将根据模型在验证集上的性能来选择模型并调整超参数。其次，定义了模型训练函数训练。记录了每个时代的训练时间，这有助于比较不同模型的时间成本。
def train(net, train_iter, valid_iter, num_epochs, lr, wd, ctx, lr_period,
lr_decay):
trainer = gluon.Trainer(net.collect_params(), \'sgd\',
{\'learning_rate\': lr, \'momentum\': 0.9, \'wd\': wd})
for epoch in range(num_epochs):
train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()
if epoch > 0 and epoch % lr_period == 0:
trainer.set_learning_rate(trainer.learning_rate * lr_decay)
for X, y in train_iter:
y = y.astype(\'float32\').as_in_ctx(ctx)
with autograd.record():
y_hat = net(X.as_in_ctx(ctx))
l = loss(y_hat, y).sum()
l.backward()
trainer.step(batch_size)
train_l_sum += float(l)
train_acc_sum += float((y_hat.argmax(axis=1) == y).sum())
n += y.size
time_s = "time %.2f sec" % (time.time() - start)
if valid_iter is not None:
valid_acc = d2l.evaluate_accuracy_gpu(net, valid_iter)
epoch_s = ("epoch %d, loss %f, train acc %f, valid acc %f, "
% (epoch + 1, train_l_sum / n, train_acc_sum / n,
valid_acc))
else:
epoch_s = ("epoch %d, loss %f, train acc %f, " %
(epoch + 1, train_l_sum / n, train_acc_sum / n))
print(epoch_s + time_s + \', lr \' + str(trainer.learning_rate))
6. Training and Validating the Model
现在可以对模型进行验证。可以调整以下超参数。例如，可以增加纪元的数量。由于lr_period和lr_decay分别设置为80和0.1，因此每80个周期后，优化算法的学习速率将乘以0.1。为了简单起见，在这里只训练了一个时代。
ctx, num_epochs, lr, wd = d2l.try_gpu(), 1, 0.1, 5e-4
lr_period, lr_decay, net = 80, 0.1, get_net(ctx)
net.hybridize()
train(net, train_iter, valid_iter, num_epochs, lr, wd, ctx, lr_period,
lr_decay)
epoch 1, loss 2.859060, train acc 0.100000, valid acc 0.100000, time 9.51 sec, lr 0.1
7. Classifying the Testing Set and Submitting Results on Kaggle
在获得满意的模型设计和超参数后，使用所有训练数据集（包括验证集）对模型进行再训练并对测试集进行分类。
net, preds = get_net(ctx), []
net.hybridize()
train(net, train_valid_iter, None, num_epochs, lr, wd, ctx, lr_period,
lr_decay)
for X, _ in test_iter:
y_hat = net(X.as_in_ctx(ctx))
preds.extend(y_hat.argmax(axis=1).astype(int).asnumpy())
sorted_ids = list(range(1, len(test_ds) + 1))
sorted_ids.sort(key=lambda x: str(x))
df = pd.DataFrame({\'id\': sorted_ids, \'label\': preds})
df[\'label\'] = df[\'label\'].apply(lambda x: train_valid_ds.synsets[x])
df.to_csv(\'submission.csv\', index=False)
epoch 1, loss 2.873863, train acc 0.106000, time 9.55 sec, lr 0.1
执行上述代码后，将得到一个“submission.csv “文件。此文件的格式符合Kaggle竞赛要求。
8. Summary
We can create an ImageFolderDataset instance to read the dataset containing the original image files.
We can use convolutional neural networks, image augmentation, and hybrid programming to take part in an image classification competition.