All Known Subinterfaces:

GraphVertex, IOutputLayer, Layer, RecurrentLayer

All Known Implementing Classes:

AbstractLayer, ActivationLayer, AutoEncoder, BaseGraphVertex, BaseLayer, BaseOutputLayer, BasePretrainNetwork, BaseRecurrentLayer, BaseWrapperLayer, BaseWrapperVertex, BatchNormalization, BidirectionalLayer, CenterLossOutputLayer, Cnn3DLossLayer, CnnLossLayer, Convolution1DLayer, Convolution3DLayer, ConvolutionLayer, Cropping1DLayer, Cropping2DLayer, Cropping3DLayer, Deconvolution2DLayer, Deconvolution3DLayer, DenseLayer, DepthwiseConvolution2DLayer, DropoutLayer, DuplicateToTimeSeriesVertex, ElementWiseMultiplicationLayer, ElementWiseVertex, EmbeddingLayer, EmbeddingSequenceLayer, FrozenLayer, FrozenLayerWithBackprop, FrozenVertex, GlobalPoolingLayer, GravesBidirectionalLSTM, GravesLSTM, InputVertex, L2NormalizeVertex, L2Vertex, LastTimeStepLayer, LastTimeStepVertex, LayerVertex, LocalResponseNormalization, LossLayer, LSTM, MaskLayer, MaskZeroLayer, MergeVertex, MultiLayerNetwork, OCNNOutputLayer, OutputLayer, PoolHelperVertex, PReLU, PreprocessorVertex, RepeatVector, ReshapeVertex, ReverseTimeSeriesVertex, RnnLossLayer, RnnOutputLayer, SameDiffGraphVertex, SameDiffLayer, SameDiffOutputLayer, ScaleVertex, SeparableConvolution2DLayer, ShiftVertex, SimpleRnn, SpaceToBatch, SpaceToDepth, StackVertex, Subsampling1DLayer, Subsampling3DLayer, SubsamplingLayer, SubsetVertex, TimeDistributedLayer, UnstackVertex, Upsampling1D, Upsampling2D, Upsampling3D, VariationalAutoencoder, Yolo2OutputLayer, ZeroPadding1DLayer, ZeroPadding3DLayer, ZeroPaddingLayer
```
public interface Trainable
```

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`TrainingConfig`	`getConfig()`
`INDArray`	`getGradientsViewArray()`
`long`	`numParams()`
`INDArray`	`params()`
`Map<String,INDArray>`	`paramTable(boolean backpropOnly)`
`boolean`	`updaterDivideByMinibatch(String paramName)`	DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size. However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector.

- Method Detail
  - getConfig
```
TrainingConfig getConfig()
```
    Returns:
    
    Training configuration
  - numParams
```
long numParams()
```
    Returns:
    
    Number of parameters
  - params
```
INDArray params()
```
    Returns:
    
    1d parameter vector
  - paramTable
```
Map<String,INDArray> paramTable(boolean backpropOnly)
```
    Parameters:
    
    backpropOnly - If true: return only parameters that are not exclusively used for layerwise pretraining
    
    Returns:
    
    Parameter table
  - updaterDivideByMinibatch
```
boolean updaterDivideByMinibatch(String paramName)
```
    DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size.
    However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector. Put another way, most gradients should be divided by the minibatch to get the average; some "gradients" are actually final updates already, and should not be divided by the minibatch size.
    
    Parameters:
    
    paramName - Name of the parameter
    
    Returns:
    
    True if gradients should be divided by minibatch (most params); false otherwise (edge cases like batch norm mean/variance estimates)
  - getGradientsViewArray
```
INDArray getGradientsViewArray()
```
    Returns:
    
    1D gradients view array

Interface Trainable

Method Summary

Method Detail

getConfig

numParams

params

paramTable

updaterDivideByMinibatch

getGradientsViewArray