I am trying to train my model. My model outputs a [4,2] tensor where 4 is the batch size and 2 because of binary classification. After receiving the outputs I found the index of the maximum element for each row.so , now the shape is [4,1] and the shape of my label is [4,1] as well.I could not understand why am I still getting this error.Could someone please help me to solve it out.Also, the optimizer I am using is SGD and the loss criteria is crossentropy.

  for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(dataloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        #inputs, labels = data

        inputs, labels = \
            data['image'], data['Status']

        # zero the parameter gradients
        outputs = net(inputs.float())


        loss = criterion(a, labels)

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

This is the error I am getting.

torch.Size([4, 1]) torch.Size([4, 1])
RuntimeError                              Traceback (most recent call last)
<ipython-input-83-72f63a4db63e> in <module>()
     22         labels=labels.float()
     23         print(a.shape,labels.shape)
---> 24         loss = criterion(a, labels)
     25         loss.backward()
     26         optimizer.step()

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)
   2822     if size_average is not None or reduce is not None:
   2823         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2824     return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)

RuntimeError: 1D target tensor expected, multi-target not supported

Also,model is:

import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 16 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 16, 5)
        self.conv2 = nn.Conv2d(16, 32, 7)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(4608,128)  
        self.fc2 = nn.Linear(128,16)
        self.fc3 = nn.Linear(16, 2)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square, you can specify with a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = self.dropout1(x)

        x = torch.flatten(x, 1) # flatten all dimensions except the batch dimension
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.dropout2(x)
        x =self.fc3(x)
        return x

net = Net()
net = net.float()

1 Answer 1


If this is a binary classification problem then your model should only need to predict one output - a value between 0 and 1. A predicted value close to 0 would indicate the input likely belongs to your first class, and a predicted value close to 1 would indicate the input likely belongs to the second class.

Then you can optimise your model using a loss function such as nn.BCELoss(prediction, target) or nn.BCEWithLogitsLoss(prediction, target). This should avoid the error that you currently get as you won't be dealing with multiple output values for your predictions.


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