使用 dl4j 和 FCN 网络进行语义分割
Semantic Segmentation using dl4j with FCN Networks
我已经查看了 dl4j 示例并执行了 AnimalsClassification 示例以测试成功。
I have to train, evaluate and predict(use) a semantic segmentation algorithm like UNet, because input images are not the same size FCN is required.
and changed the network of AnimalsClassification example to UNet from this link
但是出现错误。
你能帮我解决这个错误吗？
错误：
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
19:22:39,951 INFO ~ Load data....
19:22:40,696 INFO ~ Build model....
19:22:41,363 INFO ~ Loaded [CpuBackend] backend
19:22:44,955 INFO ~ Number of threads used for NativeOps: 2
19:22:45,555 INFO ~ Number of threads used for BLAS: 2
19:22:45,562 INFO ~ Backend used: [CPU]; OS: [Linux]
19:22:45,562 INFO ~ Cores: [2]; Memory: [1.3GB];
19:22:45,562 INFO ~ Blas vendor: [OPENBLAS]
19:22:56,425 WARN ~ Layer "Layer not named" distribution is set but will not be applied unless weight init is set to WeighInit.DISTRIBUTION.
Exception in thread "main" java.lang.IllegalStateException: Invalid configuration: network has no inputs. Use .addInputs(String...) to label (and give an ordering to) the network inputs
at org.deeplearning4j.nn.conf.ComputationGraphConfiguration.validate(ComputationGraphConfiguration.java:279)
at org.deeplearning4j.nn.conf.ComputationGraphConfiguration$GraphBuilder.build(ComputationGraphConfiguration.java:918)
at org.deeplearning4j.examples.convolution.AnimalsClassification.graphBuilder(AnimalsClassification.java:443)
at org.deeplearning4j.examples.convolution.AnimalsClassification.run(AnimalsClassification.java:145)
at org.deeplearning4j.examples.convolution.AnimalsClassification.main(AnimalsClassification.java:447)
Process finished with exit code 1
我修改的代码是：
package org.deeplearning4j.examples.convolution;
import lombok.Builder;
import org.apache.commons.io.FilenameUtils;
import org.datavec.api.io.filters.BalancedPathFilter;
import org.datavec.api.io.labels.ParentPathLabelGenerator;
import org.datavec.api.split.FileSplit;
import org.datavec.api.split.InputSplit;
import org.datavec.image.loader.NativeImageLoader;
import org.datavec.image.recordreader.ImageRecordReader;
import org.deeplearning4j.api.storage.StatsStorage;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.datasets.iterator.MultipleEpochsIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.distribution.GaussianDistribution;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.ui.api.UIServer;
import org.deeplearning4j.ui.stats.StatsListener;
import org.deeplearning4j.ui.storage.InMemoryStatsStorage;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.learning.config.AdaDelta;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.schedule.ScheduleType;
import org.nd4j.linalg.schedule.StepSchedule;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.File;
import java.util.List;
import java.util.Random;
import static java.lang.Math.toIntExact;
/**
* Animal Classification
*
* Example classification of photos from 4 different animals (bear, duck, deer, turtle).
*
* References:
* - U.S. Fish and Wildlife Service (animal sample dataset): http://digitalmedia.fws.gov/cdm/
* - Tiny ImageNet Classification with CNN: http://cs231n.stanford.edu/reports/2015/pdfs/leonyao_final.pdf
*
* CHALLENGE: Current setup gets low score results. Can you improve the scores? Some approaches:
* - Add additional images to the dataset
* - Apply more transforms to dataset
* - Increase epochs
* - Try different model configurations
* - Tune by adjusting learning rate, updaters, activation & loss functions, regularization, ...
