Replacing classifier head in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does 'replacing the classifier head' mean in a neural network?

It means changing the last layer(s) of a pre-trained model to fit a new task, like changing the output to match new classes.

beginner

Why do we replace the classifier head instead of retraining the whole model?

Because the earlier layers already learned useful features, so we only need to adjust the last part to the new task, saving time and data.

intermediate

In PyTorch, which module usually represents the classifier head in models like ResNet?

The 'fc' (fully connected) layer is usually the classifier head in ResNet models.

intermediate

How do you replace the classifier head in a PyTorch model?

You assign a new layer to the model's classifier attribute, for example: model.fc = nn.Linear(in_features, num_classes).

intermediate

What should you consider about the input features when replacing the classifier head?

The new classifier's input size must match the output size of the previous layer to connect properly.

What is the main reason to replace the classifier head in a pre-trained model?

A To adapt the model to a new number of output classes

B To change the input image size

C To speed up the GPU

D To reduce the number of layers

Replacing the classifier head changes the output layer to match the new task's classes.

In PyTorch's ResNet, which attribute is replaced to change the classifier head?

A model.fc

B model.conv1

C model.layer1

D model.avgpool

The 'fc' attribute is the final fully connected layer acting as the classifier head.

If the original classifier outputs 1000 classes, and your new task has 10 classes, what should you do?

A Keep the original classifier

B Change the input image size

C Replace the classifier head with output size 10

D Add more layers before the classifier

You replace the classifier head to output the correct number of classes for your task.

What PyTorch module is commonly used to create a new classifier head?

A nn.Conv2d

B nn.MaxPool2d

C nn.ReLU

D nn.Linear

nn.Linear creates a fully connected layer used as a classifier head.

What must match between the old and new classifier head when replacing it?

A Output size of the new head and input size of the previous layer

B Input size of the new head and output size of the previous layer

C Number of layers in the model

D Learning rate

The new classifier's input size must match the previous layer's output size to connect properly.

Explain how and why you would replace the classifier head in a pre-trained PyTorch model.

Think about transfer learning and output classes.

Describe the steps to ensure the new classifier head connects correctly to the rest of the model.

Focus on layer sizes and connections.