jgao1025 · April 10, 2016 03:31 · Apr 10, 2016
diff --git a/code b/code
@@ -0,0 +1,101 @@
+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********************");
+    }
+}
diff --git a/expection b/expection
@@ -0,0 +1,24 @@
+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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