BasePretrainNetwork (deeplearning4j-nn 1.0.0-M2.1 API)

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 Description

static class BasePretrainNetwork.Builder<T extends BasePretrainNetwork.Builder<T>>

Field Summary

Fields
Modifier and Type Field 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 Description

BasePretrainNetwork(BasePretrainNetwork.Builder builder)

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	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