Autoencoders
What are autoencoders?
Autoencoders are neural networks for unsupervised learning. Eclipse Deeplearning4j supports certain autoencoder layers such as variational autoencoders.
Where’s Restricted Boltzmann Machine?
RBMs are no longer supported as of version 0.9.x. They are no longer best-in-class for most machine learning problems.
Supported layers
AutoEncoder
Autoencoder layer. Adds noise to input and learn a reconstruction function.
corruptionLevel
Level of corruption - 0.0 (none) to 1.0 (all values corrupted)
sparsity
Autoencoder sparity parameter
param sparsity Sparsity
VariationalAutoencoder
Variational Autoencoder layer
See: Kingma & Welling, 2013: Auto-Encoding Variational Bayes - https://arxiv.org/abs/1312.6114
This implementation allows multiple encoder and decoder layers, the number and sizes of which can be set independently.
A note on scores during pretraining: This implementation minimizes the negative of the variational lower bound objective as described in Kingma & Welling; the mathematics in that paper is based on maximization of the variational lower bound instead. Thus, scores reported during pretraining in DL4J are the negative of the variational lower bound equation in the paper. The backpropagation and learning procedure is otherwise as described there.
encoderLayerSizes
Size of the encoder layers, in units. Each encoder layer is functionally equivalent to a {- link org.deeplearning4j.nn.conf.layers.DenseLayer}. Typically the number and size of the decoder layers (set via {- link #decoderLayerSizes(int…)} is similar to the encoder layers.
setEncoderLayerSizes
Size of the encoder layers, in units. Each encoder layer is functionally equivalent to a {- link org.deeplearning4j.nn.conf.layers.DenseLayer}. Typically the number and size of the decoder layers (set via {- link #decoderLayerSizes(int…)} is similar to the encoder layers.
param encoderLayerSizes Size of each encoder layer in the variational autoencoder
decoderLayerSizes
Size of the decoder layers, in units. Each decoder layer is functionally equivalent to a {- link org.deeplearning4j.nn.conf.layers.DenseLayer}. Typically the number and size of the decoder layers is similar to the encoder layers (set via {- link #encoderLayerSizes(int…)}.
param decoderLayerSizes Size of each deccoder layer in the variational autoencoder
setDecoderLayerSizes
Size of the decoder layers, in units. Each decoder layer is functionally equivalent to a {- link org.deeplearning4j.nn.conf.layers.DenseLayer}. Typically the number and size of the decoder layers is similar to the encoder layers (set via {- link #encoderLayerSizes(int…)}.
param decoderLayerSizes Size of each deccoder layer in the variational autoencoder
reconstructionDistribution
The reconstruction distribution for the data given the hidden state - i.e., P(data|Z). This should be selected carefully based on the type of data being modelled. For example: - {- link GaussianReconstructionDistribution} + {identity or tanh} for real-valued (Gaussian) data - {- link BernoulliReconstructionDistribution} + sigmoid for binary-valued (0 or 1) data
param distribution Reconstruction distribution
lossFunction
Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution. Note that this is NOT following the standard VAE design (as per Kingma & Welling), which assumes a probabilistic output - i.e., some p(x|z). It is however a valid network configuration, allowing for optimization of more traditional objectives such as mean squared error. Note: clearly, setting the loss function here will override any previously set recontruction distribution
param outputActivationFn Activation function for the output/reconstruction
param lossFunction Loss function to use
lossFunction
Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution. Note that this is NOT following the standard VAE design (as per Kingma & Welling), which assumes a probabilistic output - i.e., some p(x|z). It is however a valid network configuration, allowing for optimization of more traditional objectives such as mean squared error. Note: clearly, setting the loss function here will override any previously set recontruction distribution
param outputActivationFn Activation function for the output/reconstruction
param lossFunction Loss function to use
lossFunction
Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution. Note that this is NOT following the standard VAE design (as per Kingma & Welling), which assumes a probabilistic output - i.e., some p(x|z). It is however a valid network configuration, allowing for optimization of more traditional objectives such as mean squared error. Note: clearly, setting the loss function here will override any previously set recontruction distribution
param outputActivationFn Activation function for the output/reconstruction
param lossFunction Loss function to use
pzxActivationFn
Activation function for the input to P(z|data). Care should be taken with this, as some activation functions (relu, etc) are not suitable due to being bounded in range [0,infinity).
param activationFunction Activation function for p(z| x)
pzxActivationFunction
Activation function for the input to P(z|data). Care should be taken with this, as some activation functions (relu, etc) are not suitable due to being bounded in range [0,infinity).
param activation Activation function for p(z | x)
nOut
Set the size of the VAE state Z. This is the output size during standard forward pass, and the size of the distribution P(Z|data) during pretraining.
param nOut Size of P(Z | data) and output size
numSamples
Set the number of samples per data point (from VAE state Z) used when doing pretraining. Default value: 1.
This is parameter L from Kingma and Welling: “In our experiments we found that the number of samples L per datapoint can be set to 1 as long as the minibatch size M was large enough, e.g. M = 100.”
param numSamples Number of samples per data point for pretraining
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