Reading recustructed vector from autoencoder in DL4J

Question

My goal is to have an autoencoding network where I can train the identity function and then do forward passes yielding a reconstruction of the input.

For this, I'm trying to use VariationalAutoencoder, e.g. something like:

MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(77147718)
                .trainingWorkspaceMode(WorkspaceMode.NONE)
                .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
                .gradientNormalizationThreshold(1.0)
                .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
                .list()
                .layer(0, new VariationalAutoencoder.Builder()
                        .activation(Activation.LEAKYRELU)
                        .nIn(100).nOut(15)
                        .encoderLayerSizes(120, 60, 30)
                        .decoderLayerSizes(30, 60, 120)
                        .pzxActivationFunction(Activation.IDENTITY)
                        .reconstructionDistribution(new BernoulliReconstructionDistribution(Activation.SIGMOID.getActivationFunction()))
                        .build())
                .pretrain(true).backprop(false)
                .build();

However, VariationalAutoencoder seems to be designed for training (and providing) mappings from an input to an encoded version, i.e. a vector of size 100 to a vector of size 15 in above example configuration.

However, I'm not particularly interested in the encoded version, but would like to train a mapping of a 100-vector to itself. Then, I'd like to run a other 100-vectors through it and get back their reconstructed versions.

But even when looking at the API of of the VariationalAutoencoder (or AutoEncoder too), I can't figure out how to do this. Or are those layers not designed for this kind of "end-to-end usage" and I would have to manually construct an autoencoding network?

Answer 1

You can see how to use the VAE layer to extract averaged reconstructions from the variational example.

There's two methods for getting the reconstruction from a variational layer. The standard is generateAtMeanGivenZ Which will draw samples from the layer and give you the average. If you want raw samples you can use generateRandomGivenZ. See the javadoc page for all the other methods.