Package org.deeplearning4j.nn.layers.samediff

Class SameDiffOutputLayer

java.lang.Object

org.deeplearning4j.nn.layers.AbstractLayer<SameDiffOutputLayer>

org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Layer, IOutputLayer, Model, Trainable

public class SameDiffOutputLayer
extends AbstractLayer<SameDiffOutputLayer>
implements IOutputLayer

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.Layer

Layer.TrainingMode, Layer.Type

Field Summary

Fields

Modifier and Type	Field	Description
protected INDArray	gradients
protected Map<String,INDArray>	gradTable
static String	INPUT_KEY
protected INDArray	labels
static String	LABELS_KEY
protected String	outputKey
protected SDVariable	outputVar
protected INDArray	params
protected Map<String,INDArray>	paramTable
protected SameDiff	sameDiff

Fields inherited from class org.deeplearning4j.nn.layers.AbstractLayer

cacheMode, conf, dataType, dropoutApplied, epochCount, index, input, inputModificationAllowed, iterationCount, maskArray, maskState, preOutput, trainingListeners

Constructor Summary

Constructors

Constructor	Description
SameDiffOutputLayer(NeuralNetConfiguration conf, DataType dataType)

Method Summary

Modifier and Type	Method	Description
INDArray	activate(boolean training, LayerWorkspaceMgr workspaceMgr)	Perform forward pass and return the activations array with the last set input
Pair<Gradient,INDArray>	backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)	Calculate the gradient relative to the error in the next layer
void	clearNoiseWeightParams()
Layer	clone()
double	computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)	Compute score after labels and input have been set.
INDArray	computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
protected void	doInit()
double	f1Score(INDArray examples, INDArray labels)	Returns the f1 score for the given examples.
double	f1Score(DataSet data)	Sets the input and labels and returns a score for the prediction wrt true labels
void	fit(INDArray examples, int[] labels)	Fit the model
void	fit(INDArray examples, INDArray labels)	Fit the model
void	fit(DataSet data)	Fit the model
void	fit(DataSetIterator iter)	Train the model based on the datasetiterator
INDArray	getGradientsViewArray()
INDArray	getParam(String param)	Get the parameter
boolean	isPretrainLayer()	Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
boolean	needsLabels()	Returns true if labels are required for this output layer
int	numLabels()	Returns the number of possible labels
long	numParams()	The number of parameters for the model
INDArray	params()	Returns the parameters of the neural network as a flattened row vector
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
int[]	predict(INDArray examples)	Takes in a list of examples For each row, returns a label
List<String>	predict(DataSet dataSet)	Takes in a DataSet of examples For each row, returns a label
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	setParam(String key, INDArray val)	Set the parameter with a new ndarray
void	setParams(INDArray params)	Set the parameters for this model.
protected void	setParams(INDArray params, char order)
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

Methods inherited from class org.deeplearning4j.nn.layers.AbstractLayer

activate, addListeners, allowInputModification, applyConstraints, applyDropOutIfNecessary, applyMask, assertInputSet, backpropDropOutIfPresent, batchSize, calcRegularizationScore, clear, close, computeGradientAndScore, conf, feedForwardMaskArray, fit, fit, getConfig, getEpochCount, getHelper, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, gradient, gradientAndScore, init, input, layerConf, layerId, numParams, score, setCacheMode, setConf, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, type, update, update, updaterDivideByMinibatch

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.layers.IOutputLayer

getLabels, setLabels

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

activate, allowInputModification, calcRegularizationScore, feedForwardMaskArray, getEpochCount, getHelper, getIndex, getInputMiniBatchSize, getIterationCount, getListeners, getMaskArray, setCacheMode, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setIterationCount, setListeners, setListeners, setMaskArray, type

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

addListeners, applyConstraints, batchSize, clear, close, computeGradientAndScore, conf, fit, fit, getOptimizer, gradient, gradientAndScore, init, input, numParams, score, setConf, update, update

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

getConfig, updaterDivideByMinibatch

Field Detail

INPUT_KEY

public static final String INPUT_KEY

See Also:

Constant Field Values

LABELS_KEY

public static final String LABELS_KEY

See Also:

