我正在 Pytorch 中創建一個 CNN，我相信我在訓練功能方面遇到了一些問題。
對于每個時期，損失都會減少。但精度保持不變，它不會改變。訓練函式的輸出是這樣的：
Epoch: 1
correct: 234, N_test: 468 ------> loss: 58.2041027, accuracy_val: %50.0
Epoch: 2
correct: 234, N_test: 468 ------> loss: 51.47981386, accuracy_val: %50.0
Epoch: 3
correct: 234, N_test: 468 ------> loss: 51.57150275, accuracy_val: %50.0
Epoch: 4
correct: 234, N_test: 468 ------> loss: 39.14232715, accuracy_val: %50.0
Epoch: 5
correct: 234, N_test: 468 ------> loss: 32.23730827, accuracy_val: %50.0
我知道雖然它們是相關的，但損失和準確性有其復雜性，但我相信代碼可能有問題，我無法確定是什么。
這是神經網路：
class CNN(nn.Module):
# Contructor
def __init__(self):
super(CNN, self).__init__()
# Conv1
self.cnn1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=1, padding=0)
self.conv1_bn = nn.BatchNorm2d(64)
self.maxpool1=nn.MaxPool2d(kernel_size=2, stride=2)
# Conv2
self.cnn2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=5,stride=1, padding=0)
self.conv2_bn = nn.BatchNorm2d(64)
self.maxpool2=nn.MaxPool2d(kernel_size=2, stride=2)
# Conv3
self.cnn3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5,stride=1, padding=0)
self.conv3_bn = nn.BatchNorm2d(128)
self.maxpool3=nn.MaxPool2d(kernel_size=2, stride=2)
# FCL 1
self.fc1 = nn.Linear(in_features=128 * 27 * 27, out_features=500)
self.bn_fc1 = nn.BatchNorm1d(500)
# FCL 2
self.fc2 = nn.Linear(in_features=500, out_features=500)
self.bn_fc2 = nn.BatchNorm1d(500)
# FCL3
self.fc3 = nn.Linear(in_features=500, out_features=1)
# Prediction
def forward(self, x):
# conv1
x = self.cnn1(x)
x = self.conv1_bn(x)
x = torch.relu(x)
x = self.maxpool1(x)
# conv2
x = self.cnn2(x)
x = self.conv2_bn(x)
x = torch.relu(x)
x = self.maxpool2(x)
# conv3
x = self.cnn3(x)
x = self.conv3_bn(x)
x = torch.relu(x)
x = self.maxpool3(x)
# Fcl1
x = x.view(x.size(0), -1)
x = self.fc1(x)
x = self.bn_fc1(x)
x = torch.relu(x)
# Fcl2
x = self.fc2(x)
x = self.bn_fc2(x)
x = torch.relu(x)
# final fcl
x = self.fc3(x)
x = torch.sigmoid(x)
return x
訓練功能：
def train_model(model,train_loader,test_loader,optimizer,n_epochs=5):
#global variable
N_test=len(dataset_val)
accuracy_list=[]
loss_list=[]
for epoch in range(n_epochs):
cost = 0
model.train()
print(f"Epoch: {epoch 1}")
for x, y in train_loader:
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
z = model(x)
y = y.unsqueeze(-1)
y = y.float()
loss = criterion(z, y)
loss.backward()
optimizer.step()
cost =loss.item()
correct=0
model.eval()
#perform a prediction on the validation data
for x_test, y_test in test_loader:
x_test, y_test = x_test.to(device), y_test.to(device)
z = model(x_test)
_, yhat = torch.max(z.data, 1)
correct = (yhat == y_test).sum().item()
accuracy = correct / N_test
accuracy_list.append(accuracy)
loss_list.append(cost)
print(f"------> loss: {round(cost, 8)}, accuracy_val: %{accuracy * 100}")
return accuracy_list, loss_lis
劇情是這樣的：
用精度和損失繪圖
uj5u.com熱心網友回復：
由于您有 1 個輸出并且您在第 2 個維度上取最大值，因此您的輸出都將為 1：
_, yhat = torch.max(z.data, 1)
correct = (yhat == y_test).sum().item()
要進行二元分類，您需要選擇一個閾值，然后將您的資料閾值分為兩類，或者有 2 個輸出（在這種情況下可能更容易）。
uj5u.com熱心網友回復：
我從最后一層洗掉了 sigmoid 函式并替換BCELoss()為CrossEntropyLoss()并作業！
此外，正如@jhso 所說，要進行二元分類需要一個閾值，我們必須將資料閾值分為兩類，或者有 2 個輸出（在這種情況下可能更容易）。
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