public static void Run()
{
var mnist_train = new FashionMNIST(train: true);
var(x, y) = mnist_train[0];
Console.WriteLine($"X shape: {x.Shape}, X dtype: {x.DataType}, Y shape: {y.Shape}, Y dtype: {y.DataType}");
var transformer = new Compose(
new ToTensor(),
new Normalize(new MxNet.Tuple <float>(0.13f, 0.31f))
);
var train = mnist_train.TransformFirst(transformer);
int batch_size = 256;
var train_data = new DataLoader(train, batch_size: batch_size, shuffle: true);
foreach (var(data, label) in train_data)
{
Console.WriteLine(data.Shape + ", " + label.Shape);
break;
}
var mnist_valid = new FashionMNIST(train: false);
var valid_data = new DataLoader(mnist_valid, batch_size: batch_size, shuffle: true);
var net = new Sequential();
net.Add(new Conv2D(channels: 6, kernel_size: (5, 5), activation: ActivationType.Relu),
new MaxPool2D(pool_size: (2, 2), strides: (2, 2)),
new Conv2D(channels: 16, kernel_size: (3, 3), activation: ActivationType.Relu),
new MaxPool2D(pool_size: (2, 2), strides: (2, 2)),
new Flatten(),
new Dense(120, activation: ActivationType.Relu),
new Dense(84, activation: ActivationType.Relu),
new Dense(10));
net.Initialize(new Xavier());
var softmax_cross_entropy = new SoftmaxCrossEntropyLoss();
var trainer = new Trainer(net.CollectParams(), new SGD(learning_rate: 0.1f));
for (int epoch = 0; epoch < 10; epoch++)
{
var tic = DateTime.Now;
float train_loss = 0;
float train_acc = 0;
float valid_acc = 0;
foreach (var(data, label) in train_data)
{
NDArray loss = null;
NDArray output = null;
// forward + backward
using (Autograd.Record())
{
output = net.Call(data);
loss = softmax_cross_entropy.Call(output, label);
}
loss.Backward();
//update parameters
trainer.Step(batch_size);
//calculate training metrics
train_loss += loss.Mean();
train_acc += Acc(output, label);
}
// calculate validation accuracy
foreach (var(data, label) in valid_data)
{
valid_acc += Acc(net.Call(data), label);
}
Console.WriteLine($"Epoch {epoch}: loss {train_loss / train_data.Length}," +
$" train acc {train_acc / train_data.Length}, " +
$"test acc {train_acc / train_data.Length} " +
$"in {(DateTime.Now - tic).TotalMilliseconds} ms");
}
net.SaveParameters("net.params");
}
