RuntimeError: 0D or 1D target tensor expected, multi-target not supported I was training a deep learning model but i am getting this issue
Question:
*My Training Model*
def train(model,criterion,optimizer,iters):
epoch = iters
train_loss = []
validaion_loss = []
train_acc = []
validation_acc = []
states = ['Train','Valid']
for epoch in range(epochs):
print("epoch : {}/{}".format(epoch+1,epochs))
for phase in states:
if phase == 'Train':
model.train() *training the data if phase is train*
dataload = train_data_loader
else:
model.eval()
dataload = valid_data_loader
run_loss,run_acc = 0,0 *creating variables to calculate loss and acc*
for data in dataload:
inputs,labels = data
inputs = inputs.to(device)
labels = labels.to(device)
labels = labels.byte()
optimizer.zero_grad() #Using the optimizer
with torch.set_grad_enabled(phase == 'Train'):
outputs = model(inputs)
loss = criterion(outputs,labels.unsqueeze(1).float())
predict = outputs>=0.5
if phase == 'Train':
loss.backward() #backward propagation
optimizer.step()
acc = torch.sum(predict == labels.unsqueeze(1))
run_loss+=loss.item()
run_acc+=acc.item()/len(labels)
if phase == 'Train': #calulating train loss and accucracy
epoch_loss = run_loss/len(train_data_loader)
train_loss.append(epoch_loss)
epoch_acc = run_acc/len(train_data_loader)
train_acc.append(epoch_acc)
else: #training validation loss and accuracy
epoch_loss = run_loss/len(valid_data_loader)
validaion_loss.append(epoch_loss)
epoch_acc = run_acc/len(valid_data_loader)
validation_acc.append(epoch_acc)
print("{}, loss :{},accuracy:{}".format(phase,epoch_loss,epoch_acc))
history = {'Train_loss':train_loss,'Train_accuracy':train_acc,
'Validation_loss':validaion_loss,'Validation_Accuracy':validation_acc}
return model,history[enter image description here][1]
I was experiencing the error as 0D or 1D target tensor expected, multi-target not supported could you please help in rectifying the code which is described above. Referred the previous related articles but unable to get the desired result. What are the code snippets I had to change so that my model will run successfully. Any suggestions are mostly welcome. Thanks in Advance.
Answers:
Your problem is that labels have the correct shape to calculate the loss. When you add .unsqueeze(1) to labels you made your labels with this shape [32,1] which is not consistent to the requirment to calcualte the loss.
To fix the problem, you only need to remove .unsqueeze(1) for labels.
If you read the documentation of CrossEntropLoss, the arguments:
- Input should be in (N,C) shape which is
outputs in your case and [32,3].
- Target should be in N shape which is
labels in your case and should be [32]. Therefore, the loss function expects labels to be in 1D target not multi-target.
This issue can also be due to loss function. Try using alternative loss functions that can deal with multi-target tensor. I used nn.MSELoss() and the error went away.
*My Training Model*
def train(model,criterion,optimizer,iters):
epoch = iters
train_loss = []
validaion_loss = []
train_acc = []
validation_acc = []
states = ['Train','Valid']
for epoch in range(epochs):
print("epoch : {}/{}".format(epoch+1,epochs))
for phase in states:
if phase == 'Train':
model.train() *training the data if phase is train*
dataload = train_data_loader
else:
model.eval()
dataload = valid_data_loader
run_loss,run_acc = 0,0 *creating variables to calculate loss and acc*
for data in dataload:
inputs,labels = data
inputs = inputs.to(device)
labels = labels.to(device)
labels = labels.byte()
optimizer.zero_grad() #Using the optimizer
with torch.set_grad_enabled(phase == 'Train'):
outputs = model(inputs)
loss = criterion(outputs,labels.unsqueeze(1).float())
predict = outputs>=0.5
if phase == 'Train':
loss.backward() #backward propagation
optimizer.step()
acc = torch.sum(predict == labels.unsqueeze(1))
run_loss+=loss.item()
run_acc+=acc.item()/len(labels)
if phase == 'Train': #calulating train loss and accucracy
epoch_loss = run_loss/len(train_data_loader)
train_loss.append(epoch_loss)
epoch_acc = run_acc/len(train_data_loader)
train_acc.append(epoch_acc)
else: #training validation loss and accuracy
epoch_loss = run_loss/len(valid_data_loader)
validaion_loss.append(epoch_loss)
epoch_acc = run_acc/len(valid_data_loader)
validation_acc.append(epoch_acc)
print("{}, loss :{},accuracy:{}".format(phase,epoch_loss,epoch_acc))
history = {'Train_loss':train_loss,'Train_accuracy':train_acc,
'Validation_loss':validaion_loss,'Validation_Accuracy':validation_acc}
return model,history[enter image description here][1]
I was experiencing the error as 0D or 1D target tensor expected, multi-target not supported could you please help in rectifying the code which is described above. Referred the previous related articles but unable to get the desired result. What are the code snippets I had to change so that my model will run successfully. Any suggestions are mostly welcome. Thanks in Advance.
Your problem is that labels have the correct shape to calculate the loss. When you add .unsqueeze(1) to labels you made your labels with this shape [32,1] which is not consistent to the requirment to calcualte the loss.
To fix the problem, you only need to remove .unsqueeze(1) for labels.
If you read the documentation of CrossEntropLoss, the arguments:
- Input should be in (N,C) shape which is
outputsin your case and [32,3]. - Target should be in N shape which is
labelsin your case and should be [32]. Therefore, the loss function expectslabelsto be in1D target not multi-target.
This issue can also be due to loss function. Try using alternative loss functions that can deal with multi-target tensor. I used nn.MSELoss() and the error went away.