org.deeplearning4j.nn.api.layers.IOutputLayer Maven / Gradle / Ivy
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package org.deeplearning4j.nn.api.layers;
import org.deeplearning4j.nn.api.Classifier;
import org.deeplearning4j.nn.api.Layer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
public interface IOutputLayer extends Layer, Classifier {
/**
* Returns true if labels are required
* for this output layer
* @return true if this output layer needs labels or not
*/
boolean needsLabels();
/**
* Set the labels array for this output layer
*
* @param labels Labels array to set
*/
void setLabels(INDArray labels);
/**
* Get the labels array previously set with {@link #setLabels(INDArray)}
*
* @return Labels array, or null if it has not been set
*/
INDArray getLabels();
/**
* Compute score after labels and input have been set.
*
* @param fullNetworkRegScore Regularization score (l1/l2/weight decay) for the entire network
* @param training whether score should be calculated at train or test time (this affects things like application of
* dropout, etc)
* @return score (loss function)
*/
double computeScore(double fullNetworkRegScore, boolean training, LayerWorkspaceMgr workspaceMgr);
/**
* Compute the score for each example individually, after labels and input have been set.
*
* @param fullNetworkRegScore Regularization score (l1/l2/weight decay) for the entire network
* @return A column INDArray of shape [numExamples,1], where entry i is the score of the ith example
*/
INDArray computeScoreForExamples(double fullNetworkRegScore, LayerWorkspaceMgr workspaceMgr);
}