IndexError：目標 1 超出范圍

Question

當我運行下面的程序時，它給了我一個錯誤。 問題似乎在於丟失 function 但我找不到。 我已經閱讀了 nn.CrossEntropyLoss 的 Pytorch 文檔，但仍然找不到問題。

圖像尺寸為 (1 x 256 x 256)，批量大小為 1

我是 PyTorch 的新手，謝謝。

import torch
import torch.nn as nn
from PIL import Image
import numpy as np
torch.manual_seed(0)

x = np.array(Image.open("cat.jpg"))
x = np.expand_dims(x, axis = 0)
x = np.expand_dims(x, axis = 0)
x = torch.from_numpy(x)
x = x.type(torch.FloatTensor) # shape = (1, 1, 256, 256)

def Conv(in_channels, out_channels, kernel=3, stride=1, padding=0):
    return nn.Conv2d(in_channels, out_channels, kernel, stride, padding)

class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()

        self.sequential = nn.Sequential(
            Conv(1, 3),
            Conv(3, 5),
            nn.Flatten(),
            nn.Linear(317520, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        y = self.sequential(x)
        return y

def compute_loss(y_hat, y):
    return nn.CrossEntropyLoss()(y_hat, y)

model = model()
y_hat = model(x)

loss = compute_loss(y_hat, torch.tensor([1]))

錯誤：

Traceback (most recent call last):
  File "D:/Me/AI/Models/test.py", line 38, in <module>
    **loss = compute_loss(y, torch.tensor([1]))**
  File "D:/Me/AI/Models/test.py", line 33, in compute_loss
    return nn.CrossEntropyLoss()(y_hat, y)
  File "D:\Softwares\Anaconda\envs\deeplearning\lib\site-packages\torch\nn\modules\module.py", line 1054, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Softwares\Anaconda\envs\deeplearning\lib\site-packages\torch\nn\modules\loss.py", line 1120, in forward
    return F.cross_entropy(input, target, weight=self.weight,
  File "D:\Softwares\Anaconda\envs\deeplearning\lib\site-packages\torch\nn\functional.py", line 2824, in cross_entropy
    return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
**IndexError: Target 1 is out of bounds.**

Process finished with exit code 1

Answer 1

這看起來像一個二進制分類器 model：cat or not cat。 但是您正在使用 CrossEntropyLoss，當您有超過 2 個目標類時使用它。 所以你應該使用的是Binary Cross Entropy Loss 。

def compute_loss(y_hat, y):
    return nn.BCELoss()(y_hat, y)

Answer 2

試試loss = compute_loss(y_hat, torch.tensor([0]))

Answer 3

我認為這種變化
nn.Linear(317520, 1) -> nn.Linear(317520, 2)