机器学习 - 提高模型 (代码）

如果模型出现了 underfitting 问题，就得提高模型了。

举个例子，代码如下：

class CircleModelV1(nn.Module):
  def __init__(self):
    super().__init__()
    self.layer_1 = nn.Linear(in_features = 2, out_features = 10)
    self.layer_2 = nn.Linear(in_features = 10, out_features = 10)
    self.layer_3 = nn.Linear(in_features = 10, out_features = 1)
  
  def forward(self, x):
    return self.layer_3(self.layer_2(self.layer_1(x)))

model_1 = CircleModelV1().to("cpu")
print(model_1)

loss_fn = nn.BCEWithLogitsLoss()
optimizer = torch.optim.SGD(model_1.parameters(), lr=0.1)

torch.manual_seed(42)

epochs = 1000

X_train, y_train = X_train.to("cpu"), y_train.to("cpu")
X_test, y_test = X_test.to("cpu"), y_test.to("cpu")

for epoch in range(epochs):
  ### Training
  # 1. Forward pass 
  y_logits = model_1(X_train).squeeze()
  y_pred = torch.round(torch.sigmoid(y_logits))  # logits -> probabilities -> prediction labels 

  # 2. Calculate loss/accuracy 
  loss = loss_fn(y_logits, y_train)
  acc = accuracy_fn(y_true = y_train, y_pred = y_pred)

  # 3. Optimizer zero grad 
  optimizer.zero_grad()

  # 4. Loss backwards 
  loss.backward()

  # 5. Optimizer step 
  optimizer.step() 

  ### Testing 
  model_1.eval()
  with torch.inference_mode():
    # 1. Forward pass 
    test_logits = model_1(X_test).squeeze()
    test_pred = torch.round(torch.sigmoid(test_logits))
    # 2. Calculate loss/accuracy 
    test_loss = loss_fn(test_logits, y_test)
    test_acc = accuracy_fn(y_true = y_test, y_pred = test_pred)
  
  if epoch % 100 == 0:
    print(f"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}%")

# 结果如下
CircleModelV1(
  (layer_1): Linear(in_features=2, out_features=10, bias=True)
  (layer_2): Linear(in_features=10, out_features=10, bias=True)
  (layer_3): Linear(in_features=10, out_features=1, bias=True)
)
Epoch: 0 | Loss: 0.69528, Accuracy: 51.38%
Epoch: 100 | Loss: 0.69325, Accuracy: 47.88%
Epoch: 200 | Loss: 0.69309, Accuracy: 49.88%
Epoch: 300 | Loss: 0.69303, Accuracy: 50.50%
Epoch: 400 | Loss: 0.69300, Accuracy: 51.38%
Epoch: 500 | Loss: 0.69299, Accuracy: 51.12%
Epoch: 600 | Loss: 0.69298, Accuracy: 51.50%
Epoch: 700 | Loss: 0.69298, Accuracy: 51.38%
Epoch: 800 | Loss: 0.69298, Accuracy: 51.50%
Epoch: 900 | Loss: 0.69298, Accuracy: 51.38%

都看到这了，点个赞呗~