st = time.time()
best_score = 0
for epoch in range(epochs + 1):
epoch_loss = 0.0
for iteration in range(total_num // batch_size):
seed = np.random.choice(total_num, batch_size)
x_batch = x_train[seed]
y_batch = y_train[seed]
loss = model.loss(x_batch, y_batch)
epoch_loss += loss
model.zero_grad()
model.backward()
model.update()
epoch_loss /= batch_size
pred = model.predict(x_train)
target = np.argmax(y_train, axis=1)
score = len(np.where(pred == target)[0]) / len(target)
val_pred = model.predict(x_val)
val_target = np.argmax(y_val, axis=1)
val_score = len(np.where(val_pred == val_target)[0]) / len(val_target)
acc_stack.append(score)
val_acc_stack.append(val_score)
loss_stack.append(epoch_loss)
