function test_nn_gradients_are_numerically_correct
batch_x = rand(20, 5);
batch_y = rand(20, 2);

for output = {'sigm', 'linear', 'softmax'}
    y=batch_y;
    if(strcmp(output,'softmax'))
        % softmax output requires a binary output vector
        y=(y==repmat(max(y,[],2),1,size(y,2)));
    end
    
    for activation_function = {'sigm', 'tanh_opt'}
        for dropoutFraction = {0 rand()}
            nn = nnsetup([5 3 4 2]);

            nn.activation_function = activation_function{1};
            nn.output = output{1};
            nn.dropoutFraction = dropoutFraction{1};

            rand('state',0)
            nn = nnff(nn, batch_x, y);
            nn = nnbp(nn);
            nnchecknumgrad(nn, batch_x, y);
        end
    end
end