org.deeplearning4j.nn.api

Interface Classifier

All Superinterfaces:

Model

All Known Subinterfaces:

IOutputLayer

All Known Implementing Classes:

BaseOutputLayer, CenterLossOutputLayer, Cnn3DLossLayer, CnnLossLayer, LossLayer, MultiLayerNetwork, OCNNOutputLayer, OutputLayer, RnnLossLayer, RnnOutputLayer, SameDiffOutputLayer, Yolo2OutputLayer

public interface Classifier
extends Model

A classifier (this is for supervised learning)

Author:

Adam Gibson

Method Summary

Modifier and Type	Method and Description
double	f1Score(DataSet data) Sets the input and labels and returns a score for the prediction wrt true labels
double	f1Score(INDArray examples, INDArray labels) Returns the f1 score for the given examples.
void	fit(DataSet data) Fit the model
void	fit(DataSetIterator iter) Train the model based on the datasetiterator
void	fit(INDArray examples, INDArray labels) Fit the model
void	fit(INDArray examples, int[] labels) Fit the model
int	numLabels() Deprecated. Will be removed in a future release
List<String>	predict(DataSet dataSet) Takes in a DataSet of examples For each row, returns a label
int[]	predict(INDArray examples) Takes in a list of examples For each row, returns a label

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

addListeners, applyConstraints, batchSize, clear, computeGradientAndScore, conf, fit, fit, getGradientsViewArray, getOptimizer, getParam, gradient, gradientAndScore, init, input, numParams, numParams, params, paramTable, paramTable, score, setBackpropGradientsViewArray, setConf, setListeners, setListeners, setParam, setParams, setParamsViewArray, setParamTable, update, update

Method Detail

f1Score

double f1Score(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(INDArray examples,
               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

@Deprecated
int numLabels()

Deprecated. Will be removed in a future release

Returns the number of possible labels

Returns:

the number of possible labels for this classifier

fit

void fit(DataSetIterator iter)

Train the model based on the datasetiterator

Parameters:

iter - the iterator to train on

predict

int[] predict(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

List<String> predict(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

fit

void fit(INDArray examples,
         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(DataSet data)

Fit the model

Parameters:

data - the data to train on

fit

void fit(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