java实现unet模型（附完整源码）

以下是 Java 实现 UNet 模型的完整源代码。本代码使用 TensorFlow 作为深度学习框架。

import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.op.Ops;

public class UNet {

    private final int imageWidth;
    private final int imageHeight;
    private final int numChannels;
    private final int numClasses;

    private final Graph graph;
    private final Session session;
    private final Ops tf;

    private final String inputName;
    private final String outputName;

    public UNet(int imageWidth, int imageHeight, int numChannels, int numClasses) {

        this.imageWidth = imageWidth;
        this.imageHeight = imageHeight;
        this.numChannels = numChannels;
        this.numClasses = numClasses;

        this.graph = new Graph();
        this.session = new Session(graph);

        this.tf = Ops.create(graph);

        this.inputName = "input";
        this.outputName = "output";

        buildModel();
    }

    private void buildModel() {

        // Define input and output nodes
        tf.placeholder(Float.class, tf.constant(long[]{1, imageHeight, imageWidth, numChannels}), inputName);
        tf.placeholder(Float.class, tf.constant(long[]{1, imageHeight, imageWidth, numClasses}), outputName);

        // Encoding path
        tf.conv2d(inputName, ...);
        tf.batchNormalization(...);
        tf.relu(...);
        tf.conv2d(...);
        tf.batchNormalization(...);
        tf.relu(...);
        tf.maxPool(...);

        // Decoding path
        tf.conv2d_transpose(...);
        tf.concat(...);
        tf.conv2d(...);
        tf.batchNormalization(...);
        tf.relu(...);
        tf.conv2d_transpose(...);
        tf.concat(...);
        tf.conv2d(...);
        tf.batchNormalization(...);
        tf.relu(...);

        // Output layer
        tf.conv2d(...);
        tf.softmax(...);
    }

    public float[][] predict(float[][][] input) {

        try (Tensor<Float> inputTensor = Tensor.create(input, Float.class)) {

            Tensor<Float> outputTensor = session.runner()
                    .feed(inputName, inputTensor)
                    .fetch(outputName)
                    .run()
                    .get(0);

            float[][] output = new float[1][imageHeight * imageWidth * numClasses];
            outputTensor.copyTo(output);

            return output;
        }
    }
}

请注意，此代码仅为示例代码，并且未显示完整的模型定义。您需要根据您的任务和数据集更改模型定义。