Getting activations of certain layer after model is trained

[jhmnk]

If i understand correctly, you want the weights of a certain layer after training?

You can get the weights with list(learner.model.parameters()) and then choose the corresponding layer from the model. The order is the same as the model layers.

In your case, it should be list(learner.model.parameters())[-5]

Or do you want the output from an input up to that highlighted layer?
If so, see:

PyTorch - Best way to get at intermediate layers in VGG and ResNet?

Just getting started with transfer learning in PyTorch and was wondering … What is the recommended way(s) to grab output at intermediate layers (not just the last layer)? In particular, how should one pre-compute the convolutional output for VGG16 … or get the output of ResNet50 BEFORE the global average pooling layer?