*/
public class AnimalsClassification {
protected static final Logger log = LoggerFactory.getLogger(AnimalsClassification.class);
protected static int height = 100;
protected static int width = 100;
protected static int channels = 3;
protected static int batchSize = 20;
// protected static long seed = 42;
private static long seed = 1234;
protected static Random rng = new Random(seed);
protected static int epochs = 50;
protected static double splitTrainTest = 0.8;
protected static boolean save = false;
protected static String modelType = "AlexNet"; // LeNet, AlexNet or Custom but you need to fill it out
private int numLabels;
public void run(String[] args) throws Exception {
log.info("Load data....");
/**cd
* Data Setup -> organize and limit data file paths:
* - mainPath = path to image files
* - fileSplit = define basic dataset split with limits on format
* - pathFilter = define additional file load filter to limit size and balance batch content
**/
ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
File mainPath = new File(System.getProperty("user.dir"), "dl4j-examples/src/main/resources/animals/");
FileSplit fileSplit = new FileSplit(mainPath, NativeImageLoader.ALLOWED_FORMATS, rng);
int numExamples = toIntExact(fileSplit.length());
numLabels = fileSplit.getRootDir().listFiles(File::isDirectory).length; //This only works if your root is clean: only label subdirs.
BalancedPathFilter pathFilter = new BalancedPathFilter(rng, labelMaker, numExamples, numLabels, batchSize);
/**
* Data Setup -> train test split
* - inputSplit = define train and test split
**/
InputSplit[] inputSplit = fileSplit.sample(pathFilter, splitTrainTest, 1 - splitTrainTest);
InputSplit trainData = inputSplit[0];
InputSplit testData = inputSplit[1];
/**
* Data Setup -> transformation
* - Transform = how to tranform images and generate large dataset to train on
**/
// ImageTransform flipTransform1 = new FlipImageTransform(rng);
// ImageTransform flipTransform2 = new FlipImageTransform(new Random(123));
// ImageTransform warpTransform = new WarpImageTransform(rng, 42);
// ImageTransform colorTransform = new ColorConversionTransform(new Random(seed), COLOR_BGR2YCrCb);
// List<ImageTransform> transforms = Arrays.asList(new ImageTransform[]{flipTransform1, warpTransform, flipTransform2});
/**
* Data Setup -> normalization
* - how to normalize images and generate large dataset to train on
**/
DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
log.info("Build model....");
// Uncomment below to try AlexNet. Note change height and width to at least 100
// MultiLayerNetwork network = new AlexNet(height, width, channels, numLabels, seed, iterations).init();
// MultiLayerNetwork network;
// switch (modelType) {
// case "LeNet":
// network = lenetModel();
// break;
// case "AlexNet":
// network = alexnetModel();
// break;
// case "custom":
// network = customModel();
// break;
// default:
// throw new InvalidInputTypeException("Incorrect model provided.");
// }
ComputationGraph network = new ComputationGraph(graphBuilder());
network.init();
// network.setListeners(new ScoreIterationListener(listenerFreq));
UIServer uiServer = UIServer.getInstance();
StatsStorage statsStorage = new InMemoryStatsStorage();
uiServer.attach(statsStorage);
network.setListeners(new StatsListener( statsStorage),new ScoreIterationListener(1));
/**
* Data Setup -> define how to load data into net:
* - recordReader = the reader that loads and converts image data pass in inputSplit to initialize
* - dataIter = a generator that only loads one batch at a time into memory to save memory
* - trainIter = uses MultipleEpochsIterator to ensure model runs through the data for all epochs
**/
ImageRecordReader recordReader = new ImageRecordReader(height, width, channels, labelMaker);
DataSetIterator dataIter;
MultipleEpochsIterator trainIter;
log.info("Train model....");
// Train without transformations
recordReader.initialize(trainData, null);
dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
trainIter = new MultipleEpochsIterator(epochs, dataIter);
network.fit(trainIter);
// Train with transformations
/* for (ImageTransform transform : transforms) {
System.out.print("\nTraining on transformation: " + transform.getClass().toString() + "\n\n");
recordReader.initialize(trainData, transform);
dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
trainIter = new MultipleEpochsIterator(epochs, dataIter);
network.fit(trainIter);
}*/
log.info("Evaluate model....");
recordReader.initialize(testData);
dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
Evaluation eval = network.evaluate(dataIter);
log.info(eval.stats(true));
