C# 用TensorFlow.NET训练自己的模型的完整代码例程

好的，以下是使用TensorFlow.NET训练自己的模型的完整代码例程：

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using TensorFlow;

namespace TensorFlowExample
{
    class Program
    {
        static void Main(string[] args)
        {
            // 定义输入数据的维度
            var inputDim = new TFShape(-1, 28, 28, 1);
            // 定义输出数据的维度
            var outputDim = new TFShape(-1, 10);

            // 构建计算图
            var graph = new TFGraph();
            var input = graph.Placeholder(TFDataType.Float, inputDim, "input");
            var output = graph.Placeholder(TFDataType.Float, outputDim, "output");

            // 定义模型
            var conv1 = graph.Conv2D(input, graph.Const(1.0f, new TFShape(5, 5, 1, 32)), new int[] { 1, 1, 1, 1 }, "SAME", "conv1");
            var pool1 = graph.MaxPool(conv1, new int[] { 1, 2, 2, 1 }, new int[] { 1, 2, 2, 1 }, "SAME", "pool1");
            var conv2 = graph.Conv2D(pool1, graph.Const(1.0f, new TFShape(5, 5, 32, 64)), new int[] { 1, 1, 1, 1 }, "SAME", "conv2");
            var pool2 = graph.MaxPool(conv2, new int[] { 1, 2, 2, 1 }, new int[] { 1, 2, 2, 1 }, "SAME", "pool2");
            var flatten = graph.Reshape(pool2, new TFShape(-1, 7 * 7 * 64), "flatten");
            var fc1 = graph.FullyConnected(flatten, graph.Const(1.0f, new TFShape(7 * 7 * 64, 1024)), "fc1");
            var relu1 = graph.Relu(fc1, "relu1");
            var fc2 = graph.FullyConnected(relu1, graph.Const(1.0f, new TFShape(1024, 10)), "fc2");
            var outputSoftmax = graph.Softmax(fc2, "outputSoftmax");

            // 定义损失函数
            var loss = graph.ReduceMean(graph.SoftmaxCrossEntropyWithLogits(output, outputSoftmax), new TFOutput(0), "loss");

            // 定义优化器
            var optimizer = graph.GradientDescentOptimizer(learningRate: 0.01f);
            var trainOperation = optimizer.Minimize(loss);

            // 创建会话
            var session = new TFSession(graph);

            // 准备数据集
            var mnist = new MnistData();
            var trainData = mnist.GetTrainData().Take(1000).ToList();
            var testData = mnist.GetTestData().Take(100).ToList();

            // 定义训练迭代次数
            var iterations = 100;

            // 开始训练
            for (int i = 0; i < iterations; i++)
            {
                var lossValue = 0.0f;
                foreach (var batch in trainData.Batch(100))
                {
                    var inputs = batch.Select(x => x.Image).ToArray();
                    var outputs = batch.Select(x => x.Label).ToArray();

                    var runner = session.GetRunner();
                    runner.AddInput(input, inputs);
                    runner.AddInput(output, outputs);
                    runner.AddTarget(trainOperation);

                    var result = runner.Run();
                    lossValue += result.FirstOrDefault().GetValue().ToScalar<float>();
                }
                Console.WriteLine($"Iteration {i}, Loss = {lossValue / trainData.Count}");

                // 在测试集上测试模型的准确率
                var correct = 0;
                foreach (var data in testData)
                {
                    var inputs = new float[1, 28, 28, 1];
                    for (int j = 0; j < 28; j++)
                    {
                        for (int k = 0; k < 28; k++)
                        {
                            inputs[0, j, k, 0] = data.Image[j * 28 + k];
                        }
                    }

                    var runner = session.GetRunner();
                    runner.AddInput(input, inputs);

                    var result = runner.Run(outputSoftmax);
                    var outputData = (float[,])result.FirstOrDefault().GetValue();
                    var label = outputData.ArgMax(1)[0];
                    if (label == data.Label)
                    {
                        correct++;
                    }
                }
                Console.WriteLine($"Iteration {i}, Test Accuracy = {correct * 1.0 / testData.Count}");
            }

            // 保存模型
            var modelDirectory = "model";
            if (!System.IO.Directory.Exists(modelDirectory))
            {
                System.IO.Directory.CreateDirectory(modelDirectory);
            }
            var saver = session.GetSaver();
            saver.Save(session, $"{modelDirectory}/model.ckpt");

            Console.ReadKey();
        }
    }
}

这是一个使用TensorFlow.NET训练手写数字识别模型的完整例程，包括模型的定义、损失函数、优化器、训练过程、测试准确率和模型的保存等步骤。需要注意的是，本例程中使用的是MNIST数据集，如果要使用其他数据集需要对代码进行适当修改。