vgg16 java实现
时间: 2023-11-04 16:22:37 浏览: 62
VGG16是一种深度卷积神经网络模型,用于图像分类任务。在Java中实现VGG16需要使用深度学习框架,如TensorFlow或Keras,并使用相应的Java API。
以下是使用DL4J(Deep Learning for Java)框架实现VGG16的示例代码:
```java
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.*;import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.lossfunctions.LossFunctions;
public class VGG16 {
public static MultiLayerConfiguration getConfig() {
int height = 224;
int width = 224;
int channels = 3;
int numClasses = 1000;
return new NeuralNetConfiguration.Builder()
.seed(123)
.weightInit(WeightInit.XAVIER)
.updater(new Nesterovs())
.list()
.layer(0, new ConvolutionLayer.Builder(3, 3)
.nIn(channels)
.nOut(64)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(1, new ConvolutionLayer.Builder(3, 3)
.nOut(64)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(2, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(3, new ConvolutionLayer.Builder(3, 3)
.nOut(128)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(4, new ConvolutionLayer.Builder(3, 3)
.nOut(128)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(5, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(6, new ConvolutionLayer.Builder(3, 3)
.nOut(256)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(7, new ConvolutionLayer.Builder(3, 3)
.nOut(256)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(8, new ConvolutionLayer.Builder(3, 3)
.nOut(256)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(9, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(10, new ConvolutionLayer.Builder(3, 3)
.nOut(512)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(11, new ConvolutionLayer.Builder(3, 3)
.nOut(512)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(12, new ConvolutionLayer.Builder(3, 3)
.nOut(512)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(13, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(14, new ConvolutionLayer.Builder(3, 3)
.nOut(512)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(15, new ConvolutionLayer.Builder(3, 3)
.nOut(512)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(16, new ConvolutionLayer.Builder(3, 3)
.nOut(512)
.stride(1, 1)
.activation(Activation.RELU)
.build())
.layer(17, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(2, 2)
.stride(2, 2)
.build())
.layer(18, new DenseLayer.Builder().activation(Activation.RELU).nOut(4096).build())
.layer(19, new DenseLayer.Builder().activation(Activation.RELU).nOut(4096).build())
.layer(20, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(numClasses)
.activation(Activation.SOFTMAX)
.build())
.setInputType(InputType.convolutional(height, width, channels))
.backprop(true)
.pretrain(false)
.build();
}
}
```
这只是VGG16的一个简化实现示例。你可以根据需要添加更多的层或调整参数。请注意,此示例假设你已经安装了DL4J和相应的依赖项。
希望对你有所帮助!
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