為什么pytorch分類model不學習？

Question

我創建了一個簡單的 pytorch 分類 model ，其中包含使用 sklearns make_classification生成的示例數據集。 即使經過數千個 epoch 的訓練，model 的准確率也徘徊在 30% 到 40% 之間。 在訓練期間，損失值波動很大。 我想知道為什么這個 model 不學習，是否由於代碼中的一些邏輯錯誤。

import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X,y = make_classification(n_features=15,n_classes=5,n_informative=4)

DEVICE = torch.device('cuda')
epochs = 5000

class CustomDataset(Dataset):
    def __init__(self,X,y):
        self.X =  torch.from_numpy(X)
        self.y = torch.from_numpy(y)
    def __len__(self):
        return len(self.X)
    def __getitem__(self, index):
        X = self.X[index]
        y = self.y[index]
        return (X,y)

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = nn.Linear(15,10)
        self.l2 = nn.Linear(10,5)
        self.relu = nn.ReLU()
    def forward(self,x):
        x = self.l1(x)
        x = self.relu(x)
        x = self.l2(x)
        x = self.relu(x)
        return x

model = Model().double().to(DEVICE)

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
loss_function = nn.CrossEntropyLoss()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
train_data = CustomDataset(X_train,y_train)
test_data = CustomDataset(X_test,y_test)

trainloader = DataLoader(train_data, batch_size=32, shuffle=True)
testloader = DataLoader(test_data, batch_size=32, shuffle=True)

for i in range(epochs):
    for (x,y) in trainloader:
        x = x.to(DEVICE)
        y = y.to(DEVICE)
        optimizer.zero_grad()
        output = model(x)
        loss = loss_function(output,y)
        loss.backward()
        optimizer.step()
    if i%200==0:
        print("epoch: ",i," Loss: ",loss.item())

correct = 0
total = 0
# since we're not training, we don't need to calculate the gradients for our outputs
with torch.no_grad():
    for x, y in testloader:
        
        # calculate outputs by running x through the network
        outputs = model(x.to(DEVICE)).to(DEVICE)
        # the class with the highest energy is what we choose as prediction
        _, predicted = torch.max(outputs.data, 1)
        total += y.size(0)
        correct += (predicted == y.to(DEVICE)).sum().item()

    print(f'Accuracy of the network on the test data: {100 * correct // total} %')

Answer 1

您不應該在 output 層上使用 ReLU 激活。 通常 softmax 激活用於最后一層的多 class 分類，或者直接將 logits 饋送到損失 function 而不顯式添加 softmax 激活層。

嘗試從最后一層移除 ReLU 激活。