org.deeplearning4j.nn.api

Interface Classifier

All Superinterfaces:

Model

All Known Implementing Classes:

BaseMultiLayerNetwork, DBN, OutputLayer, SemanticHashing, StackedAutoEncoder, StackedDenoisingAutoEncoder

public interface Classifier
extends Model

A classifier (this is for supervised learning)

Author:

Adam Gibson

Method Summary

Modifier and Type	Method and Description
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 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	iterate(org.nd4j.linalg.api.ndarray.INDArray examples, int[] labels, Object[] params) Iterate once on 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
int[]	predict(org.nd4j.linalg.api.ndarray.INDArray examples) Takes in a list of examples For each row, returns a label
double	score() Assuming an input and labels are already set will score based on what's already set
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.

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

fit, fit, iterate, numParams, params, setParams, transform

Method Detail

score

double score()

Assuming an input and labels are already set will score based on what's already set

Specified by:

score in interface Model

Returns:

the f1 score for the already set input/output

score

double score(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

score

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.

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

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

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

Fit the model

Parameters:

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

labels - the example labels(a binary outcome matrix)

params - extra parameters

fit

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

Fit the model

Parameters:

data - the data to train on

params - extra parameters

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

fit

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

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

params - extra parameters

iterate

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

Iterate once on 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

params - extra parameters