Constant Field Values

sameDiff

protected SameDiff sameDiff

outputVar

protected SDVariable outputVar

outputKey

protected String outputKey

labels

protected INDArray labels

params

protected INDArray params

gradients

protected INDArray gradients

paramTable

protected Map<String,INDArray> paramTable

gradTable

protected Map<String,INDArray> gradTable

Constructor Detail

SameDiffOutputLayer

public SameDiffOutputLayer(NeuralNetConfiguration conf,
                           DataType dataType)

Method Detail

clone

public Layer clone()

Overrides:

clone in class Object

isPretrainLayer

public boolean isPretrainLayer()

Description copied from interface: Layer

Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)

Specified by:

isPretrainLayer in interface Layer

Returns:

true if the layer can be pretrained (using fit(INDArray), false otherwise

clearNoiseWeightParams

public void clearNoiseWeightParams()

Specified by:

clearNoiseWeightParams in interface Layer

activate

public INDArray activate(boolean training,
                         LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Perform forward pass and return the activations array with the last set input

Specified by:

activate in interface Layer

Parameters:

training - training or test mode

workspaceMgr - Workspace manager

Returns:

the activation (layer output) of the last specified input. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

backpropGradient

public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon,
                                                      LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Calculate the gradient relative to the error in the next layer

Specified by:

backpropGradient in interface Layer

Parameters:

epsilon - w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C is cost function a=sigma(z) is activation.

workspaceMgr - Workspace manager

Returns:

Pair where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD workspace via the workspace manager

params

public INDArray params()

Returns the parameters of the neural network as a flattened row vector

Specified by:

params in interface Model

Specified by:

params in interface Trainable

Overrides:

params in class AbstractLayer<SameDiffOutputLayer>

Returns:

the parameters of the neural network

getParam

public INDArray getParam(String param)

Description copied from interface: Model

Get the parameter

Specified by:

getParam in interface Model

Overrides:

getParam in class AbstractLayer<SameDiffOutputLayer>

Parameters:

param - the key of the parameter

Returns:

the parameter vector/matrix with that particular key

numParams

public long numParams()

Description copied from class: AbstractLayer

The number of parameters for the model

Specified by:

numParams in interface Model

Specified by:

numParams in interface Trainable

Overrides:

numParams in class AbstractLayer<SameDiffOutputLayer>

Returns:

the number of parameters for the model

setParam

public void setParam(String key,
                     INDArray val)

Description copied from interface: Model

Set the parameter with a new ndarray

Specified by:

setParam in interface Model

Overrides:

setParam in class AbstractLayer<SameDiffOutputLayer>

Parameters:

key - the key to se t

val - the new ndarray

setParams

public void setParams(INDArray params)

Description copied from interface: Model

Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model

Specified by:

setParams in interface Model

Overrides:

setParams in class AbstractLayer<SameDiffOutputLayer>

Parameters:

params - the parameters for the model

setParams

protected void setParams(INDArray params,
                         char order)

Overrides:

setParams in class AbstractLayer<SameDiffOutputLayer>

setParamsViewArray

public void setParamsViewArray(INDArray params)

Description copied from interface: Model

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.

Specified by:

setParamsViewArray in interface Model

Overrides:

setParamsViewArray in class AbstractLayer<SameDiffOutputLayer>

Parameters:

params - a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array

getGradientsViewArray

public INDArray getGradientsViewArray()

Specified by:

getGradientsViewArray in interface Model

Specified by:

getGradientsViewArray in interface Trainable

Overrides:

getGradientsViewArray in class AbstractLayer<SameDiffOutputLayer>

Returns:

1D gradients view array

setBackpropGradientsViewArray

public void setBackpropGradientsViewArray(INDArray gradients)

Description copied from interface: Model

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.

