public void main()
{
(var x_train, var t_trainSrc, var x_test, var t_testSrc) = minst.load_mnist_normalize_onehot();
var t_train = t_trainSrc.todouble();
var t_test = t_testSrc.todouble();
var network = new TwoLayerNet(input_size: 784, hidden_size: 50, output_size: 10);
var x_batch = x_train.Take(3).ToArray();
var t_batch = t_train.Take(3).ToArray();
var grad_numerical = network.numerical_gradient(x_batch, t_batch);
var grad_backprop = network.gradient(x_batch, t_batch);
foreach (var key in grad_numerical.Keys)
{
var diff = np.average(np.abs(grad_backprop[key].minus(grad_numerical[key])));
std.print(key + ":" + diff);
}
}
public void main()
{
(var x_train, var t_trainSrc, var x_test, var t_testSrc) = minst.load_mnist_normalize_onehot();
var t_train = t_trainSrc.todouble();
var t_test = t_testSrc.todouble();
var network = new TwoLayerNet(input_size: 784, hidden_size: 50, output_size: 10);
var iters_num = 10000;
var train_size = x_train.Length;
var batch_size = 100;
var learning_rate = 0.1;
var train_loss_list = new List <double>();
var train_acc_list = new List <double>();
var test_acc_list = new List <double>();
var iter_per_epoch = Math.Max(train_size / batch_size, 1);
for (int i = 0; i < iters_num; i++)
{
var batch_mask = np.random.choice(train_size, batch_size);
var x_batch = x_train.choice(batch_mask);
var t_batch = t_train.choice(batch_mask);
// 勾配
// grad = network.numerical_gradient(x_batch, t_batch);
var grad = network.gradient(x_batch, t_batch);
// 更新
foreach (var key in new[] { "W1", "b1", "W2", "b2" })
{
network._params[key].assign(network._params[key].minus(grad[key].mul(learning_rate)));
}
var loss = network.loss(x_batch, t_batch);
train_loss_list.Add(loss);
if (i % iter_per_epoch == 0)
{
var train_acc = network.accuracy(x_train, t_train);
var test_acc = network.accuracy(x_test, t_test);
train_acc_list.Add(train_acc);
test_acc_list.Add(test_acc);
std.print(std.str(train_acc) + ", " + std.str(test_acc));
}
}
// グラフの描画
var markers = new Dictionary <string, string> {
{ "train", "o" }, { "test", "s" }
};
var x = np.arange(train_acc_list.Count);
plt.plot(x, train_acc_list.ToArray(), label: "train acc");
plt.plot(x, test_acc_list.ToArray(), label: "test acc", linestyle: "--");
plt.xlabel("epochs");
plt.ylabel("accuracy");
plt.ylim(0, 1.0);
plt.legend(loc: "lower right");
plt.show();
}