Quick to start
Pytorch vs Tensorflow
- Pytorch + Caffine2 :
- FaceBook開發
- 一邊推一邊算
- 靈活,方便調適,主要學術界
- Tensorflow2.0+Karis :
- Google開發
- 先寫公式,在代數字
- 開發時間早,生態好,主要工業界
創建 Models
繼承 nn.Module
def __init__(self,)
: 定義網路 每層結構(layer)
def forward(self)
: 資料如何在網路中傳遞
super()
= super(className,self)
,找到MOR裡,claaaName後,最先有__init__
ex:
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| class MyNetWork(nn.Module): super().__init__() self.model = nn.Sequential( models... ) def forward(self, ...):
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查看
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| model = MyNetWork() print(model)
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Optimizing(最佳化) Model Parameters
訓練模型需要:
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| loss_fn = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.paramet)
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在單個訓練循環中,模型對訓練數據集(分批輸入)進行預測,並反向傳播預測誤差以調整模型的參數。
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| def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device)
pred = model(X) loss = loss_fn(pred, y)
optimizer.zero_grad() loss.backward() optimizer.step()
if batch % 100 == 0: loss, current = loss.item(), (batch + 1) * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
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訓練可在cpu或gpu
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| device = "cuda" if torch.cuda.is_available() else "cpu" print(f"using {device}")
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we can put instance to cpu or gpu
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| model = className().to(device)
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對比模型在test dataset以確保學習
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| def test(dataloader, model, loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
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每個epoch 模型
我們會打印模型accuracy和loss
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| epochs = 5 for t in range(epochs): print(f"Epoch {t+1}\n-------------------------------") train(train_dataloader, model, loss_fn, optimizer) test(test_dataloader, model, loss_fn) print("Done!")
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保存模型
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| torch.save(model.state_dict(), "model.pth") print("Saved PyTorch Model State to model.pth")
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加載模型
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| model = NeuralNetwork() model.load_state_dict(torch.load("model.pth"))
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