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Created April 10, 2016 03:31
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deeplearning4j exception and code
Raw
code
package org.mydeeplearning4j.pic.detection;
import org.canova.api.records.reader.RecordReader;
import org.canova.api.split.FileSplit;
import org.canova.image.recordreader.ImageRecordReader;
import org.deeplearning4j.datasets.canova.RecordReaderDataSetIterator;
import org.deeplearning4j.datasets.iterator.DataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
import org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.File;
import java.util.ArrayList;
import java.util.List;
/**
* Created by bottles on 3/31/2016.
*/
public class convoltest {
private static final Logger log = LoggerFactory.getLogger(convoltest.class);
public static void main(String[] args) throws Exception {
int nChannels = 1;
int outputNum = 10;
int batchSize = 1000;
int nEpochs = 10;
int iterations = 1;
int seed = 123;
final int numRows = 28;
final int numColumns = 28;
// Set path to the labeled images. in world cup, there is only a 28*28 pixels jpg image.
String labeledPath = "C:\\Users\\Public\\Pictures\\world cup";
//create array of strings called labels
List<String> labels = new ArrayList<>();
//traverse dataset to get each label
for(File f : new File(labeledPath).listFiles()) {
labels.add(f.getName());
}
// Instantiating RecordReader. Specify height and width of images.
RecordReader recordReader = new ImageRecordReader(numRows, numColumns, true, labels);
// Point to data path.
recordReader.initialize(new FileSplit(new File(labeledPath)));
DataSetIterator mnistTrain = new RecordReaderDataSetIterator(recordReader, numRows*numColumns, labels.size());
log.info("Build model....");
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.regularization(true).l2(0.0005)
.learningRate(0.01)
.weightInit(WeightInit.XAVIER)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(Updater.NESTEROVS).momentum(0.9)
.list(4)
.layer(0, new ConvolutionLayer.Builder(5, 5)
.nIn(nChannels)
.stride(1, 1)
.nOut(20).dropOut(0.5)
.activation("relu")
.build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2,2)
.stride(2,2)
.build())
.layer(2, new DenseLayer.Builder().activation("relu")
.nOut(500).build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum)
.activation("softmax")
.build())
.backprop(true).pretrain(false);
new ConvolutionLayerSetup(builder,numRows, numColumns,1);
MultiLayerConfiguration conf = builder.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
log.info("Train model....");
model.setListeners(new ScoreIterationListener(1));
model.fit(mnistTrain); //exception created here
log.info("****************Example finished********************");
}
}
Raw
expection
Exception in thread "main" java.lang.IllegalArgumentException: Shapes do not match: x.shape=[1, 10], y.shape=[1, 3]
at org.nd4j.linalg.api.parallel.tasks.cpu.CPUTaskFactory.getTransformAction(CPUTaskFactory.java:92)
at org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner.doTransformOp(DefaultOpExecutioner.java:409)
at org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner.exec(DefaultOpExecutioner.java:62)
at org.nd4j.linalg.api.ndarray.BaseNDArray.subi(BaseNDArray.java:2660)
at org.nd4j.linalg.api.ndarray.BaseNDArray.subi(BaseNDArray.java:2641)
at org.nd4j.linalg.api.ndarray.BaseNDArray.sub(BaseNDArray.java:2419)
at org.deeplearning4j.nn.layers.BaseOutputLayer.getGradientsAndDelta(BaseOutputLayer.java:154)
at org.deeplearning4j.nn.layers.BaseOutputLayer.backpropGradient(BaseOutputLayer.java:133)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.calcBackpropGradients(MultiLayerNetwork.java:1224)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.backprop(MultiLayerNetwork.java:1178)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.computeGradientAndScore(MultiLayerNetwork.java:1753)
at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:132)
at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:56)
at org.deeplearning4j.optimize.Solver.optimize(Solver.java:52)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.fit(MultiLayerNetwork.java:1168)
at org.mydeeplearning4j.pic.detection.convoltest.main(convoltest.java:101)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:144)
Process finished with exit code 1
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