org.deeplearning4j.nn.layers

Class OutputLayer

java.lang.Object

org.deeplearning4j.nn.layers.BaseLayer

org.deeplearning4j.nn.layers.OutputLayer

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Layer, Model

public class OutputLayer
extends BaseLayer
implements Serializable, Classifier

Output layer with different objective cooccurrences for different objectives. This includes classification as well as prediction

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

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

Layer.Type

Field Summary

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

conf, dropoutMask, input, iterationListeners, optimizer, paramInitializer, params, score

Constructor Summary

Constructors

Constructor and Description
OutputLayer(NeuralNetConfiguration conf)
OutputLayer(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)

Method Summary

Modifier and Type	Method and Description
void	clear() Clear input
void	fit(org.nd4j.linalg.dataset.api.DataSet data) Fit the model
void	fit(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iter)
void	fit(org.nd4j.linalg.api.ndarray.INDArray data) Fit the model to the given data
void	fit(org.nd4j.linalg.api.ndarray.INDArray examples, org.nd4j.linalg.api.ndarray.INDArray labels) Fit the model
void	fit(org.nd4j.linalg.api.ndarray.INDArray examples, int[] labels) Fit the model
org.nd4j.linalg.api.ndarray.INDArray	getLabels()
Gradient	gradient() Gets the gradient from one training iteration
Pair<Gradient,Double>	gradientAndScore() Get the gradient and score
void	iterate(org.nd4j.linalg.api.ndarray.INDArray input) iterate one iteration of the network
org.nd4j.linalg.api.ndarray.INDArray	labelProbabilities(org.nd4j.linalg.api.ndarray.INDArray examples) Returns the probabilities for each label for each example row wise
int	numLabels() Returns the number of possible labels
org.nd4j.linalg.api.ndarray.INDArray	output(org.nd4j.linalg.api.ndarray.INDArray x) Classify input
int[]	predict(org.nd4j.linalg.api.ndarray.INDArray d) Returns the predictions for each example in the dataset
double	score() Objective function: the specified objective
double	score(org.nd4j.linalg.dataset.api.DataSet data) Sets the input and labels and returns a score for the prediction wrt true labels
double	score(org.nd4j.linalg.api.ndarray.INDArray examples, org.nd4j.linalg.api.ndarray.INDArray labels) Returns the f1 score for the given examples.
void	setLabels(org.nd4j.linalg.api.ndarray.INDArray labels)
void	setParams(org.nd4j.linalg.api.ndarray.INDArray params) Set the parameters for this model.
void	setScore() Update the score
org.nd4j.linalg.api.ndarray.INDArray	transform(org.nd4j.linalg.api.ndarray.INDArray data) Transform the data based on the model's output.

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

accumulateScore, activate, activate, activationMean, applyDropOutIfNecessary, backWard, backwardGradient, batchSize, calcGradient, clone, conf, createGradient, derivativeActivation, error, errorSignal, fit, getIterationListeners, getOptimizer, getParam, initParams, input, merge, numParams, params, paramTable, preOutput, setConf, setIterationListeners, setParam, setParamTable, transpose, type, update, validateInput

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.Model

accumulateScore, batchSize, conf, fit, getOptimizer, getParam, initParams, input, numParams, params, paramTable, setConf, setParam, setParamTable, update, validateInput

Constructor Detail

OutputLayer

public OutputLayer(NeuralNetConfiguration conf)

OutputLayer

public OutputLayer(NeuralNetConfiguration conf,
                   org.nd4j.linalg.api.ndarray.INDArray input)

Method Detail

score

public double score()

Objective function: the specified objective

Specified by:

score in interface Model

Overrides:

score in class BaseLayer

Returns:

the score for the objective

setScore

public void setScore()

Description copied from interface: Model

Update the score

Specified by:

setScore in interface Model

Overrides:

setScore in class BaseLayer

gradient

public Gradient gradient()

Gets the gradient from one training iteration

Specified by:

gradient in interface Model

Returns:

the gradient (bias and weight matrix)

gradientAndScore

public Pair<Gradient,Double> gradientAndScore()

Description copied from interface: Model

Get the gradient and score

Specified by:

gradientAndScore in interface Model

Overrides:

gradientAndScore in class BaseLayer

Returns:

the gradient and score

score

public double score(org.nd4j.linalg.dataset.api.DataSet data)

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

Specified by:

score in interface Classifier

Parameters:

data - the data to score

Returns:

the score for the given input,label pairs

score

public double score(org.nd4j.linalg.api.ndarray.INDArray examples,
                    org.nd4j.linalg.api.ndarray.INDArray labels)

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:

score 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()

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(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iter)

Specified by:

fit in interface Classifier

predict

public int[] predict(org.nd4j.linalg.api.ndarray.INDArray d)

Returns the predictions for each example in the dataset

Specified by:

predict in interface Classifier

Parameters:

d - the matrix to predict

Returns:

the prediction for the dataset

labelProbabilities

public org.nd4j.linalg.api.ndarray.INDArray labelProbabilities(org.nd4j.linalg.api.ndarray.INDArray examples)

Returns the probabilities for each label for each example row wise

Specified by:

labelProbabilities in interface Classifier

Parameters:

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

Returns:

the likelihoods of each example and each label

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray examples,
                org.nd4j.linalg.api.ndarray.INDArray labels)

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(org.nd4j.linalg.dataset.api.DataSet data)

Fit the model

Specified by:

fit in interface Classifier

Parameters:

data - the data to train on

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray examples,
                int[] labels)

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

clear

public void clear()

Description copied from interface: Model

Clear input

Specified by:

clear in interface Model

Overrides:

clear in class BaseLayer

transform

public org.nd4j.linalg.api.ndarray.INDArray transform(org.nd4j.linalg.api.ndarray.INDArray data)

Transform the data based on the model's output. This can be anything from a number to reconstructions.

Specified by:

transform in interface Model

Parameters:

data - the data to transform

Returns:

the transformed data

setParams

public void setParams(org.nd4j.linalg.api.ndarray.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

Specified by:

setParams in interface Model

Overrides:

setParams in class BaseLayer

Parameters:

params - the parameters for the model

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray data)

Fit the model to the given data

Specified by:

fit in interface Model

Overrides:

fit in class BaseLayer

Parameters:

data - the data to fit the model to

iterate

public void iterate(org.nd4j.linalg.api.ndarray.INDArray input)

Description copied from class: BaseLayer

iterate one iteration of the network

Specified by:

iterate in interface Model

Overrides:

iterate in class BaseLayer

Parameters:

input - the input to iterate on

output

public org.nd4j.linalg.api.ndarray.INDArray output(org.nd4j.linalg.api.ndarray.INDArray x)

Classify input

Parameters:

x - the input (can either be a matrix or vector) If it's a matrix, each row is considered an example and associated rows are classified accordingly. Each row will be the likelihood of a label given that example

Returns:

a probability distribution for each row

getLabels

public org.nd4j.linalg.api.ndarray.INDArray getLabels()

setLabels

public void setLabels(org.nd4j.linalg.api.ndarray.INDArray labels)