Model (deeplearning4j-nn 1.0.0-M2 API)

All Known Subinterfaces:

Classifier, IOutputLayer, Layer, RecurrentLayer

All Known Implementing Classes:

AbstractLayer, ActivationLayer, AutoEncoder, BaseLayer, BaseOutputLayer, BasePretrainNetwork, BaseRecurrentLayer, BaseWrapperLayer, BatchNormalization, BidirectionalLayer, CenterLossOutputLayer, Cnn3DLossLayer, CnnLossLayer, ComputationGraph, Convolution1DLayer, Convolution3DLayer, ConvolutionLayer, Cropping1DLayer, Cropping2DLayer, Cropping3DLayer, Deconvolution2DLayer, Deconvolution3DLayer, DenseLayer, DepthwiseConvolution2DLayer, DropoutLayer, ElementWiseMultiplicationLayer, EmbeddingLayer, EmbeddingSequenceLayer, FrozenLayer, FrozenLayerWithBackprop, GlobalPoolingLayer, GravesBidirectionalLSTM, GravesLSTM, LastTimeStepLayer, LocalResponseNormalization, LossLayer, LSTM, MaskLayer, MaskZeroLayer, MultiLayerNetwork, OCNNOutputLayer, OutputLayer, PReLU, RepeatVector, RnnLossLayer, RnnOutputLayer, SameDiffLayer, SameDiffOutputLayer, SeparableConvolution2DLayer, SimpleRnn, SpaceToBatch, SpaceToDepth, Subsampling1DLayer, Subsampling3DLayer, SubsamplingLayer, TimeDistributedLayer, Upsampling1D, Upsampling2D, Upsampling3D, VariationalAutoencoder, Yolo2OutputLayer, ZeroPadding1DLayer, ZeroPadding3DLayer, ZeroPaddingLayer
```
public interface Model
```

Method Summary

All Methods Instance Methods Abstract Methods Deprecated Methods
Modifier and Type	Method and Description
`void`	`addListeners(TrainingListener... listener)` This method ADDS additional TrainingListener to existing listeners
`void`	`applyConstraints(int iteration, int epoch)` Apply any constraints to the model
`int`	`batchSize()` The current inputs batch size
`void`	`clear()` Clear input
`void`	`close()`
`void`	`computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)` Update the score
`NeuralNetConfiguration`	`conf()` The configuration for the neural network
`void`	`fit()` Deprecated.
`void`	`fit(INDArray data, LayerWorkspaceMgr workspaceMgr)` Fit the model to the given data
`INDArray`	`getGradientsViewArray()`
`ConvexOptimizer`	`getOptimizer()` Returns this models optimizer
`INDArray`	`getParam(String param)` Get the parameter
`Gradient`	`gradient()` Get the gradient.
`Pair<Gradient,Double>`	`gradientAndScore()` Get the gradient and score
`void`	`init()` Init the model
`INDArray`	`input()` The input/feature matrix for the model
`long`	`numParams()` the number of parameters for the model
`long`	`numParams(boolean backwards)` the number of parameters for the model
`INDArray`	`params()` Parameters of the model (if any)
`Map<String,INDArray>`	`paramTable()` The param table
`Map<String,INDArray>`	`paramTable(boolean backpropParamsOnly)` Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
`double`	`score()` The score for the model
`void`	`setBackpropGradientsViewArray(INDArray gradients)` Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
`void`	`setConf(NeuralNetConfiguration conf)` Setter for the configuration
`void`	`setListeners(Collection<TrainingListener> listeners)` Set the trainingListeners for the ComputationGraph (and all layers in the network)
`void`	`setListeners(TrainingListener... listeners)` Set the trainingListeners for the ComputationGraph (and all layers in the network)
`void`	`setParam(String key, INDArray val)` Set the parameter with a new ndarray
`void`	`setParams(INDArray params)` Set the parameters for this model.
`void`	`setParamsViewArray(INDArray params)` Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
`void`	`setParamTable(Map<String,INDArray> paramTable)` Setter for the param table
`void`	`update(Gradient gradient)` Update layer weights and biases with gradient change
`void`	`update(INDArray gradient, String paramType)` Perform one update applying the gradient

- Method Detail
  - init
```
void init()
```
    Init the model
  - setListeners
```
void setListeners(Collection<TrainingListener> listeners)
```
    Set the trainingListeners for the ComputationGraph (and all layers in the network)
  - setListeners
```
void setListeners(TrainingListener... listeners)
```
    Set the trainingListeners for the ComputationGraph (and all layers in the network)
  - addListeners
```
void addListeners(TrainingListener... listener)
```
    This method ADDS additional TrainingListener to existing listeners
    
    Parameters:
    
    listener -
  - fit
```
@Deprecated
void fit()
```
    Deprecated.
    
