public static void Main(string[] args)
{
var dataset_path = "/home/michal/dataset/mnist/";
//load dataset
var dataset = new DatasetMnist(dataset_path + "train-images.idx3-ubyte",
dataset_path + "train-labels.idx1-ubyte",
dataset_path + "t10k-images.idx3-ubyte",
dataset_path + "t10k-labels.idx1-ubyte");
/*
* create example network
* 3 convolutional layers, dropout and full connected layer as output
* C3x3x16 - P2x2 - C3x3x32 - P2x2 - C3x3x32 - FC10
*/
var cnn = new CNN(dataset.get_input_shape(), dataset.get_output_shape(), (float)0.0025);
cnn.add_layer("convolution", new Shape(3, 3, 16));
cnn.add_layer("elu");
cnn.add_layer("max_pooling", new Shape(2, 2));
cnn.add_layer("convolution", new Shape(3, 3, 32));
cnn.add_layer("elu");
cnn.add_layer("max_pooling", new Shape(2, 2));
cnn.add_layer("convolution", new Shape(3, 3, 32));
cnn.add_layer("elu");
cnn.add_layer("dropout");
cnn.add_layer("output");
cnn.print();
//train network - set epoch count
uint epoch_count = 1;
cnn.train(dataset.get_training_output_all(), dataset.get_training_input_all(), epoch_count);
// test network response on whole testing dataset items
var compare = new ClassificationCompare(dataset.get_classes_count());
//neural network output - vector of floats
var nn_output = rysy.VectorFloatCreate(dataset.get_classes_count());
//for all testing items
for (uint item_idx = 0; item_idx < dataset.get_testing_count(); item_idx++)
{
//get network response
cnn.forward(nn_output, dataset.get_testing_input(item_idx));
//compare with testing dataset
compare.add(dataset.get_testing_output(item_idx), nn_output);
if (compare.is_nan_error())
{
Console.WriteLine("NaN error");
}
}
//process computing and print results
compare.compute();
Console.WriteLine(compare.asString());
Console.WriteLine("program done");
}