Why do we need to call zero_grad() in PyTorch?
Why does zero_grad() need to be called during training?
Why does zero_grad() need to be called during training?
I am wondering if there is a way that I can use different learning rate for different layers like what is in Caffe. I am trying to modify a pre-trained model and use it for other tasks. What I want is to speed up the training for new added layers and keep the trained layers at low learning rate in order to prevent them from being distorted. for example, I have a 5-conv-layer pre-trained model. Now I add a new conv layer and fine tune it. The first 5 layers would have learning rate of 0.00001 and the last one would have 0.001. Any idea how to achieve this?
I’m working on a image class-incremental classifier approach using a CNN as a feature extractor and a fully-connected block for classifying.
I’m trying to train a network with an unbalanced data. I have A (198 samples), B (436 samples), C (710 samples), D (272 samples) and I have read about the “weighted_cross_entropy_with_logits” but all the examples I found are for binary classification so I’m not very confident in how to set those weights.
When should I use .eval()? I understand it is supposed to allow me to “evaluate my model”. How do I turn it back off for training?
The code that I have (that I can’t change) uses the Resnet with my_input_tensor as the input_tensor.
I’ve gone through the official doc. I’m having a hard time understanding what this function is used for and how it works. Can someone explain this in layman’s terms?
After Training, I saved Both Keras whole Model and Only Weights using
In Keras (with Tensorflow backend), is the current input pattern available to my custom loss function?