org.deeplearning4j.nn.conf.layers

Class BasePretrainNetwork

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.BaseLayer

org.deeplearning4j.nn.conf.layers.FeedForwardLayer

org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig

Direct Known Subclasses:

AutoEncoder, VariationalAutoencoder

public abstract class BasePretrainNetwork
extends FeedForwardLayer

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	BasePretrainNetwork.Builder<T extends BasePretrainNetwork.Builder<T>>

Field Summary

Fields

Modifier and Type	Field and Description
protected LossFunctions.LossFunction	lossFunction
protected double	visibleBiasInit

Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

nIn, nOut, timeDistributedFormat

Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer

activationFn, biasInit, biasUpdater, gainInit, gradientNormalization, gradientNormalizationThreshold, iUpdater, regularization, regularizationBias, weightInitFn, weightNoise

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer

constraints, iDropout, layerName

Constructor Summary

Constructors

Constructor and Description
BasePretrainNetwork(BasePretrainNetwork.Builder builder)

Method Summary

Modifier and Type	Method and Description
boolean	isPretrainParam(String paramName) Is the specified parameter a layerwise pretraining only parameter? For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop. Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.

Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

getOutputType, getPreProcessorForInputType, setNIn

Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer

clone, getGradientNormalization, getRegularizationByParam, getUpdaterByParam, resetLayerDefaultConfig

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer

getMemoryReport, initializeConstraints, initializer, instantiate, setDataType

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.deeplearning4j.nn.api.TrainingConfig

getGradientNormalizationThreshold, getLayerName

Field Detail

lossFunction

protected LossFunctions.LossFunction lossFunction

visibleBiasInit

protected double visibleBiasInit

Constructor Detail

BasePretrainNetwork

public BasePretrainNetwork(BasePretrainNetwork.Builder builder)

Method Detail

isPretrainParam

public boolean isPretrainParam(String paramName)

Description copied from class: Layer

Is the specified parameter a layerwise pretraining only parameter?
For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.

Specified by:

isPretrainParam in interface TrainingConfig

Overrides:

isPretrainParam in class FeedForwardLayer

Parameters:

paramName - Parameter name/key

Returns:

True if the parameter is for layerwise pretraining only, false otherwise