// Example on how to get predict results with trained model. Result for first example in minibatch is printed
dataIter.reset();
DataSet testDataSet = dataIter.next();
List<String> allClassLabels = recordReader.getLabels();
int labelIndex = testDataSet.getLabels().argMax(1).getInt(0);
// int[] predictedClasses = network.predict(testDataSet.getFeatures());
String expectedResult = allClassLabels.get(labelIndex);
// String modelPrediction = allClassLabels.get(predictedClasses[0]);
// System.out.print("\nFor a single example that is labeled " + expectedResult + " the model predicted " + modelPrediction + "\n\n");
if (save) {
log.info("Save model....");
String basePath = FilenameUtils.concat(System.getProperty("user.dir"), "src/main/resources/");
ModelSerializer.writeModel(network, basePath + "model.bin", true);
}
log.info("****************Example finished********************");
}
private ConvolutionLayer convInit(String name, int in, int out, int[] kernel, int[] stride, int[] pad, double bias) {
return new ConvolutionLayer.Builder(kernel, stride, pad).name(name).nIn(in).nOut(out).biasInit(bias).build();
}
private ConvolutionLayer conv3x3(String name, int out, double bias) {
return new ConvolutionLayer.Builder(new int[]{3,3}, new int[] {1,1}, new int[] {1,1}).name(name).nOut(out).biasInit(bias).build();
}
private ConvolutionLayer conv5x5(String name, int out, int[] stride, int[] pad, double bias) {
return new ConvolutionLayer.Builder(new int[]{5,5}, stride, pad).name(name).nOut(out).biasInit(bias).build();
}
private SubsamplingLayer maxPool(String name, int[] kernel) {
return new SubsamplingLayer.Builder(kernel, new int[]{2,2}).name(name).build();
}
private DenseLayer fullyConnected(String name, int out, double bias, double dropOut, Distribution dist) {
return new DenseLayer.Builder().name(name).nOut(out).biasInit(bias).dropOut(dropOut).dist(dist).build();
}
public MultiLayerNetwork lenetModel() {
/**
* Revisde Lenet Model approach developed by ramgo2 achieves slightly above random
* Reference: https://gist.github.com/ramgo2/833f12e92359a2da9e5c2fb6333351c5
**/
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.l2(0.005)
.activation(Activation.RELU)
.weightInit(WeightInit.XAVIER)
.updater(new Nesterovs(0.0001,0.9))
.list()
.layer(0, convInit("cnn1", channels, 50 , new int[]{5, 5}, new int[]{1, 1}, new int[]{0, 0}, 0))
.layer(1, maxPool("maxpool1", new int[]{2,2}))
.layer(2, conv5x5("cnn2", 100, new int[]{5, 5}, new int[]{1, 1}, 0))
.layer(3, maxPool("maxool2", new int[]{2,2}))
.layer(4, new DenseLayer.Builder().nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(numLabels)
.activation(Activation.SOFTMAX)
.build())
.backprop(true).pretrain(false)
.setInputType(InputType.convolutional(height, width, channels))
.build();
return new MultiLayerNetwork(conf);
}
public MultiLayerNetwork alexnetModel() {
/**
* AlexNet model interpretation based on the original paper ImageNet Classification with Deep Convolutional Neural Networks
* and the imagenetExample code referenced.
* http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
**/
double nonZeroBias = 1;
double dropOut = 0.5;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0.0, 0.01))
.activation(Activation.RELU)
.updater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 1e-2, 0.1, 100000), 0.9))
.biasUpdater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 2e-2, 0.1, 100000), 0.9))
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) // normalize to prevent vanishing or exploding gradients
.l2(5 * 1e-4)
.list()
.layer(0, convInit("cnn1", channels, 96, new int[]{11, 11}, new int[]{4, 4}, new int[]{3, 3}, 0))
.layer(1, new LocalResponseNormalization.Builder().name("lrn1").build())
.layer(2, maxPool("maxpool1", new int[]{3,3}))
.layer(3, conv5x5("cnn2", 256, new int[] {1,1}, new int[] {2,2}, nonZeroBias))
.layer(4, new LocalResponseNormalization.Builder().name("lrn2").build())
.layer(5, maxPool("maxpool2", new int[]{3,3}))
.layer(6,conv3x3("cnn3", 384, 0))
.layer(7,conv3x3("cnn4", 384, nonZeroBias))
.layer(8,conv3x3("cnn5", 256, nonZeroBias))
.layer(9, maxPool("maxpool3", new int[]{3,3}))
.layer(10, fullyConnected("ffn1", 4096, nonZeroBias, dropOut, new GaussianDistribution(0, 0.005)))
.layer(11, fullyConnected("ffn2", 4096, nonZeroBias, dropOut, new GaussianDistribution(0, 0.005)))
.layer(12, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.name("output")
.nOut(numLabels)
.activation(Activation.SOFTMAX)
.build())
.backprop(true)
.pretrain(false)
.setInputType(InputType.convolutional(height, width, channels))
.build();
return new MultiLayerNetwork(conf);
}
public static MultiLayerNetwork customModel() {
/**
* Use this method to build your own custom model.
