[英]Pytorch Geometric: RuntimeError: expected scalar type Long but found Float
[英]RuntimeError: expected scalar type Long but found Float (Pytorch)
我已经尝试过很多次修复,我也使用了来自functional.py的示例代码,然后我得到了相同的“损失”值。 我怎样才能解决这个问题?
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的数据集
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 model
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))
这使
---------------------------------------------------------------------------
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
似乎张量“标签”的 dtype 是 FloatTensor。 但是,nn.CrossEntropyLoss 需要 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 dtype 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,您必须添加以下代码:
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
然后它可以正常工作
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