Hi there,
I want to know if I can change the model structure during the training. I know there are trainable parameter that get updated every backward propagation, and that’s not what I am asking.
So for example, I have a initial model with a nn.conv2d with input size of (1, 3,11,22) and one nn.linear(11,22), however during the training I find that some feature is not important or pure noise, so I need to change my nn.linear to (9,22).
Basically, I want the model update the structure itself so the model after the training might total different than the initial one.  It will getting more efficient by updating itself during the training by focus more on important feature rather than noise.
But the auto machine learning is another story which I think it’s total different strategy. Here I just want to some minor adjust to the model without autoML.
Thanks for any advise.