Problems using pretrained ResNet50 in PyTorch to solve CIFAR10 Dataset
I got the following error using a pretrained ResNet50 in PyTorch:

RuntimeError 
Traceback (most recent call last)
&lt;ipython-input-14-8f0d0641ef12&gt; in &lt;module&gt;()
      28         # Update parameters
      29         optimizer.zero_grad()
 ---&gt; 30         loss.backward()
      31         optimizer.step()
      32 

 1 frames
 /usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py in 
 backward(tensors, grad_tensors, retain_graph, create_graph, 
 grad_variables)
      98     Variable._execution_engine.run_backward(
      99         tensors, grad_tensors, retain_graph, create_graph,
 --&gt; 100         allow_unreachable=True)  # allow_unreachable flag
     101 
     102 

 RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn


Notebook is in this link: https://colab.research.google.com/drive/1k40NNulSIS6ANagopSPBH4Xty_Cw39qC?usp=sharing

The problem is that you're setting a new attribute model.classifier, while you actually want to replace the current "classifier", i.e., change the model.fc.

It is beyond the scope of your question, but you'll find another problem later on. Your new classifier has a LogSoftmax() module and you're using the nn.CrossEntropyLoss(). As you can see here, you should not do this.
