(x_train, t_train), (x_test, t_test) = load_mnist(normalize = True, one_hot_label = True)
iters_num = 5000
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
iter_per_epoch = max(train_size // batch_size, 1)
train_loss_list = []
train_acc_list = []
test_acc_list = []
model = NeuralNet()
model.add_affine(784, 50)
model.add_active("relu")
model.add_affine(50, 10)
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]
model.fit(x_batch, t_batch, learning_rate)
loss = model.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = model.accuracy(x_train, t_train)
