/*-
*
* * Copyright 2015 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://www.apache.org/licenses/LICENSE-2.0
* *
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS,
* * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* * See the License for the specific language governing permissions and
* * limitations under the License.
*
*/
package org.deeplearning4j.nn.layers;
import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.Updater;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.optimize.Solver;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.util.FeatureUtil;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
/**
* LossLayer is a flexible output "layer" that performs a loss function on
* an input without MLP logic.
*
* @author Justin Long (crockpotveggies)
*/
public class LossLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.LossLayer>
implements Serializable, IOutputLayer {
//current input and label matrices
protected INDArray labels;
private transient Solver solver;
private double fullNetworkL1;
private double fullNetworkL2;
public LossLayer(NeuralNetConfiguration conf) {
super(conf);
}
public LossLayer(NeuralNetConfiguration conf, INDArray input) {
super(conf, input);
}
/** Compute score after labels and input have been set.
* @param fullNetworkL1 L1 regularization term for the entire network
* @param fullNetworkL2 L2 regularization term 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)
*/
@Override
public double computeScore(double fullNetworkL1, double fullNetworkL2, boolean training) {
if (input == null || labels == null)
throw new IllegalStateException("Cannot calculate score without input and labels " + layerId());
this.fullNetworkL1 = fullNetworkL1;
this.fullNetworkL2 = fullNetworkL2;
INDArray preOut = input;
ILossFunction lossFunction = layerConf().getLossFn();
//double score = lossFunction.computeScore(getLabels2d(), preOut, layerConf().getActivationFunction(), maskArray, false);
double score = lossFunction.computeScore(getLabels2d(), preOut, layerConf().getActivationFn(), maskArray,
false);
score += fullNetworkL1 + fullNetworkL2;
score /= getInputMiniBatchSize();
this.score = score;
return score;
}
/**Compute the score for each example individually, after labels and input have been set.
*
* @param fullNetworkL1 L1 regularization term for the entire network (or, 0.0 to not include regularization)
* @param fullNetworkL2 L2 regularization term for the entire network (or, 0.0 to not include regularization)
* @return A column INDArray of shape [numExamples,1], where entry i is the score of the ith example
*/
@Override
public INDArray computeScoreForExamples(double fullNetworkL1, double fullNetworkL2) {
if (input == null || labels == null)
throw new IllegalStateException("Cannot calculate score without input and labels " + layerId());
INDArray preOut = input;
ILossFunction lossFunction = layerConf().getLossFn();
INDArray scoreArray =
lossFunction.computeScoreArray(getLabels2d(), preOut, layerConf().getActivationFn(), maskArray);
double l1l2 = fullNetworkL1 + fullNetworkL2;
if (l1l2 != 0.0) {
scoreArray.addi(l1l2);
}
return scoreArray;
}
@Override
public void computeGradientAndScore() {
if (input == null || labels == null)
return;
INDArray preOut = input;
Pair<Gradient, INDArray> pair = getGradientsAndDelta(preOut);
this.gradient = pair.getFirst();
score = computeScore(fullNetworkL1, fullNetworkL2, true);
}
@Override
protected void setScoreWithZ(INDArray z) {
throw new RuntimeException("Not supported " + layerId());
}
@Override
public Pair<Gradient, Double> gradientAndScore() {
return new Pair<>(gradient(), score());
}
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon) {
return getGradientsAndDelta(input);
}
/** Returns tuple: {Gradient,Delta,Output} given preOut */
private Pair<Gradient, INDArray> getGradientsAndDelta(INDArray preOut) {
// delta calculation
ILossFunction lossFunction = layerConf().getLossFn();
INDArray delta = lossFunction.computeGradient(getLabels2d(), preOut, layerConf().getActivationFn(), maskArray);
// grab the empty gradient
Gradient gradient = new DefaultGradient();
return new Pair<>(gradient, delta);
}
/**
* Gets the gradient from one training iteration
* @return the gradient (bias and weight matrix)
*/
@Override
public Gradient gradient() {
return gradient;
}
@Override
public double calcL2(boolean backpropParamsOnly) {
return 0;
}
@Override
public double calcL1(boolean backpropParamsOnly) {
return 0;
}
@Override
public Type type() {
return Type.FEED_FORWARD;
}
@Override
public void fit(INDArray input) {
// no-op
}
@Override
public INDArray activate(boolean training) {
INDArray z = input;
//INDArray ret = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(
// conf.getLayer().getActivationFunction(), z.dup(), conf.getExtraArgs() ));
INDArray ret = conf.getLayer().getActivationFn().getActivation(z.dup(), training);
if (maskArray != null) {
ret.muliColumnVector(maskArray);
}
return ret;
}
@Override
public INDArray activate(INDArray input, boolean training) {
setInput(input);
return output(training);
}
@Override
public INDArray activate(INDArray input) {
setInput(input);
return output(true);
}
@Override
public INDArray activate() {
return output(false);
}
public INDArray output(INDArray input, boolean training) {
setInput(input);
return output(training);
}
public INDArray output(INDArray input) {
setInput(input);
return output(false);
}
/**
* Classify input
* @param training determines if its training
* the input (can either be a matrix or vector)
* If it's a matrix, each row is considered an example
* and associated rows are classified accordingly.
