// Constructs a convolutional network with two convolutional layers,
// two fully-connected layers, ReLU units and a softmax on the output.
public static void BuildCnn(IAllocator allocator, SeedSource seedSource, int batchSize, bool useCudnn, out Sequential model, out ICriterion criterion, out bool outputIsClassIndices)
{
var inputWidth = MnistParser.ImageSize;
var inputHeight = MnistParser.ImageSize;
var elementType = DType.Float32;
var inputDims = new long[] { batchSize, 1, inputHeight, inputWidth };
model = new Sequential();
model.Add(new ViewLayer(inputDims));
var outSize = AddCnnLayer(allocator, seedSource, elementType, model, inputDims, 20, useCudnn);
outSize = AddCnnLayer(allocator, seedSource, elementType, model, outSize, 40, useCudnn);
var convOutSize = outSize[1] * outSize[2] * outSize[3];
model.Add(new ViewLayer(batchSize, convOutSize));
var hiddenSize = 1000;
var outputSize = 10;
model.Add(new DropoutLayer(allocator, seedSource, elementType, 0.5f, batchSize, convOutSize));
model.Add(new LinearLayer(allocator, seedSource, elementType, (int)convOutSize, hiddenSize, batchSize));
model.Add(new ReLULayer(allocator, elementType, batchSize, hiddenSize));
model.Add(new DropoutLayer(allocator, seedSource, elementType, 0.5f, batchSize, hiddenSize));
model.Add(new LinearLayer(allocator, seedSource, elementType, hiddenSize, outputSize, batchSize));
model.Add(LayerBuilder.BuildLogSoftMax(allocator, elementType, batchSize, outputSize, useCudnn));
criterion = new ClassNLLCriterion(allocator, batchSize, outputSize);
outputIsClassIndices = true; // output of criterion is class indices
}
// Constructs a network with two fully-connected layers; one sigmoid, one softmax
public static void BuildMLPSoftmax(IAllocator allocator, SeedSource seedSource, int batchSize, bool useCudnn, out Sequential model, out ICriterion criterion, out bool outputIsClassIndices)
{
int inputSize = MnistParser.ImageSize * MnistParser.ImageSize;
int hiddenSize = 100;
int outputSize = MnistParser.LabelCount;
var elementType = DType.Float32;
model = new Sequential();
model.Add(new ViewLayer(batchSize, inputSize));
model.Add(new LinearLayer(allocator, seedSource, elementType, inputSize, hiddenSize, batchSize));
model.Add(new SigmoidLayer(allocator, elementType, batchSize, hiddenSize));
model.Add(new LinearLayer(allocator, seedSource, elementType, hiddenSize, outputSize, batchSize));
model.Add(LayerBuilder.BuildLogSoftMax(allocator, elementType, batchSize, outputSize, useCudnn));
criterion = new ClassNLLCriterion(allocator, batchSize, outputSize);
outputIsClassIndices = true; // output of criterion is class indices
}