fc_learning_api_mnist.m

function [ output_args ] = fc_learning_api( input_args )
%FC_LEARNING_API Summary of this function goes here
%   Detailed explanation goes here
    startup();
    
    [train_input, train_classes, test_input,  test_classes] = GenerateDatasetMNIST();
    
    hidden_neurons_count = 500;
    output_neurons_count = 10;
    input_dim = size(train_input,2);
    
    rng(0,'v5uniform');
    
    
    learningRate = 0.0005;
    momentum = 0.9;
    weightDecay = 0.0005;
    
    nn = network();
    
    nn.addLayer(LayerInput(input_dim), {});
    nn.addLayer(LayerFC(input_dim,hidden_neurons_count,WeightFillerGaussian(1/(input_dim + hidden_neurons_count))),  GradientUpdaterUsingMomentumAndWeightDecay(learningRate, momentum, weightDecay));
    nn.addLayer(LayerActivationRELU,  {});
    nn.addLayer(LayerFC(hidden_neurons_count,output_neurons_count,WeightFillerGaussian(1/(hidden_neurons_count + output_neurons_count))),  GradientUpdaterUsingMomentumAndWeightDecay(learningRate, momentum, weightDecay));
    nn.addLayer(LayerActivationRELU,  {});
    
        
    epochs = 30;
    minibatchSize = 64;
    
    trainSoftmaxNetwork(nn, epochs, minibatchSize, train_input, train_classes);

    output_train_full = nn.forwardPropogate(train_input);
    [~, ind_train] = max(output_train_full{end}');
    [~, ind_train_gt] = max(train_classes');
    accuracy_train = (sum(ind_train == ind_train_gt)) / numel(ind_train);

    output_test_full = nn.forwardPropogate(test_input);
    [~, ind_test] = max(output_test_full{end}');
    [~, ind_test_gt] = max(test_classes');
    accuracy_test = (sum(ind_test == ind_test_gt)) / numel(ind_test);

    disp(['train accuracy : ' num2str(accuracy_train) ' test accuracy : ' num2str(accuracy_test)]);
end