**/
return null;
}
@Builder.Default private int[] inputShape = new int[] {3, 512, 512};
@Builder.Default private int numClasses = 0;
@Builder.Default private WeightInit weightInit = WeightInit.RELU;
@Builder.Default private IUpdater updater = new AdaDelta();
@Builder.Default private CacheMode cacheMode = CacheMode.NONE;
// @Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED;
@Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.SINGLE;
@Builder.Default private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
public ComputationGraphConfiguration graphBuilder() {
ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(updater)
.weightInit(weightInit)
.dist(new TruncatedNormalDistribution(0.0, 0.5))
.l2(5e-5)
.miniBatch(true)
.cacheMode(cacheMode)
.trainingWorkspaceMode(workspaceMode)
.inferenceWorkspaceMode(workspaceMode)
.graphBuilder();
graph
.addLayer("conv1-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "input")
.addLayer("conv1-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv1-1")
.addLayer("pool1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv1-2")
.addLayer("conv2-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool1")
.addLayer("conv2-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv2-1")
.addLayer("pool2", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv2-2")
.addLayer("conv3-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool2")
.addLayer("conv3-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv3-1")
.addLayer("pool3", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv3-2")
.addLayer("conv4-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool3")
.addLayer("conv4-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv4-1")
.addLayer("drop4", new DropoutLayer.Builder(0.5).build(), "conv4-2")
.addLayer("pool4", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "drop4")
.addLayer("conv5-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool4")
.addLayer("conv5-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv5-1")
.addLayer("drop5", new DropoutLayer.Builder(0.5).build(), "conv5-2")
// up6
.addLayer("up6-1", new Upsampling2D.Builder(2).build(), "drop5")
.addLayer("up6-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up6-1")
.addVertex("merge6", new MergeVertex(), "drop4", "up6-2")
.addLayer("conv6-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge6")
.addLayer("conv6-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv6-1")
// up7
.addLayer("up7-1", new Upsampling2D.Builder(2).build(), "conv6-2")
.addLayer("up7-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up7-1")
.addVertex("merge7", new MergeVertex(), "conv3-2", "up7-2")
.addLayer("conv7-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge7")
.addLayer("conv7-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv7-1")
// up8
.addLayer("up8-1", new Upsampling2D.Builder(2).build(), "conv7-2")
.addLayer("up8-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up8-1")
.addVertex("merge8", new MergeVertex(), "conv2-2", "up8-2")
.addLayer("conv8-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge8")
.addLayer("conv8-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv8-1")
// up9
.addLayer("up9-1", new Upsampling2D.Builder(2).build(), "conv8-2")
.addLayer("up9-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up8-1")
.addVertex("merge9", new MergeVertex(), "conv1-2", "up9-2")
.addLayer("conv9-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge9")
.addLayer("conv9-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv9-1")
.addLayer("conv9-3", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(2)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv9-2")
.addLayer("conv10", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1)
.convolutionMode(ConvolutionMode.Truncate).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.SIGMOID).build(), "conv9-3")
.addLayer("output", new CnnLossLayer.Builder(LossFunctions.LossFunction.MCXENT).build(), "conv10")
.setOutputs("output").backprop(true).pretrain(false);
return graph.build();
}
public static void main(String[] args) throws Exception {
new AnimalsClassification().run(args);
}
}
非常感谢。
它说明了堆栈跟踪中的正确错误。您需要在图表上调用 setInputs。这类似于我们在底部设置输出的方式。
请仔细阅读错误信息。
我已经查看了 dl4j 示例并执行了 AnimalsClassification 示例以测试成功。
I have to train, evaluate and predict(use) a semantic segmentation algorithm like UNet, because input images are not the same size FCN is required.
and changed the network of AnimalsClassification example to UNet from this link
但是出现错误。 你能帮我解决这个错误吗？
错误：
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
19:22:39,951 INFO ~ Load data....
