Abstract
Personal understanding of the working paradigm of training an artificial neural network (ANN) based on Pytorch.
Paradigm
一、数据（torch.utils.data.DataLoader）
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二、模型（torch.nn）
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三、策略（损失函数, criterion = torch.nn.BlaBlaLoss）+ 算法（优化算法, optimizer = torch.optim.SGD|Adam|Adadelta...）
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四、迭代训练
(
FOR
1. optimizer.zero_grad()
2. outputs_train = net(inputs_train)
3. loss_train = criterion(outputs_train, labels_train)
4. loss_train.backward()
5. optimizer.step()
END FOR
)
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五、调参/测试（验证集调参/测试集进行最后的打分）
(
with torch.no_grad():
outputs_test = net(inputs_test)
loss_test = criterion(outputs_test, labels_test)
... other test criterion ...
)
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六、加速（optional）
(
# Train on GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
inputs, labels = inputs.to(device), labels .to(device)
# Data parallelism
net = nn.DataParallel(net)
)
to be continued...
References
Pytorch tutorial: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
李航老师: 《统计机器学习》
