如何解决运行时错误:/pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15____ 不支持多目标
我面临那个错误 运行时错误:/pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15____
不支持多目标我的输入是 340 的二进制向量,目标是 8 的二进制向量,对于 '"
loss = criterion(outputs,stat_batch)
,我得到 outputs.shape
= [64,8] 和 stat_batch.shape
=[64,8]
这是模型
class MMP(nn.Module):
def __init__(self,M=1):
super(MMP,self).__init__()
# input layer
self.layer1 = nn.Sequential(
nn.Conv1d(340,256,kernel_size=1,stride=1,padding=0),nn.ReLU())
self.layer2 = nn.Sequential(
nn.Conv1d(256,128,nn.ReLU())
self.layer3 = nn.Sequential(
nn.Conv1d(128,64,nn.ReLU())
self.drop1 = nn.Sequential(nn.Dropout())
self.batch1 = nn.Batchnorm1d(128)
# LSTM
self.lstm1=nn.Sequential(nn.LSTM(
input_size=64,hidden_size=128,num_layers=2,bidirectional=True,batch_first= True))
self.fc1 = nn.Linear(128*2,8)
self.sof = nn.softmax(dim=-1)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.drop1(out)
out = out.squeeze()
out = out.unsqueeze(0)
#out = out.batch1(out)
out,_ = self.lstm1(out)
print("lstm",out.shape)
out = self.fc1(out)
out =out.squeeze()
#out = out.squeeze()
out = self.sof(out)
return out
#traiin_model
criterion = nn.CrossEntropyLoss()
if CUDA:
criterion = criterion.cuda()
optimizer = optim.SGD(model.parameters(),lr=LEARNING_RATE,momentum=0.9)
for epoch in range(N_EPOCHES):
tot_loss=0
# Training
for i,(seq_batch,stat_batch) in enumerate(training_generator):
# Transfer to GPU
seq_batch,stat_batch = seq_batch.to(device),stat_batch.to(device)
print(i)
print(seq_batch)
print(stat_batch)
optimizer.zero_grad()
# Model computation
seq_batch = seq_batch.unsqueeze(-1)
outputs = model(seq_batch)
if CUDA:
loss = criterion(outputs,stat_batch).float().cuda()
else:
loss = criterion(outputs.view(-1),stat_batch.view(-1))
print(f"Epoch: {epoch},number: {i},loss:{loss.item()}...\n\n")
tot_loss += loss.item(print(f"Epoch: {epoch},file_number: {i},loss:{loss.item()}...\n\n"))
loss.backward()
optimizer.step()
解决方法
您的目标 stat_batch
必须具有 (64,)
的形状,因为 nn.CrossEntropyLoss
接受类索引,不是单热编码。
要么适当地构建您的标签张量,要么改用 stat_batch.argmax(axis=1)
。
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