pytorch 错误:CrossEntropyLoss() 不支持多目标 [英] pytorch error: multi-target not supported in CrossEntropyLoss()
问题描述
我正在进行一个使用加速度数据来预测一些活动的项目.但是我在损失计算上有问题.我正在为它使用 CrossEntropyLoss.
I am on a project using acceleration data to predict some activities.
But I have problems on the loss calculation. I am using CrossEntropyLoss for it.
数据用于它，如下所示我使用每行的前 4 个数据来预测索引，就像每行的最后一个一样.
Data is used for it like below I use the first 4 data of each rows to predict the index like the last one of each rows.
1 84 84 81 4
81 85 85 80 1
81 82 84 80 1
1 85 84 2 0
81 85 82 80 1
81 82 84 80 1
81 25 84 80 5
错误信息如下.
minoh@minoh-VirtualBox:~/cow$ python lec5.py
Traceback (most recent call last):
File "lec5.py", line 97, in <module>
train(epoch)
File "lec5.py", line 74, in train
loss = criterion(y_pred, labels)
File "/home/minoh/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 357, in __call__
result = self.forward(*input, **kwargs)
File "/home/minoh/anaconda3/lib/python3.6/site-packages/torch/nn/modules/loss.py", line 679, in forward
self.ignore_index, self.reduce)
File "/home/minoh/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 1161, in cross_entropy
return nll_loss(log_softmax(input, 1), target, weight, size_average, ignore_index, reduce)
File "/home/minoh/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 1052, in nll_loss
return torch._C._nn.nll_loss(input, target, weight, size_average, ignore_index, reduce)
RuntimeError: multi-target not supported at /opt/conda/conda-bld/pytorch_1518243271935/work/torch/lib/THNN/generic/ClassNLLCriterion.c:22
我的代码基于 Sung Kim 的 pytorch
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
class CowDataset(Dataset):
def __init__(self):
xy_str = np.loadtxt('cow_test', delimiter = ' ', dtype = np.str)
xy = xy_str.astype(np.float32)
xy_int = xy_str.astype(np.int)
self.len = xy.shape[0]
self.x_data = torch.from_numpy(xy[:, 0:4])
self.y_data = torch.from_numpy(xy_int[:, [4]])
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
def __len__(self):
return self.len
dataset = CowDataset()
train_loader = DataLoader(dataset = dataset, batch_size = 32, shuffle = True)
class CowTestset(Dataset):
def __init__(self):
xy_str = np.loadtxt('cow_test2', delimiter = ' ', dtype =np.str)
xy = xy_str.astype(np.float32)
xy_int = xy_str.astype(np.int)
self.len = xy.shape[0]
self.x_data = torch.from_numpy(xy[:, 0:4])
self.y_data = torch.from_numpy(xy_int[:, [4]])
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
def __len__(self):
return self.len
testset = CowTestset()
test_loader = DataLoader(dataset = testset, batch_size = 32, shuffle = True)
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.l1 = torch.nn.Linear(4,5)
self.l2 = torch.nn.Linear(5,7)
self.l3 = torch.nn.Linear(7,6)
self.sigmoid = torch.nn.Sigmoid()
def forward(self, x):
out1 = self.sigmoid(self.l1(x))
out2 = self.sigmoid(self.l2(out1))
y_pred = self.sigmoid(self.l3(out2))
return y_pred
model = Model()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr = 0.1, momentum = 0.5)
def train(epoch):
model.train()
for batch_idx, (inputs, labels) in enumerate(train_loader):
inputs, labels = Variable(inputs), Variable(labels)
optimizer.zero_grad()
y_pred = model(inputs)
loss = criterion(y_pred, labels)
loss.backward()
optimizer.step()
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data[0]))
def test():
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
data, target = Variable(data, volatile = True), Variable(target)
print(target)
output = model(data)
test_loss += criterion(output, target).data[0]
pred = output.data.max(1, keepdim = True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
test_loss /= len(test_loader.dataset)
print('
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
'.format(test_loss, correct, len(test_loader.dataset), 100.* correct / len(test_loader.dataset)))
for epoch in range(1,7):
train(epoch)
test()
推荐答案
好的.所以我重现了你的问题，在搜索并阅读了 CrossEntropyLoss() 的 API 之后，我发现这是因为你的标签尺寸错误.
Ok. So I reproduced your problem and after some search and reading the API of CrossEntropyLoss(), I have found it's because you have a wrong label dimension.
CrossEntropyLoss 的官方文档在这里.你可以看到
输入:(N,C) 其中 C = 类数
目标:(N) 其中每个值是 0≤targets[i]≤C−1
Input: (N,C) where C = number of classes
Target: (N) where each value is 0≤targets[i]≤C−1
虽然在这里，在您的 criterion() 函数中，您有一个 batchSize x 7 输入和 batchSize x 1 标签.令人困惑的一点是，假设您的 batchSize 为 10，则不能将 10x1 张量视为大小为 10 的张量，这是损失函数所期望的.您必须明确进行大小转换.
While here, in your criterion() function, you have a batchSize x 7 input and batchSize x 1 label. The confusing point is, say your batchSize is 10, a 10x1 tensor can not be regarded as a size-10 tensor, which is what the loss function expectes. You must explictly do the size conversion.
解决方案:
在调用 loss = criteria(y_pred, labels) 之前添加 labels = labels.squeeze_() 并在测试代码中执行相同的操作.squeeze_() 函数就地删除大小为 1 的维度.所以你现在有一个 batchSize 大小的标签.
Solution:
Add labels = labels.squeeze_() before you call loss = criterion(y_pred, labels) and do the same thing in your test code. The squeeze_() funciton removes size-1 dimensions inplace. So you have a batchSize-size label now.
这篇关于pytorch 错误:CrossEntropyLoss() 不支持多目标的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
