输出结果
核心代码
for i in range(1000000):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
grads = network.gradient(x_batch, t_batch)
optimizer.update(network.params, grads)
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("epoch:" + str(epoch_cnt) + ", train_acc:" + str(float('%.4f' % train_acc)) + ", test_acc:" + str(float('%.4f' % test_acc)))
epoch_cnt += 1
if epoch_cnt >= max_epochs: #
break