19:22:40,696 INFO ~ Build model....
19:22:41,363 INFO ~ Loaded [CpuBackend] backend
19:22:44,955 INFO ~ Number of threads used for NativeOps: 2
19:22:45,555 INFO ~ Number of threads used for BLAS: 2
19:22:45,562 INFO ~ Backend used: [CPU]; OS: [Linux]
19:22:45,562 INFO ~ Cores: [2]; Memory: [1.3GB];
19:22:45,562 INFO ~ Blas vendor: [OPENBLAS]
19:22:56,425 WARN ~ Layer "Layer not named" distribution is set but will not be applied unless weight init is set to WeighInit.DISTRIBUTION.
Exception in thread "main" java.lang.IllegalStateException: Invalid configuration: network has no inputs. Use .addInputs(String...) to label (and give an ordering to) the network inputs
at org.deeplearning4j.nn.conf.ComputationGraphConfiguration.validate(ComputationGraphConfiguration.java:279)
at org.deeplearning4j.nn.conf.ComputationGraphConfiguration$GraphBuilder.build(ComputationGraphConfiguration.java:918)
at org.deeplearning4j.examples.convolution.AnimalsClassification.graphBuilder(AnimalsClassification.java:443)
at org.deeplearning4j.examples.convolution.AnimalsClassification.run(AnimalsClassification.java:145)
at org.deeplearning4j.examples.convolution.AnimalsClassification.main(AnimalsClassification.java:447)
Process finished with exit code 1
我修改的代码是：
package org.deeplearning4j.examples.convolution;
import lombok.Builder;
import org.apache.commons.io.FilenameUtils;
import org.datavec.api.io.filters.BalancedPathFilter;
import org.datavec.api.io.labels.ParentPathLabelGenerator;
import org.datavec.api.split.FileSplit;
import org.datavec.api.split.InputSplit;
import org.datavec.image.loader.NativeImageLoader;
import org.datavec.image.recordreader.ImageRecordReader;
import org.deeplearning4j.api.storage.StatsStorage;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.datasets.iterator.MultipleEpochsIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.distribution.GaussianDistribution;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.ui.api.UIServer;
import org.deeplearning4j.ui.stats.StatsListener;
import org.deeplearning4j.ui.storage.InMemoryStatsStorage;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.learning.config.AdaDelta;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.schedule.ScheduleType;
import org.nd4j.linalg.schedule.StepSchedule;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.File;
import java.util.List;
import java.util.Random;
import static java.lang.Math.toIntExact;
/**
* Animal Classification
*
* Example classification of photos from 4 different animals (bear, duck, deer, turtle).
*
* References:
* - U.S. Fish and Wildlife Service (animal sample dataset): http://digitalmedia.fws.gov/cdm/
* - Tiny ImageNet Classification with CNN: http://cs231n.stanford.edu/reports/2015/pdfs/leonyao_final.pdf
*
* CHALLENGE: Current setup gets low score results. Can you improve the scores? Some approaches:
* - Add additional images to the dataset
* - Apply more transforms to dataset
* - Increase epochs
* - Try different model configurations
* - Tune by adjusting learning rate, updaters, activation & loss functions, regularization, ...
