Package org.deeplearning4j.nn.layers.objdetect

Class Yolo2OutputLayer

java.lang.Object

org.deeplearning4j.nn.layers.AbstractLayer<Yolo2OutputLayer>

org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Layer, IOutputLayer, Model, Trainable

public class Yolo2OutputLayer
extends AbstractLayer<Yolo2OutputLayer>
implements Serializable, IOutputLayer

See Also:

Serialized Form

Nested Class Summary

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

Layer.TrainingMode, Layer.Type

Field Summary

Fields

Modifier and Type	Field	Description
protected INDArray	labels

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

cacheMode, conf, dataType, dropoutApplied, epochCount, index, input, inputModificationAllowed, iterationCount, maskArray, maskState, preOutput, trainingListeners

Constructor Summary

Constructors

Constructor	Description
Yolo2OutputLayer(NeuralNetConfiguration conf, DataType dataType)

Method Summary

Modifier and Type	Method	Description
INDArray	activate(boolean training, LayerWorkspaceMgr workspaceMgr)	Perform forward pass and return the activations array with the last set input
Pair<Gradient,INDArray>	backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)	Calculate the gradient relative to the error in the next layer
void	clearNoiseWeightParams()
Layer	clone()
void	computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)	Update the score
double	computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)	Compute score after labels and input have been set.
INDArray	computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
double	f1Score(INDArray examples, INDArray labels)	Returns the f1 score for the given examples.
double	f1Score(DataSet data)	Sets the input and labels and returns a score for the prediction wrt true labels
void	fit(INDArray examples, int[] labels)	Fit the model
void	fit(INDArray examples, INDArray labels)	Fit the model
void	fit(DataSet data)	Fit the model
void	fit(DataSetIterator iter)	Train the model based on the datasetiterator
INDArray	getConfidenceMatrix(INDArray networkOutput, int example, int bbNumber)	Get the confidence matrix (confidence for all x/y positions) for the specified bounding box, from the network output activations array
List<DetectedObject>	getPredictedObjects(INDArray networkOutput, double threshold)
INDArray	getProbabilityMatrix(INDArray networkOutput, int example, int classNumber)	Get the probability matrix (probability of the specified class, assuming an object is present, for all x/y positions), from the network output activations array
Pair<Gradient,Double>	gradientAndScore()	Get the gradient and score
boolean	isPretrainLayer()	Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
boolean	needsLabels()	Returns true if labels are required for this output layer
int	numLabels()	Returns the number of possible labels
int[]	predict(INDArray examples)	Takes in a list of examples For each row, returns a label
List<String>	predict(DataSet dataSet)	Takes in a DataSet of examples For each row, returns a label
double	score()	The score for the model

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

activate, addListeners, allowInputModification, applyConstraints, applyDropOutIfNecessary, applyMask, assertInputSet, backpropDropOutIfPresent, batchSize, calcRegularizationScore, clear, close, conf, feedForwardMaskArray, fit, fit, getConfig, getEpochCount, getGradientsViewArray, getHelper, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, getParam, gradient, init, input, layerConf, layerId, numParams, numParams, params, paramTable, paramTable, setBackpropGradientsViewArray, setCacheMode, setConf, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, setParam, setParams, setParams, setParamsViewArray, setParamTable, type, update, update, updaterDivideByMinibatch

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.layers.IOutputLayer

getLabels, setLabels

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

activate, allowInputModification, calcRegularizationScore, feedForwardMaskArray, getEpochCount, getHelper, getIndex, getInputMiniBatchSize, getIterationCount, getListeners, getMaskArray, setCacheMode, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setIterationCount, setListeners, setListeners, setMaskArray, type

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

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

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

getConfig, getGradientsViewArray, numParams, params, paramTable, updaterDivideByMinibatch

Field Detail

labels

protected INDArray labels

Constructor Detail

Yolo2OutputLayer

public Yolo2OutputLayer(NeuralNetConfiguration conf,
                        DataType dataType)

Method Detail

backpropGradient

public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon,
                                                      LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Calculate the gradient relative to the error in the next layer

Specified by:

backpropGradient in interface Layer

Parameters:

epsilon - w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C is cost function a=sigma(z) is activation.

workspaceMgr - Workspace manager

Returns:

Pair where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD workspace via the workspace manager

activate

public INDArray activate(boolean training,
                         LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Perform forward pass and return the activations array with the last set input

Specified by:

activate in interface Layer

Parameters:

training - training or test mode

workspaceMgr - Workspace manager

Returns:

the activation (layer output) of the last specified input. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

clone

public Layer clone()

Overrides:

clone in class Object

needsLabels

public boolean needsLabels()

Description copied from interface: IOutputLayer

Returns true if labels are required for this output layer

Specified by:

needsLabels in interface IOutputLayer

Returns:

true if this output layer needs labels or not

computeScore

public double computeScore(double fullNetRegTerm,
                           boolean training,
                           LayerWorkspaceMgr workspaceMgr)

Description copied from interface: IOutputLayer

Compute score after labels and input have been set.