* Each row will be the likelihood of a label given that example
* @return a probability distribution for each row
*/
public INDArray output(boolean training) {
if (input == null) {
throw new IllegalArgumentException("Cannot perform forward pass with null input " + layerId());
}
return activate(training);
}
@Override
public Layer transpose() {
throw new UnsupportedOperationException("Not applicable " + layerId());
}
@Override
public boolean isPretrainLayer() {
return false;
}
@Override
public Gradient calcGradient(Gradient layerError, INDArray indArray) {
throw new UnsupportedOperationException("Not supported " + layerId());
}
@Override
public void merge(Layer layer, int batchSize) {
throw new UnsupportedOperationException("Not supported " + layerId());
}
@Override
public INDArray params() {
return null;
}
/**
* Sets the input and labels and returns a score for the prediction
* wrt true labels
*
* @param data the data to score
* @return the score for the given input,label pairs
*/
@Override
public double f1Score(DataSet data) {
return f1Score(data.getFeatures(), data.getLabels());
}
/**
* 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.
*
* @param examples te the examples to classify (one example in each row)
* @param labels the true labels
* @return the scores for each ndarray
*/
@Override
public double f1Score(INDArray examples, INDArray labels) {
Evaluation eval = new Evaluation();
eval.eval(labels, labelProbabilities(examples));
return eval.f1();
}
/**
* Returns the number of possible labels
*
* @return the number of possible labels for this classifier
*/
@Override
public int numLabels() {
return labels.size(1);
}
@Override
public void fit(DataSetIterator iter) {
// no-op
}
/**
* Returns the predictions for each example in the dataset
* @param input the matrix to predict
* @return the prediction for the dataset
*/
@Override
public int[] predict(INDArray input) {
INDArray output = output(input);
int[] ret = new int[input.rows()];
for (int i = 0; i < ret.length; i++)
ret[i] = Nd4j.getBlasWrapper().iamax(output.getRow(i));
return ret;
}
/**
* Return predicted label names
*
* @param dataSet to predict
* @return the predicted labels for the dataSet
*/
@Override
public List<String> predict(DataSet dataSet) {
int[] intRet = predict(dataSet.getFeatures());
List<String> ret = new ArrayList<>();
for (int i : intRet) {
ret.add(i, dataSet.getLabelName(i));
}
return ret;
}
/**
* Returns the probabilities for each label
* for each example row wise
*
* @param examples the examples to classify (one example in each row)
* @return the likelihoods of each example and each label
*/
@Override
public INDArray labelProbabilities(INDArray examples) {
return output(examples);
}
/**
* Fit the model
*
* @param input the examples to classify (one example in each row)
* @param labels the example labels(a binary outcome matrix)
*/
@Override
public void fit(INDArray input, INDArray labels) {
setInput(input);
setLabels(labels);
applyDropOutIfNecessary(true);
if (solver == null) {
solver = new Solver.Builder().configure(conf()).listeners(getListeners()).model(this).build();
//Set the updater state view array. For MLN and CG, this is done by MultiLayerUpdater and ComputationGraphUpdater respectively
Updater updater = solver.getOptimizer().getUpdater();
int updaterStateSize = (int)conf().getLayer().getIUpdater().stateSize(numParams());
if (updaterStateSize > 0)
updater.setStateViewArray(this, Nd4j.createUninitialized(new int[] {1, updaterStateSize}, Nd4j.order()),
true);
}
solver.optimize();
}
/**
* Fit the model
*
* @param data the data to train on
*/
@Override
public void fit(DataSet data) {
fit(data.getFeatures(), data.getLabels());
}
/**
* Fit the model
*
* @param examples the examples to classify (one example in each row)
* @param labels the labels for each example (the number of labels must match
*/
@Override
public void fit(INDArray examples, int[] labels) {
INDArray outcomeMatrix = FeatureUtil.toOutcomeMatrix(labels, numLabels());
fit(examples, outcomeMatrix);
}
@Override
public void clear() {
super.clear();
if (labels != null) {
labels.data().destroy();
labels = null;
}
solver = null;
}
@Override
public void iterate(INDArray input) {
throw new UnsupportedOperationException("Not supported " + layerId());
}
@Override
public INDArray getLabels() {
return labels;
}
public void setLabels(INDArray labels) {
this.labels = labels;
}
protected INDArray getLabels2d() {
if (labels.rank() > 2) {
return labels.reshape(labels.size(2), labels.size(1));
}
return labels;
}
}