*/
public class AnimalsClassification {
protected static final Logger log = LoggerFactory.getLogger(AnimalsClassification.class);
protected static int height = 100;
protected static int width = 100;
protected static int channels = 3;
protected static int batchSize = 20;
// protected static long seed = 42;
private static long seed = 1234;
protected static Random rng = new Random(seed);
protected static int epochs = 50;
protected static double splitTrainTest = 0.8;
protected static boolean save = false;
protected static String modelType = "AlexNet"; // LeNet, AlexNet or Custom but you need to fill it out
private int numLabels;
public void run(String[] args) throws Exception {
log.info("Load data....");
/**cd
* Data Setup -> organize and limit data file paths:
* - mainPath = path to image files
* - fileSplit = define basic dataset split with limits on format
* - pathFilter = define additional file load filter to limit size and balance batch content
**/
ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
File mainPath = new File(System.getProperty("user.dir"), "dl4j-examples/src/main/resources/animals/");
FileSplit fileSplit = new FileSplit(mainPath, NativeImageLoader.ALLOWED_FORMATS, rng);
int numExamples = toIntExact(fileSplit.length());
numLabels = fileSplit.getRootDir().listFiles(File::isDirectory).length; //This only works if your root is clean: only label subdirs.
BalancedPathFilter pathFilter = new BalancedPathFilter(rng, labelMaker, numExamples, numLabels, batchSize);
/**
* Data Setup -> train test split
* - inputSplit = define train and test split
**/
InputSplit[] inputSplit = fileSplit.sample(pathFilter, splitTrainTest, 1 - splitTrainTest);
InputSplit trainData = inputSplit[0];
InputSplit testData = inputSplit[1];
/**
* Data Setup -> transformation
* - Transform = how to tranform images and generate large dataset to train on
**/
// ImageTransform flipTransform1 = new FlipImageTransform(rng);
// ImageTransform flipTransform2 = new FlipImageTransform(new Random(123));
// ImageTransform warpTransform = new WarpImageTransform(rng, 42);
// ImageTransform colorTransform = new ColorConversionTransform(new Random(seed), COLOR_BGR2YCrCb);
// List<ImageTransform> transforms = Arrays.asList(new ImageTransform[]{flipTransform1, warpTransform, flipTransform2});
/**
* Data Setup -> normalization
* - how to normalize images and generate large dataset to train on
**/
DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
log.info("Build model....");
// Uncomment below to try AlexNet. Note change height and width to at least 100
// MultiLayerNetwork network = new AlexNet(height, width, channels, numLabels, seed, iterations).init();
// MultiLayerNetwork network;
// switch (modelType) {
// case "LeNet":
// network = lenetModel();
// break;
// case "AlexNet":
// network = alexnetModel();
// break;
// case "custom":
// network = customModel();
// break;
// default:
// throw new InvalidInputTypeException("Incorrect model provided.");
// }
ComputationGraph network = new ComputationGraph(graphBuilder());
network.init();
// network.setListeners(new ScoreIterationListener(listenerFreq));
UIServer uiServer = UIServer.getInstance();
StatsStorage statsStorage = new InMemoryStatsStorage();
uiServer.attach(statsStorage);
network.setListeners(new StatsListener( statsStorage),new ScoreIterationListener(1));
/**
* Data Setup -> define how to load data into net:
* - recordReader = the reader that loads and converts image data pass in inputSplit to initialize
* - dataIter = a generator that only loads one batch at a time into memory to save memory
* - trainIter = uses MultipleEpochsIterator to ensure model runs through the data for all epochs
**/
ImageRecordReader recordReader = new ImageRecordReader(height, width, channels, labelMaker);
DataSetIterator dataIter;
MultipleEpochsIterator trainIter;
log.info("Train model....");
// Train without transformations
recordReader.initialize(trainData, null);
dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
trainIter = new MultipleEpochsIterator(epochs, dataIter);
network.fit(trainIter);
// Train with transformations
/* for (ImageTransform transform : transforms) {
System.out.print("\nTraining on transformation: " + transform.getClass().toString() + "\n\n");
recordReader.initialize(trainData, transform);
dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
trainIter = new MultipleEpochsIterator(epochs, dataIter);
network.fit(trainIter);
}*/
log.info("Evaluate model....");
recordReader.initialize(testData);
dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, numLabels);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
Evaluation eval = network.evaluate(dataIter);
log.info(eval.stats(true));
