public Model(int numClasses)
{
features = Sequential(
Conv2D(3, 64, kernelSize: 3, stride: 2, padding: 1),
Relu(inPlace: true),
MaxPool2D(kernelSize: new long[] { 2 }),
Conv2D(64, 192, kernelSize: 3, padding: 1),
Relu(inPlace: true),
MaxPool2D(kernelSize: new long[] { 2 }),
Conv2D(192, 384, kernelSize: 3, padding: 1),
Relu(inPlace: true),
Conv2D(384, 256, kernelSize: 3, padding: 1),
Relu(inPlace: true),
Conv2D(256, 256, kernelSize: 3, padding: 1),
Relu(inPlace: true),
MaxPool2D(kernelSize: new long[] { 2 }));
avgPool = AdaptiveAvgPool2D(2, 2);
classifier = Sequential(
Dropout(IsTraining()),
Linear(256 * 2 * 2, 4096),
Relu(inPlace: true),
Dropout(IsTraining()),
Linear(4096, 4096),
Relu(inPlace: true),
Linear(4096, numClasses)
);
RegisterModule(features);
RegisterModule(classifier);
}
private static void Train(
NN.Module model,
NN.Optimizer optimizer,
Loss loss,
IEnumerable <(TorchTensor, TorchTensor)> dataLoader,
