perfect representation than AE200. To compensate for that, I use a lower input_dropout_ratio = 0.1, and also lowered L2 regularization to 1e-5.
AE768: Not used directly. One hidden neuron per input pixel (almost), means it is more “rephrasing” rather than “compressing”. I used the same input_dropout_ratio = 0.3 and l2 = 1e-4 settings as AE200. (Among the 784 pixels columns there are a few around the edge that are exactly zero in all training data, so provide no information, and can be thrown away; that is where the 768 came from.)
AE128: This was built from the output of AE768. No input dropout, and just a bit of L2 regularization (1e-5).
L2 regularization penalizes large weights (e.g. see It helps make sure all pixels get considered, rather than allowing the algorithm to over-fit to one particular pixel.
All four models used the tanh activation function, and were given 20 epochs.

The Model Generation Code

The following listing shows the R code, for the above model descriptions in H2O, wrapped up in a function that takes two parameters:
  • data is the H2O frame to use for training data
  • x is which columns of that frame to use
create_MNIST_autoencoders <- function(data, x){
m_AE200 <- h2o.deeplearning(
  x, training_frame = data,
  hidden = c(200),
  model_id = "AE200",
  input_dropout_ratio = 0.3,  #Quite high
  l2 = 1e-4,  #Quite high
  activation = "Tanh",
  export_weights_and_biases = T,
  ignore_const_cols = F,
  train_samples_per_iteration = 0,
  epochs = 20

m_AE32 <- h2o.deeplearning(
  x, training_frame = data,
  hidden = c(32),
  model_id = "AE32",
  autoencoder = T,
  input_dropout_ratio = 0.1,  #Fairly low
  l2 = 1e-5,  #Fairly low
  activation = "Tanh",
  export_weights_and_biases = T,
  ignore_const_cols = F,
  train_samples_per_iteration = 0,
  epochs = 20

m_AE768 <- h2o.deeplearning(
  x, training_frame = data,
  hidden = c(768),
  model_id = "AE768",
  autoencoder = T,
  input_dropout_ratio = 0.3,  #Quite high
  l2 = 1e-4,  #Quite high
  activation = "Tanh",
  export_weights_and_biases = T,
  ignore_const_cols = F,
  train_samples_per_iteration = 0,
  epochs = 20

f_AE768 = h2o.deepfeatures(m_AE768, data)

m_AE128 <- h2o.deeplearning(
  1:768, training_frame=f_AE768,
  hidden = c(128),
  model_id = "AE128",
  autoencoder = T,
  input_dropout_ratio = 0,  #No dropout
  l2 = 1e-5,   #Just a bit of L2
  activation = "Tanh",
  #export_weights_and_biases = T,
  #ignore_const_cols = F,
  train_samples_per_iteration = 0,
  epochs = 20

return(list(m_AE200, m_AE32, m_AE768, m_AE128))
Feeding the output of one auto-encoder (AE768 in this case) into another (AE128) is done with h2o.deepfeatures().
These two lines are just for troubleshooting/visualization:
export_weights_and_biases = T,
ignore_const_cols = F,
And, in a sense, this one is too:
train_samples_per_iteration = 0
This says I want it to always score the model’s MSE at the end of every epoch. I did this so I could see the shape of the score history chart, and so get a feel for if 20 epochs was enough. Normally touching train_samples_per_iteration counts as micro-management, because the default is to intelligently choose when to score based on some targets for time spent training vs. scoring, and some communication overhead targets. (See the explanation in chapter 8 of the book, if you crave more detail.)

Using The Models To Make Pixels

The second helper function is shown next. It returns (a handle to) an H20 frame that has 200 + 32 + 128 columns from the autoencoders, plus any additional columns you specify in columns (which must include at least the answer column).
generate_from_MNIST_autoencoders <- function(models, data, columns){
  stopifnot(length(models) == 4)
  names(models) = c("AE200", "AE32", "AE768", "AE128")

  f_AE200 <- h2o.deepfeatures(models[["AE200"]], data)
  f_AE32 <- h2o.deepfeatures(models[["AE32"]], data)
  f_AE768 <- h2o.deepfeatures(models[["AE768"]], data)
  f_AE128 <- h2o.deepfeatures(models[["AE128"]], f_AE768)

  h2o.cbind(f_AE200, f_AE32, f_AE128, data[,columns] )
Notice how, if you don’t include the original 768 pixel columns in columns that the auto-encoded features will effectively replace the raw pixel data. (This was my intention, but you don’t have to do that.)

Usage Example

I’ll assume you have 785 columns, where the pixel data is in columns 1 to 784, and the answer, 0 to 9, is in column 785. If you have the book, you will be familiar with this convention:
train <- #...50K of training data
valid <- #...10K of validation data
test <- #...10K of test data
x <- 1:784
y <- 785
To use them I then write:
models <- create_MNIST_autoencoders(train, x)
train_ae <- generate_from_MNIST_autoencoders(models, train, y)
valid_ae <- generate_from_MNIST_autoencoders(models, valid, y)
test_ae <- generate_from_MNIST_autoencoders(models, test, y)
m <- h2o.deeplearning(1:360, 361, train_ae, validation_frame = valid_ae)
Here I train a deep learning model with all defaults, but it could be a more complex deep learning model, or it could be a random forest, GBM or any other algorithm supported by H2O.
When using it for predictions, remember to use test_ae, not test (and similarly, in production, any future data has to be put through all four auto-encoder models). So following on from the above, you could evaluate it with:
h2o.performance(m, test_ae)

Usage: extended data

If you are lucky enough to have read the book, you will know I added 113 columns of “extended information” to the MNIST data. Though I got rid of the pixels, I chose to keep the extended columns alongside the auto-encoder generated data.
This is how the above code looks if you are using the extended MNIST data:
x <- 114:897
columns <- c(1:113,898)
models <- create_MNIST_autoencoders(train, x)
train_ae <- generate_from_MNIST_autoencoders(models, train, columns)
valid_ae <- generate_from_MNIST_autoencoders(models, valid, columns)
test_ae <- generate_from_MNIST_autoencoders(models, test, columns)
m <- h2o.deeplearning(1:473, 474, train_ae, validation_frame = valid_ae)


Informally, I can tell you that a deep learning model built on 473 auto-encoded/extended columns was significantly better than one built on 897 pixel/extended columns. However, I also increased the amount of training data at the same time (see ), so I cannot tell you the relative contributions of those two changes.
But, I was pleased with the results. And the “educated guess” approach of choosing three distinct auto-encoder models and combining their outputs also seemed to work well. It might be that just one of the auto-encoder models is carrying all the useful information, and the others could be dropped? That is a good experiment to do (let us know if you do it!), but my hunch is that the “ensemble” approach is what allows the educated guess approach to work.

1 comment:

Fadl AlAkwaa said...

Thank you for this nice explanation.
I am wondering if you add some graphical plots to explain the differences between these models.