// Example on how to get predict results with trained model. Result for first example in minibatch is printed
dataIter.reset();
DataSet testDataSet = dataIter.next();
List<String> allClassLabels = recordReader.getLabels();
int labelIndex = testDataSet.getLabels().argMax(1).getInt(0);
// int[] predictedClasses = network.predict(testDataSet.getFeatures());
String expectedResult = allClassLabels.get(labelIndex);
// String modelPrediction = allClassLabels.get(predictedClasses[0]);
// System.out.print("\nFor a single example that is labeled " + expectedResult + " the model predicted " + modelPrediction + "\n\n");
if (save) {
log.info("Save model....");
String basePath = FilenameUtils.concat(System.getProperty("user.dir"), "src/main/resources/");
ModelSerializer.writeModel(network, basePath + "model.bin", true);
}
log.info("****************Example finished********************");
}
private ConvolutionLayer convInit(String name, int in, int out, int[] kernel, int[] stride, int[] pad, double bias) {
return new ConvolutionLayer.Builder(kernel, stride, pad).name(name).nIn(in).nOut(out).biasInit(bias).build();
}
private ConvolutionLayer conv3x3(String name, int out, double bias) {
return new ConvolutionLayer.Builder(new int[]{3,3}, new int[] {1,1}, new int[] {1,1}).name(name).nOut(out).biasInit(bias).build();
}
private ConvolutionLayer conv5x5(String name, int out, int[] stride, int[] pad, double bias) {
return new ConvolutionLayer.Builder(new int[]{5,5}, stride, pad).name(name).nOut(out).biasInit(bias).build();
}
private SubsamplingLayer maxPool(String name, int[] kernel) {
return new SubsamplingLayer.Builder(kernel, new int[]{2,2}).name(name).build();
}
private DenseLayer fullyConnected(String name, int out, double bias, double dropOut, Distribution dist) {
return new DenseLayer.Builder().name(name).nOut(out).biasInit(bias).dropOut(dropOut).dist(dist).build();
}
public MultiLayerNetwork lenetModel() {
/**
* Revisde Lenet Model approach developed by ramgo2 achieves slightly above random
* Reference: https://gist.github.com/ramgo2/833f12e92359a2da9e5c2fb6333351c5
**/
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.l2(0.005)
.activation(Activation.RELU)
.weightInit(WeightInit.XAVIER)
.updater(new Nesterovs(0.0001,0.9))
.list()
.layer(0, convInit("cnn1", channels, 50 , new int[]{5, 5}, new int[]{1, 1}, new int[]{0, 0}, 0))
.layer(1, maxPool("maxpool1", new int[]{2,2}))
.layer(2, conv5x5("cnn2", 100, new int[]{5, 5}, new int[]{1, 1}, 0))
.layer(3, maxPool("maxool2", new int[]{2,2}))
.layer(4, new DenseLayer.Builder().nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(numLabels)
.activation(Activation.SOFTMAX)
.build())
.backprop(true).pretrain(false)
.setInputType(InputType.convolutional(height, width, channels))
.build();
return new MultiLayerNetwork(conf);
}
public MultiLayerNetwork alexnetModel() {
/**
* AlexNet model interpretation based on the original paper ImageNet Classification with Deep Convolutional Neural Networks
* and the imagenetExample code referenced.
* http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
**/
double nonZeroBias = 1;
double dropOut = 0.5;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0.0, 0.01))
.activation(Activation.RELU)
.updater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 1e-2, 0.1, 100000), 0.9))
.biasUpdater(new Nesterovs(new StepSchedule(ScheduleType.ITERATION, 2e-2, 0.1, 100000), 0.9))
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) // normalize to prevent vanishing or exploding gradients
.l2(5 * 1e-4)
.list()
.layer(0, convInit("cnn1", channels, 96, new int[]{11, 11}, new int[]{4, 4}, new int[]{3, 3}, 0))
.layer(1, new LocalResponseNormalization.Builder().name("lrn1").build())
.layer(2, maxPool("maxpool1", new int[]{3,3}))
.layer(3, conv5x5("cnn2", 256, new int[] {1,1}, new int[] {2,2}, nonZeroBias))
.layer(4, new LocalResponseNormalization.Builder().name("lrn2").build())
.layer(5, maxPool("maxpool2", new int[]{3,3}))
.layer(6,conv3x3("cnn3", 384, 0))
.layer(7,conv3x3("cnn4", 384, nonZeroBias))
.layer(8,conv3x3("cnn5", 256, nonZeroBias))
.layer(9, maxPool("maxpool3", new int[]{3,3}))
.layer(10, fullyConnected("ffn1", 4096, nonZeroBias, dropOut, new GaussianDistribution(0, 0.005)))
.layer(11, fullyConnected("ffn2", 4096, nonZeroBias, dropOut, new GaussianDistribution(0, 0.005)))
.layer(12, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.name("output")
.nOut(numLabels)
.activation(Activation.SOFTMAX)
.build())
.backprop(true)
.pretrain(false)
.setInputType(InputType.convolutional(height, width, channels))
.build();
return new MultiLayerNetwork(conf);
}
public static MultiLayerNetwork customModel() {
/**
* Use this method to build your own custom model.
