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我是Deeplearning4j的初学者，并将对Cifar-10图像进行分类测试。我只复制从DL4j例的Alexnet（AnimalsClassification.java）像：如何使用Alexnet对Cifar图像进行分类Deeplearning4j
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0.0, 0.01))
.activation(Activation.RELU)
.updater(Updater.NESTEROVS)
.iterations(iterations)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) // normalize to prevent vanishing or exploding gradients
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(1e-2)
.biasLearningRate(1e-2*2)
.learningRateDecayPolicy(LearningRatePolicy.Step)
.lrPolicyDecayRate(0.1)
.lrPolicySteps(100000)
.regularization(true)
.l2(5 * 1e-4)
.momentum(0.9)
.miniBatch(false)
.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();
当运行它引发了异常代码说，有关于新的INT [] {3-一些问题，“层-9”构，3}，它应该大于0并且小于pHeight + 2 * padH。在java代码中将重量*高度从32 * 32更改为100 * 100时，它运行正常，但我并不希望结果良好。所以我对alexnet上处理32 * 32图像的图层配置有些困惑。