Specified by:

computeScore in interface IOutputLayer

Parameters:

fullNetRegTerm - Regularization score (l1/l2/weight decay) for the entire network

training - whether score should be calculated at train or test time (this affects things like application of dropout, etc)

Returns:

score (loss function)

score

public double score()

Description copied from interface: Model

The score for the model

Specified by:

score in interface Model

Overrides:

score in class AbstractLayer<Yolo2OutputLayer>

Returns:

the score for the model

computeGradientAndScore

public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Model

Update the score

Specified by:

computeGradientAndScore in interface Model

Overrides:

computeGradientAndScore in class AbstractLayer<Yolo2OutputLayer>

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 AbstractLayer<Yolo2OutputLayer>

Returns:

the gradient and score

computeScoreForExamples

public INDArray computeScoreForExamples(double fullNetRegTerm,
                                        LayerWorkspaceMgr workspaceMgr)

Description copied from interface: IOutputLayer

Compute the score for each example individually, after labels and input have been set.

Specified by:

computeScoreForExamples in interface IOutputLayer

Parameters:

fullNetRegTerm - Regularization score (l1/l2/weight decay) for the entire network

Returns:

A column INDArray of shape [numExamples,1], where entry i is the score of the ith example

f1Score

public double f1Score(DataSet data)

Description copied from interface: Classifier

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

Specified by:

f1Score in interface Classifier

Parameters:

data - the data to score

Returns:

the score for the given input,label pairs

f1Score

public double f1Score(INDArray examples,
                      INDArray labels)

Description copied from interface: Classifier

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:

f1Score 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()

Description copied from interface: Classifier

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(DataSetIterator iter)

Description copied from interface: Classifier

Train the model based on the datasetiterator

Specified by:

fit in interface Classifier

Parameters:

iter - the iterator to train on

predict

public int[] predict(INDArray examples)

Description copied from interface: Classifier

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

Specified by:

predict in interface Classifier

Parameters:

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

Returns:

the labels for each example

predict

public List<String> predict(DataSet dataSet)

Description copied from interface: Classifier

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

Specified by:

predict in interface Classifier

Parameters:

dataSet - the examples to classify

Returns:

the labels for each example

fit

public void fit(INDArray examples,
                INDArray labels)

Description copied from interface: Classifier

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(DataSet data)

Description copied from interface: Classifier

Fit the model

Specified by:

fit in interface Classifier

Parameters:

data - the data to train on

fit

public void fit(INDArray examples,
                int[] labels)

Description copied from interface: Classifier

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

isPretrainLayer

public boolean isPretrainLayer()

Description copied from interface: Layer

Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)

Specified by:

isPretrainLayer in interface Layer

Returns:

true if the layer can be pretrained (using fit(INDArray), false otherwise

clearNoiseWeightParams

public void clearNoiseWeightParams()

Specified by:

clearNoiseWeightParams in interface Layer

getPredictedObjects

public List<DetectedObject> getPredictedObjects(INDArray networkOutput,
                                                double threshold)

See Also:

YoloUtils.getPredictedObjects(INDArray, INDArray, double, double)

getConfidenceMatrix

public INDArray getConfidenceMatrix(INDArray networkOutput,
                                    int example,
                                    int bbNumber)

Get the confidence matrix (confidence for all x/y positions) for the specified bounding box, from the network output activations array

Parameters:

networkOutput - Network output activations

example - Example number, in minibatch

bbNumber - Bounding box number

Returns:

Confidence matrix

getProbabilityMatrix

public INDArray getProbabilityMatrix(INDArray networkOutput,
                                     int example,
                                     int classNumber)

Get the probability matrix (probability of the specified class, assuming an object is present, for all x/y positions), from the network output activations array

Parameters:

networkOutput - Network output activations

example - Example number, in minibatch

classNumber - Class number

Returns:

Confidence matrix