[Test] public void MultiLayerPerceptron()
{
CleanMem_();
const double clipNorm = 3.0;
const long batchSize = 1000L;
const long epochs = 3;
var model = MultiLayerPerceptronModel();
var ctx = Context.GpuContext(0);
var opt = new RMSpropOptimizer(ctx, model.Loss.Loss, 0.005, 0.9, float.Epsilon,
new GlobalNormGradientClipper(clipNorm));
opt.Initalize();
var mnist = new MNIST();
var batcher = new Batcher(ctx, mnist.TrainImages, mnist.TrainLabels);
for (var e = 1; e <= epochs; ++e)
{
var i = 0;
while (batcher.Next(batchSize, opt, model.Images, model.Labels))
{
i++;
opt.Forward();
opt.Backward();
opt.Optimize();
if ((i % 10 == 0) || ((i == 1) && (e == 1)))
{
PrintStatus(e, i, opt, model, mnist.ValidationImages, mnist.ValidationLabels);
}
}
}
PrintResult(opt, model, mnist.TestImages, mnist.TestLabels);
}
[Test] public void ConvolutionalNeuralNetwork()
{
CleanMem_();
const long batchSize = 500L;
const long epochs = 2;
var model = ConvolutionalNeuralNetworkModel();
var ctx = Context.GpuContext(0);
var opt = new RMSpropOptimizer(ctx, model.Loss.Loss, 0.005, 0.9, 1e-9);
opt.Initalize();
var mnist = new MNIST();
var batcher = new Batcher(ctx, mnist.TrainImages, mnist.TrainLabels);
for (var e = 1; e <= epochs; ++e)
{
var i = 0;
while (batcher.Next(batchSize, opt, model.Images, model.Labels))
{
i++;
opt.Forward();
opt.Backward();
opt.Optimize();
if ((i % 20 == 0) || ((i == 1) && (e == 1)))
{
PrintStatus(e, i, opt, model, mnist.ValidationImages, mnist.ValidationLabels);
}
}
}
PrintResult(opt, model, mnist.TestImages, mnist.TestLabels);
CleanMem_();
}