**/
return null;
}
@Builder.Default private int[] inputShape = new int[] {3, 512, 512};
@Builder.Default private int numClasses = 0;
@Builder.Default private WeightInit weightInit = WeightInit.RELU;
@Builder.Default private IUpdater updater = new AdaDelta();
@Builder.Default private CacheMode cacheMode = CacheMode.NONE;
// @Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED;
@Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.SINGLE;
@Builder.Default private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
public ComputationGraphConfiguration graphBuilder() {
ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(updater)
.weightInit(weightInit)
.dist(new TruncatedNormalDistribution(0.0, 0.5))
.l2(5e-5)
.miniBatch(true)
.cacheMode(cacheMode)
.trainingWorkspaceMode(workspaceMode)
.inferenceWorkspaceMode(workspaceMode)
.graphBuilder();
graph
.addLayer("conv1-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "input")
.addLayer("conv1-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv1-1")
.addLayer("pool1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv1-2")
.addLayer("conv2-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool1")
.addLayer("conv2-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv2-1")
.addLayer("pool2", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv2-2")
.addLayer("conv3-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool2")
.addLayer("conv3-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv3-1")
.addLayer("pool3", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv3-2")
.addLayer("conv4-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool3")
.addLayer("conv4-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv4-1")
.addLayer("drop4", new DropoutLayer.Builder(0.5).build(), "conv4-2")
.addLayer("pool4", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "drop4")
.addLayer("conv5-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool4")
.addLayer("conv5-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv5-1")
.addLayer("drop5", new DropoutLayer.Builder(0.5).build(), "conv5-2")
// up6
.addLayer("up6-1", new Upsampling2D.Builder(2).build(), "drop5")
.addLayer("up6-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up6-1")
.addVertex("merge6", new MergeVertex(), "drop4", "up6-2")
.addLayer("conv6-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge6")
.addLayer("conv6-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv6-1")
// up7
.addLayer("up7-1", new Upsampling2D.Builder(2).build(), "conv6-2")
.addLayer("up7-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up7-1")
.addVertex("merge7", new MergeVertex(), "conv3-2", "up7-2")
.addLayer("conv7-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge7")
.addLayer("conv7-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv7-1")
// up8
.addLayer("up8-1", new Upsampling2D.Builder(2).build(), "conv7-2")
.addLayer("up8-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up8-1")
.addVertex("merge8", new MergeVertex(), "conv2-2", "up8-2")
.addLayer("conv8-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge8")
.addLayer("conv8-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv8-1")
// up9
.addLayer("up9-1", new Upsampling2D.Builder(2).build(), "conv8-2")
.addLayer("up9-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up8-1")
.addVertex("merge9", new MergeVertex(), "conv1-2", "up9-2")
.addLayer("conv9-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge9")
.addLayer("conv9-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv9-1")
.addLayer("conv9-3", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(2)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv9-2")
.addLayer("conv10", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1)
.convolutionMode(ConvolutionMode.Truncate).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.SIGMOID).build(), "conv9-3")
.addLayer("output", new CnnLossLayer.Builder(LossFunctions.LossFunction.MCXENT).build(), "conv10")
.setOutputs("output").backprop(true).pretrain(false);
return graph.build();
}
public static void main(String[] args) throws Exception {
new AnimalsClassification().run(args);
}
}
非常感谢。
它说明了堆栈跟踪中的正确错误。您需要在图表上调用 setInputs。这类似于我们在底部设置输出的方式。 请仔细阅读错误信息。