    All models have a fit method
  - update
```
void update(Gradient gradient)
```
    Update layer weights and biases with gradient change
  - update
```
void update(INDArray gradient,
            String paramType)
```
    Perform one update applying the gradient
    
    Parameters:
    
    gradient - the gradient to apply
  - score
```
double score()
```
    The score for the model
    
    Returns:
    
    the score for the model
  - computeGradientAndScore
```
void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
```
    Update the score
  - params
```
INDArray params()
```
    Parameters of the model (if any)
    
    Returns:
    
    the parameters of the model
  - numParams
```
long numParams()
```
    the number of parameters for the model
    
    Returns:
    
    the number of parameters for the model
  - numParams
```
long numParams(boolean backwards)
```
    the number of parameters for the model
    
    Returns:
    
    the number of parameters for the model
  - setParams
```
void setParams(INDArray params)
```
    Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model
    
    Parameters:
    
    params - the parameters for the model
  - setParamsViewArray
```
void setParamsViewArray(INDArray params)
```
    Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
    
    Parameters:
    
    params - a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array
  - getGradientsViewArray
```
INDArray getGradientsViewArray()
```
  - setBackpropGradientsViewArray
```
void setBackpropGradientsViewArray(INDArray gradients)
```
    Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
    
    Parameters:
    
    gradients - a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array
  - fit
```
void fit(INDArray data,
         LayerWorkspaceMgr workspaceMgr)
```
    Fit the model to the given data
    
    Parameters:
    
    data - the data to fit the model to
  - gradient
```
Gradient gradient()
```
    Get the gradient. Note that this method will not calculate the gradient, it will rather return the gradient that has been computed before. For calculating the gradient, see computeGradientAndScore(LayerWorkspaceMgr) } .
    
    Returns:
    
    the gradient for this model, as calculated before
  - gradientAndScore
```
Pair<Gradient,Double> gradientAndScore()
```
    Get the gradient and score
    
    Returns:
    
    the gradient and score
  - batchSize
```
int batchSize()
```
    The current inputs batch size
    
    Returns:
    
    the current inputs batch size
  - conf
```
NeuralNetConfiguration conf()
```
    The configuration for the neural network
    
    Returns:
    
    the configuration for the neural network
  - setConf
```
void setConf(NeuralNetConfiguration conf)
```
    Setter for the configuration
    
    Parameters:
    
    conf -
  - input
```
INDArray input()
```
    The input/feature matrix for the model
    
    Returns:
    
    the input/feature matrix for the model
  - getOptimizer
```
ConvexOptimizer getOptimizer()
```
    Returns this models optimizer
    
    Returns:
    
    this models optimizer
  - getParam
```
INDArray getParam(String param)
```
    Get the parameter
    
    Parameters:
    
    param - the key of the parameter
    
    Returns:
    
    the parameter vector/matrix with that particular key
  - paramTable
```
Map<String,INDArray> paramTable()
```
    The param table
    
    Returns:
  - paramTable
```
Map<String,INDArray> paramTable(boolean backpropParamsOnly)
```
    Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
    
    Parameters:
    
    backpropParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())
  - setParamTable
```
void setParamTable(Map<String,INDArray> paramTable)
```
    Setter for the param table
    
    Parameters:
    
    paramTable -
  - setParam
```
void setParam(String key,
              INDArray val)
```
    Set the parameter with a new ndarray
    
    Parameters:
    
    key - the key to se t
    
    val - the new ndarray
  - clear
```
void clear()
```
    Clear input
  - applyConstraints
```
void applyConstraints(int iteration,
                      int epoch)
```
    Apply any constraints to the model
  - close
```
void close()
```

Interface Model

Method Summary

Method Detail

init

setListeners

setListeners

addListeners

fit

update

update

score

computeGradientAndScore

params

numParams

numParams

setParams

setParamsViewArray

getGradientsViewArray

setBackpropGradientsViewArray

fit

gradient

gradientAndScore

batchSize

conf

setConf

input

getOptimizer

getParam

paramTable

paramTable

setParamTable

setParam

clear

applyConstraints

close