我已经尝试了很多次修复，我也使用了function.py中的示例代码，然后我得到了相同的“损失”值。我该如何解决这个问题？
我的图书馆
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import numpy as np
import matplotlib
import pandas as pd
from torch.autograd import Variable
from torch.utils.data import DataLoader,TensorDataset
from sklearn.model_selection import train_test_split
import warnings
import os
import torchvision
import torchvision.datasets as dsets
import torchvision.transforms as transforms
Mnis数据集
train=pd.read_csv("train.csv",dtype=np.float32)
targets_numpy = train.label.values
features_numpy = train.loc[:,train.columns != "label"].values/255 # normalization
features_train, features_test, targets_train, targets_test = train_test_split(features_numpy,
targets_numpy,test_size = 0.2,
random_state = 42)
featuresTrain=torch.from_numpy(features_train)
targetsTrain=torch.from_numpy(targets_train)
featuresTest=torch.from_numpy(features_test)
targetsTest=torch.from_numpy(targets_test)
batch_size=100
n_iterations=10000
num_epochs=n_iterations/(len(features_train)/batch_size)
num_epochs=int(num_epochs)
train=torch.utils.data.TensorDataset(featuresTrain,targetsTrain)
test=torch.utils.data.TensorDataset(featuresTest,targetsTest)
print(type(train))
train_loader=DataLoader(train,batch_size=batch_size,shuffle=False)
test_loader=DataLoader(test,batch_size=batch_size,shuffle=False)
print(type(train_loader))
plt.imshow(features_numpy[226].reshape(28,28))
plt.axis("off")
plt.title(str(targets_numpy[226]))
plt.show()
这是我的模型
class ANNModel(nn.Module):
def __init__(self,input_dim,hidden_dim,output_dim):
super(ANNModel,self).__init__()
self.fc1=nn.Linear(input_dim,hidden_dim)
self.relu1=nn.ReLU()
self.fc2=nn.Linear(hidden_dim,hidden_dim)
self.tanh2=nn.Tanh()
self.fc4=nn.Linear(hidden_dim,output_dim)
def forward (self,x): #forward ile elde edilen layer lar bağlanır
out=self.fc1(x)
out=self.relu1(out)
out=self.fc2(out)
out=self.tanh2(out)
out=self.fc4(out)
return out
input_dim=28*28
hidden_dim=150
output_dim=10
model=ANNModel(input_dim,hidden_dim,output_dim)
error=nn.CrossEntropyLoss()
learning_rate=0.02
optimizer=torch.optim.SGD(model.parameters(),lr=learning_rate)
问题出在哪里
count=0
loss_list=[]
iteration_list=[]
accuracy_list = []
for epoch in range(num_epochs):
for i,(images,labels) in enumerate(train_loader):
train=Variable(images.view(-1,28*28))
labels=Variable(labels)
#print(labels)
#print(outputs)
optimizer.zero_grad()
#forward propagation
outputs=model(train)
#outputs=torch.randn(784,10,requires_grad=True)
##labels=torch.randn(784,10).softmax(dim=1)
loss=error(outputs,labels)
loss.backward()
optimizer.step()
count+=1
if count %50 ==0:
correct=0
total=0
for images,labels in test_loader:
test=Variable(images.view(-1,28*28))
outputs=model(test)
predicted=torch.max(outputs.data,1)[1] #mantık???
total+= len(labels)
correct+=(predicted==labels).sum()
accuracy=100 *correct/float(total)
loss_list.append(loss.data)
iteration_list.append(count)
accuracy_list.append(accuracy)
if count %500 ==0 :
print('Iteration: {} Loss: {} Accuracy: {} %'.format(count, loss.data, accuracy))
这给出了
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-9-9e53988ad250> in <module>()
26 #outputs=torch.randn(784,10,requires_grad=True)
27 ##labels=torch.randn(784,10).softmax(dim=1)
---> 28 loss=error(outputs,labels)
29
30
2 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
2844 if size_average is not None or reduce is not None:
2845 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2846 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
2847
2848
RuntimeError: expected scalar type Long but found Float
张量“标签”的数据类型似乎是FloatTensor。然而，nn.CrossEntropyLoss 期望一个 LongTensor 类型的目标。这意味着您应该检查“标签”的类型。如果是这种情况，那么您应该使用以下代码将“标签”的数据类型从 FloatTensor 转换为 LongTensor：
loss=error(outputs,labels.long())
targetsTrain=torch.from_numpy(targets_train)
targetsTest=torch.from_numpy(targets_test)
在这些行中，您必须添加这些代码：
targetsTrain=torch.from_numpy(targets_train).type(torch.LongTensor)#data type is long
targetsTest=torch.from_numpy(targets_test).type(torch.LongTensor)#data type is long
然后就可以正常工作了
我在下面遇到了同样的错误：
运行时错误：预期标量类型为 Long，但发现为 Float
当我将 CrossEntropyLoss() 与大小为 1 的 1D 张量和 0D 张量一起使用时，如下所示
import torch
from torch import nn
tensor1 = torch.tensor([7.2]) # Here
tensor2 = torch.tensor(1.5) # Here
cel = nn.CrossEntropyLoss()
cel(input=tensor1, target=tensor2) # Error
因此，我将
1.50tensor2import torch
from torch import nn
tensor1 = torch.tensor([7.2])
tensor2 = torch.tensor(0) # Here
cel = nn.CrossEntropyLoss()
cel(input=tensor1, target=tensor2)
# tensor(0.)