Specified by:

setBackpropGradientsViewArray in interface Model

Overrides:

setBackpropGradientsViewArray in class AbstractLayer<SameDiffOutputLayer>

Parameters:

gradients - a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array

setParamTable

public void setParamTable(Map<String,INDArray> paramTable)

Description copied from interface: Model

Setter for the param table

Specified by:

setParamTable in interface Model

Overrides:

setParamTable in class AbstractLayer<SameDiffOutputLayer>

paramTable

public Map<String,INDArray> paramTable()

Description copied from interface: Model

The param table

Specified by:

paramTable in interface Model

Overrides:

paramTable in class AbstractLayer<SameDiffOutputLayer>

Returns:

paramTable

public Map<String,INDArray> paramTable(boolean backpropParamsOnly)

Description copied from interface: Model

Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters

Specified by:

paramTable in interface Model

Specified by:

paramTable in interface Trainable

Overrides:

paramTable in class AbstractLayer<SameDiffOutputLayer>

Parameters:

backpropParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())

Returns:

Parameter table

doInit

protected void doInit()

needsLabels

public boolean needsLabels()

Description copied from interface: IOutputLayer

Returns true if labels are required for this output layer

Specified by:

needsLabels in interface IOutputLayer

Returns:

true if this output layer needs labels or not

computeScore

public double computeScore(double fullNetRegTerm,
                           boolean training,
                           LayerWorkspaceMgr workspaceMgr)

Description copied from interface: IOutputLayer

Compute score after labels and input have been set.

Specified by:

computeScore in interface IOutputLayer

Parameters:

fullNetRegTerm - Regularization score (l1/l2/weight decay) for the entire network

training - whether score should be calculated at train or test time (this affects things like application of dropout, etc)

Returns:

score (loss function)

computeScoreForExamples

public INDArray computeScoreForExamples(double fullNetRegTerm,
                                        LayerWorkspaceMgr workspaceMgr)

Description copied from interface: IOutputLayer

Compute the score for each example individually, after labels and input have been set.

Specified by:

computeScoreForExamples in interface IOutputLayer

Parameters:

fullNetRegTerm - Regularization score (l1/l2/weight decay) for the entire network

Returns:

A column INDArray of shape [numExamples,1], where entry i is the score of the ith example

f1Score

public double f1Score(DataSet data)

Description copied from interface: Classifier

Sets the input and labels and returns a score for the prediction wrt true labels

Specified by:

f1Score in interface Classifier

Parameters:

data - the data to score

Returns:

the score for the given input,label pairs

f1Score

public double f1Score(INDArray examples,
                      INDArray labels)

Description copied from interface: Classifier

Returns the f1 score for the given examples. Think of this to be like a percentage right. The higher the number the more it got right. This is on a scale from 0 to 1.

Specified by:

f1Score in interface Classifier

Parameters:

examples - te the examples to classify (one example in each row)

labels - the true labels

Returns:

the scores for each ndarray

numLabels

public int numLabels()

Description copied from interface: Classifier

Returns the number of possible labels

Specified by:

numLabels in interface Classifier

Returns:

the number of possible labels for this classifier

fit

public void fit(DataSetIterator iter)

Description copied from interface: Classifier

Train the model based on the datasetiterator

Specified by:

fit in interface Classifier

Parameters:

iter - the iterator to train on

predict

public int[] predict(INDArray examples)

Description copied from interface: Classifier

Takes in a list of examples For each row, returns a label

Specified by:

predict in interface Classifier

Parameters:

examples - the examples to classify (one example in each row)

Returns:

the labels for each example

predict

public List<String> predict(DataSet dataSet)

Description copied from interface: Classifier

Takes in a DataSet of examples For each row, returns a label

Specified by:

predict in interface Classifier

Parameters:

dataSet - the examples to classify

Returns:

the labels for each example

fit

public void fit(INDArray examples,
                INDArray labels)

Description copied from interface: Classifier

Fit the model

Specified by:

fit in interface Classifier

Parameters:

examples - the examples to classify (one example in each row)

labels - the example labels(a binary outcome matrix)

fit

public void fit(DataSet data)

Description copied from interface: Classifier

Fit the model

Specified by:

fit in interface Classifier

Parameters:

data - the data to train on

fit

public void fit(INDArray examples,
                int[] labels)

Description copied from interface: Classifier

Fit the model

Specified by:

fit in interface Classifier

Parameters:

examples - the examples to classify (one example in each row)

labels - the labels for each example (the number of labels must match the number of rows in the example