org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution Maven / Gradle / Ivy
package org.deeplearning4j.nn.conf.layers.variational;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.random.impl.GaussianDistribution;
import org.nd4j.shade.jackson.annotation.JsonSubTypes;
import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
/**
* The ReconstructionDistribution is used with variational autoencoders {@link VariationalAutoencoder}
* to specify the form of the distribution p(data|x). For example, real-valued data could be modelled
* by a {@link GaussianReconstructionDistribution}, whereas binary data could be modelled by a {@link BernoulliReconstructionDistribution}.
*
* To model multiple types of data in the one data vector, use {@link CompositeReconstructionDistribution}.
*
* @author Alex Black
*/
@JsonTypeInfo(use = JsonTypeInfo.Id.NAME, include = JsonTypeInfo.As.WRAPPER_OBJECT)
@JsonSubTypes(value = {
@JsonSubTypes.Type(value = GaussianReconstructionDistribution.class, name = "Gaussian"),
@JsonSubTypes.Type(value = BernoulliReconstructionDistribution.class, name = "Bernoulli"),
@JsonSubTypes.Type(value = ExponentialReconstructionDistribution.class, name = "Exponential"),
@JsonSubTypes.Type(value = CompositeReconstructionDistribution.class, name = "Composite"),
@JsonSubTypes.Type(value = LossFunctionWrapper.class, name = "LossWrapper")
})
public interface ReconstructionDistribution extends Serializable {
/**
* Get the number of distribution parameters for the given input data size.
* For example, a Gaussian distribution has 2 parameters value (mean and log(variance)) for each data value,
* whereas a Bernoulli distribution has only 1 parameter value (probability) for each data value.
*
* @param dataSize Size of the data. i.e., nIn value
* @return Number of distribution parameters for the given reconstruction distribution
*/
int distributionInputSize(int dataSize);
/**
* Calculate the negative log probability (summed or averaged over each example in the minibatch)
*
* @param x Data to be modelled (reconstructions)
* @param preOutDistributionParams Distribution parameters used by this reconstruction distribution (for example,
* mean and log variance values for Gaussian)
* @param average Whether the log probability should be averaged over the minibatch, or simply summed.
* @return Average or sum of negative log probability of the reconstruction given the distribution parameters
*/
double negLogProbability(INDArray x, INDArray preOutDistributionParams, boolean average);
/**
* Calculate the negative log probability for each example individually
*
* @param x Data to be modelled (reconstructions)
* @param preOutDistributionParams Distribution parameters used by this reconstruction distribution (for example,
* mean and log variance values for Gaussian) - before applying activation function
* @return Negative log probability of the reconstruction given the distribution parameters, for each example individually.
* Column vector, shape [numExamples, 1]
*/
INDArray exampleNegLogProbability(INDArray x, INDArray preOutDistributionParams);
/**
* Calculate the gradient of the negative log probability with respect to the preOutDistributionParams
*
* @param x Data
* @param preOutDistributionParams Distribution parameters used by this reconstruction distribution (for example,
* mean and log variance values for Gaussian) - before applying activation function
* @return Gradient with respect to the preOutDistributionParams
*/
INDArray gradient(INDArray x, INDArray preOutDistributionParams);
/**
* Randomly sample from P(x|z) using the specified distribution parameters
*
* @param preOutDistributionParams Distribution parameters used by this reconstruction distribution (for example,
* mean and log variance values for Gaussian) - before applying activation function
* @return A random sample of x given the distribution parameters
*/
INDArray generateRandom(INDArray preOutDistributionParams);
/**
* Generate a sample from P(x|z), where x = E[P(x|z)]
* i.e., return the mean value for the distribution
*
* @param preOutDistributionParams Distribution parameters used by this reconstruction distribution (for example,
* mean and log variance values for Gaussian) - before applying activation function
* @return A deterministic sample of x (mean/expected value) given the distribution parameters
*/
INDArray generateAtMean(INDArray preOutDistributionParams);
}