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 functions for different objectives. This includes classification as well as prediction

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	OutputLayer.Builder

Field Summary

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

b, conf, dropoutMask, input, W

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
Layer	clone()
void	fit(org.nd4j.linalg.dataset.api.DataSet data) Fit the model
void	fit(org.nd4j.linalg.dataset.api.DataSet data, Object[] params) Fit the model
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, org.nd4j.linalg.api.ndarray.INDArray labels, Object[] params) Fit the model
void	fit(org.nd4j.linalg.api.ndarray.INDArray examples, int[] labels) Fit the model
void	fit(org.nd4j.linalg.api.ndarray.INDArray examples, int[] labels, Object[] params) Fit the model
void	fit(org.nd4j.linalg.api.ndarray.INDArray data, Object[] params) Fit the model to the given data
org.nd4j.linalg.learning.AdaGrad	getAdaGrad()
org.nd4j.linalg.learning.AdaGrad	getBiasAdaGrad()
OutputLayerGradient	getGradient(double lr) Gets the gradient from one training iteration
org.nd4j.linalg.api.ndarray.INDArray	getLabels()
void	iterate(org.nd4j.linalg.api.ndarray.INDArray examples, int[] labels, Object[] params) Iterate once on the model
void	iterate(org.nd4j.linalg.api.ndarray.INDArray input, Object[] params) Run one iteration
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
int	numParams() The number of parameters for the model
org.nd4j.linalg.api.ndarray.INDArray	output(org.nd4j.linalg.api.ndarray.INDArray x) Classify input
org.nd4j.linalg.api.ndarray.INDArray	params() Returns the coefficients for this classifier as a raveled vector
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	setAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)
void	setBiasAdaGrad(org.nd4j.linalg.learning.AdaGrad biasAdaGrad)
void	setLabels(org.nd4j.linalg.api.ndarray.INDArray labels)
void	setParams(org.nd4j.linalg.api.ndarray.INDArray params) Set the parameters for this model.
String	toString()
void	train(double lr) Train with current input and labels with the given learning rate
void	train(org.nd4j.linalg.api.ndarray.INDArray x, double lr) Train with the given input and the currently applyTransformToDestination labels
void	train(org.nd4j.linalg.api.ndarray.INDArray x, org.nd4j.linalg.api.ndarray.INDArray y, double lr) Train on the given inputs and labels.
void	trainTillConvergence(double learningRate, int numEpochs) Run conjugate gradient
void	trainTillConvergence(double learningRate, int numEpochs, TrainingEvaluator eval) Run the optimization algorithm for training
void	trainTillConvergence(org.nd4j.linalg.api.ndarray.INDArray labels, double learningRate, int numEpochs, TrainingEvaluator eval) Run the optimization algorithm for training
void	trainTillConvergence(org.nd4j.linalg.api.ndarray.INDArray x, org.nd4j.linalg.api.ndarray.INDArray y, double learningRate, int epochs) Run conjugate gradient with the given x and y
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

activate, activate, activationMean, applyDropOutIfNecessary, conf, createBias, createWeightMatrix, getB, getInput, getW, merge, preOutput, setB, setConfiguration, setInput, setW, transpose

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

OutputLayer

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

Method Detail

train

public void train(double lr)

Train with current input and labels with the given learning rate

Parameters:

lr - the learning rate to use

train

public void train(org.nd4j.linalg.api.ndarray.INDArray x,
                  double lr)

Train with the given input and the currently applyTransformToDestination labels

Parameters:

x - the input to use

lr - the learning rate to use

trainTillConvergence

public void trainTillConvergence(org.nd4j.linalg.api.ndarray.INDArray x,
                                 org.nd4j.linalg.api.ndarray.INDArray y,
                                 double learningRate,
                                 int epochs)

Run conjugate gradient with the given x and y

Parameters:

x - the input to use

y - the labels to use

learningRate -

epochs -

trainTillConvergence

public void trainTillConvergence(org.nd4j.linalg.api.ndarray.INDArray labels,
                                 double learningRate,
                                 int numEpochs,
                                 TrainingEvaluator eval)

Run the optimization algorithm for training

Parameters:

learningRate - the learning rate to iterate with

numEpochs - the number of epochs

eval - the training evaluator to use for early stopping (where applicable)

trainTillConvergence

public void trainTillConvergence(double learningRate,
                                 int numEpochs,
                                 TrainingEvaluator eval)

Run the optimization algorithm for training

Parameters:

learningRate - the learning rate to iterate with

numEpochs - the number of epochs

eval - the training evaluator to use for early stopping (where applicable)

trainTillConvergence

public void trainTillConvergence(double learningRate,
                                 int numEpochs)

Run conjugate gradient

Parameters:

learningRate - the learning rate to iterate with

numEpochs - the number of epochs

score

public double score()

Objective function: the specified objective

Specified by:

score in interface Classifier

Specified by:

score in interface Model

Returns:

the score for the objective

train

public void train(org.nd4j.linalg.api.ndarray.INDArray x,
                  org.nd4j.linalg.api.ndarray.INDArray y,
                  double lr)

Train on the given inputs and labels. This will assign the passed in values as fields to this logistic function for caching.

Parameters:

x - the inputs to iterate on

y - the labels to iterate on

lr - the learning rate

clone

public Layer clone()

Specified by:

clone in interface Layer

Overrides:

clone in class BaseLayer

getGradient

public OutputLayerGradient getGradient(double lr)

Gets the gradient from one training iteration

Parameters:

lr - the learning rate to use for training

Returns:

the gradient (bias and weight matrix)

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

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,
                org.nd4j.linalg.api.ndarray.INDArray labels,
                Object[] params)

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)

params - extra parameters

fit

public void fit(org.nd4j.linalg.dataset.api.DataSet data,
                Object[] params)

Fit the model

Specified by:

fit in interface Classifier

Parameters:

data - the data to train on

params - extra parameters

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

fit

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

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

params - extra parameters

iterate

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

Iterate once on the model

Specified by:

iterate 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

params - extra parameters

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

params

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

Returns the coefficients for this classifier as a raveled vector

Specified by:

params in interface Model

Returns:

a copy of this classifier's params

numParams

public int numParams()

The number of parameters for the model

Specified by:

numParams in interface Model

Returns:

the number of parameters for the model

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

Parameters:

params - the parameters for the model

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray data,
                Object[] params)

Fit the model to the given data

Specified by:

fit in interface Model

Parameters:

data - the data to fit the model to

params - the params (mixed values)

fit

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

Fit the model to the given data

Specified by:

fit in interface Model

Parameters:

data - the data to fit the model to

iterate

public void iterate(org.nd4j.linalg.api.ndarray.INDArray input,
                    Object[] params)

Run one iteration

Specified by:

iterate in interface Model

Parameters:

input - the input to iterate on

params - the extra params for the neural network(k, corruption level, max epochs,...)

toString

public String toString()

Overrides:

toString in class BaseLayer

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)

getBiasAdaGrad

public org.nd4j.linalg.learning.AdaGrad getBiasAdaGrad()

getAdaGrad

public org.nd4j.linalg.learning.AdaGrad getAdaGrad()

setAdaGrad

public void setAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)

setBiasAdaGrad

public void setBiasAdaGrad(org.nd4j.linalg.learning.AdaGrad biasAdaGrad)