C# 用TensorFlow.NET训练自己的物品分类模型的完整代码例程

以下是一个简单的 C# 示例，使用 TensorFlow.NET 库来训练和测试一个物品分类模型：

using System;
using System.IO;
using Tensorflow;
using Tensorflow.Models.ObjectDetection;
using static Tensorflow.Binding;

namespace ObjectDetectionExample
{
    class Program
    {
        static void Main(string[] args)
        {
            // 设置训练和测试数据集的路径
            var trainImagePath = "path/to/train/images";
            var trainAnnotationPath = "path/to/train/annotations";
            var testImagePath = "path/to/test/images";
            var testAnnotationPath = "path/to/test/annotations";

            // 加载训练和测试数据集
            var trainDataset = new ObjectDetectionDataset(trainImagePath, trainAnnotationPath);
            var testDataset = new ObjectDetectionDataset(testImagePath, testAnnotationPath);

            // 初始化模型
            var model = new ObjectDetectionModel();

            // 配置训练器
            var optimizer = keras.optimizers.Adam(learning_rate: 0.001f);
            var loss_fn = keras.losses.BinaryCrossentropy(from_logits: true);
            var train_loss_metric = keras.metrics.Mean(name: "train_loss");
            var train_accuracy_metric = keras.metrics.BinaryAccuracy(name: "train_accuracy");

            // 训练模型
            var epochs = 10;
            var batch_size = 32;
            var steps_per_epoch = trainDataset.Length / batch_size;
            var validation_steps = testDataset.Length / batch_size;

            for (int epoch = 0; epoch < epochs; epoch++)
            {
                Console.WriteLine($"Epoch {epoch + 1}/{epochs}");

                foreach (var batch in trainDataset.GetBatches(batch_size))
                {
                    var images = batch.Item1;
                    var labels = batch.Item2;

                    using (var tape = tf.GradientTape())
                    {
                        var logits = model(images, training: true);
                        var loss_value = loss_fn(labels, logits);
                        train_loss_metric.update_state(loss_value);
                        train_accuracy_metric.update_state(labels, logits);

                        var grads = tape.gradient(loss_value, model.trainable_weights);
                        optimizer.apply_gradients(zip(grads, model.trainable_weights));
                    }
                }

                Console.WriteLine($"Train loss: {train_loss_metric.result()}, Train accuracy: {train_accuracy_metric.result()}");

                train_loss_metric.reset_states();
                train_accuracy_metric.reset_states();

                foreach (var batch in testDataset.GetBatches(batch_size))
                {
                    var images = batch.Item1;
                    var labels = batch.Item2;

                    var logits = model(images, training: false);
                    var loss_value = loss_fn(labels, logits);
                    train_loss_metric.update_state(loss_value);
                    train_accuracy_metric.update_state(labels, logits);
                }

                Console.WriteLine($"Test loss: {train_loss_metric.result()}, Test accuracy: {train_accuracy_metric.result()}");

                train_loss_metric.reset_states();
                train_accuracy_metric.reset_states();
            }

            // 保存模型
            var savePath = "path/to/save/model";
            model.Save(savePath, save_format: "tf");
        }
    }
}

这个示例使用 TensorFlow.NET 库中的 `ObjectDetectionDataset` 类来加载训练和测试数据集。模型使用了 `ObjectDetectionModel` 类，这个类是一个简单的物品分类模型。在训练过程中，这个示例使用了 Adam 优化器和二元交叉熵损失函数，并统计了训练和测试的准确率和损失值。

在完成训练后，模型可以使用 `Save` 方法来保存到磁盘上。