可以给我调整超参数的代码吗

当然可以，以下是调整学习率和迭代次数的代码示例：

# 调整学习率和迭代次数
iters_num = 20000  # 迭代次数
learning_rate = 0.01  # 学习率
train_loss_list = []
train_acc_list = []
test_acc_list = []

for i in range(iters_num):
    batch_mask = np.random.choice(train_size, batch_size)
    X_batch = X_train[batch_mask]
    T_batch = T_train[batch_mask]
    
    grad = network.gradient(X_batch, T_batch)
    for key in ('W1', 'b1', 'W2', 'b2'):
        network.params[key] -= learning_rate * grad[key]
    
    loss = network.loss(X_batch, T_batch)
    train_loss_list.append(loss)
    
    if i % iter_per_epoch == 0:
        train_acc = network.accuracy(X_train, T_train)
        test_acc = network.accuracy(X_test, T_test)
        train_acc_list.append(train_acc)
        test_acc_list.append(test_acc)
        print('train acc, test acc | ' + str(train_acc) + ', ' + str(test_acc))

        # 调整学习率
        if i > 0 and i % 1000 == 0:
            learning_rate *= 0.1
            print("learning rate reduced to " + str(learning_rate))

代码中每隔一定的迭代次数，记录一次训练集和测试集的准确率，并且每迭代1000次时将学习率降低一个数量级。这样可以根据训练过程中的表现，动态调整学习率，加速收敛，提高模型的性能。