org.deeplearning4j.nn.api

Interface Classifier

All Superinterfaces:

Model

All Known Subinterfaces:

IOutputLayer

All Known Implementing Classes:

BaseOutputLayer, CenterLossOutputLayer, CustomOutputLayerImpl, LossLayer, MultiLayerNetwork, OutputLayer, RnnOutputLayer

public interface Classifier
extends Model

A classifier (this is for supervised learning)

Method Summary

Modifier and Type	Method and Description
double	f1Score(org.nd4j.linalg.dataset.api.DataSet data) Sets the input and labels and returns a score for the prediction wrt true labels
double	f1Score(org.nd4j.linalg.api.ndarray.INDArray examples, org.nd4j.linalg.api.ndarray.INDArray labels) Returns the f1 score for the given examples.
void	fit(org.nd4j.linalg.dataset.api.DataSet data) Fit the model
void	fit(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iter) Train the model based on the datasetiterator
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	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
java.util.List<java.lang.String>	predict(org.nd4j.linalg.dataset.api.DataSet dataSet) Takes in a DataSet of examples For each row, returns a label
int[]	predict(org.nd4j.linalg.api.ndarray.INDArray examples) Takes in a list of examples For each row, returns a label

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

accumulateScore, applyLearningRateScoreDecay, batchSize, clear, computeGradientAndScore, conf, fit, fit, getOptimizer, getParam, gradient, gradientAndScore, init, initParams, input, iterate, numParams, numParams, params, paramTable, paramTable, score, setBackpropGradientsViewArray, setConf, setListeners, setListeners, setParam, setParams, setParamsViewArray, setParamTable, update, update, validateInput

Method Detail

f1Score

double f1Score(org.nd4j.linalg.dataset.api.DataSet data)

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

Parameters:

data - the data to score

Returns:

the score for the given input,label pairs

f1Score

double f1Score(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.

Parameters:

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

labels - the true labels

Returns:

the scores for each ndarray

numLabels

int numLabels()

Returns the number of possible labels

Returns:

the number of possible labels for this classifier

fit

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

Train the model based on the datasetiterator

Parameters:

iter - the iterator to train on

predict

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

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

Parameters:

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

Returns:

the labels for each example

predict

java.util.List<java.lang.String> predict(org.nd4j.linalg.dataset.api.DataSet dataSet)

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

Parameters:

dataSet - the examples to classify

Returns:

the labels for each example

labelProbabilities

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

Parameters:

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

Returns:

the likelihoods of each example and each label

fit

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

Fit the model

Parameters:

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

labels - the example labels(a binary outcome matrix)

fit

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

Fit the model

Parameters:

data - the data to train on

fit

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

Fit the model

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