public static void TrainModel()
{
//模型建立部分
Model MNIST_CNN = new Model(new CrossEntropyLoss(), "MNIST_CNN_v1",
new ConvolutionalLayer(new Direct(1), false, new Tuple <int, int>(28, 28), 5, 1, 6, ConvolutionMode.Narrow, new Normal(0, 1)),
new PoolingLayer(3, false, 6, new Tuple <int, int>(24, 24), PoolingMode.Max),
new ConvolutionalLayer(new Direct(1), false, new Tuple <int, int>(8, 8), 5, 6, 16, ConvolutionMode.Narrow, new Normal(0, 1)),
new PoolingLayer(2, true, 16, new Tuple <int, int>(4, 4), PoolingMode.Average),
new FullConnectLayer(new Direct(1), 64, 64, true, new Normal(0, 1)),
new FullConnectLayer(new Sigmoid(), 64, 32, false, new Normal(0, 2)),
new SoftMaxLayer(32, 10, new Normal(0, 2))
);
//声明评估器
MNISTEvaluater evaluater = new MNISTEvaluater(new MNISTDataConverter(new MNISTClassifier()));
//声明分类器
IClassifier classifier = new MNISTClassifier();
//建立环境
Context.Context context = new Context.Context(MNIST_CNN, evaluater, classifier);
//准备数据集
//string trainDataPath = System.IO.Path.Combine(Application.StartupPath, "MNIST", "train-images.idx3-ubyte");
//string trainLabelPath = System.IO.Path.Combine(Application.StartupPath, "MNIST", "train-labels.idx1-ubyte");
//string testDataPath = System.IO.Path.Combine(Application.StartupPath, "MNIST", "t10k-images.idx3-ubyte");
//string testLabelPath = System.IO.Path.Combine(Application.StartupPath, "MNIST", "t10k-labels.idx1-ubyte");
var dataConverter = new MNISTDataConverter(classifier);
var dataView = new MNISTDataView(dataConverter);
//dataView.GetInputData()
context.AddProcess(new Trainer(1000, false, 0, new AdaGradOptimizer(0.5)), 10);
context.AddProcess(new Trainer(600, false, 0, new ExponentialDelayOptimizer(0.2, 0.9, 0.05)), 10);
//context.Train(dataView.trainDataSet,dataView.verifyDataSet,)
}
