I have a model architecture. I have saved the entire model using torch.save() for some n number of iterations. I want to run another iteration of my code by using the pre-trained weights of the model I saved previously.
Edit: I want the weight initialization for the new iteration be done from the weights of the pretrained model
Right now, I do something like:
# default_lr = 5
# default_weight_decay = 0.001
# model_io = the pretrained model in .pth format
model = torch.load(model_io) 
optim = torch.optim.Adam(model.parameters(),lr=default_lr, weight_decay=default_weight_decay)  
loss_new = BCELoss()  
epochs = default_epoch 
.
.
training_loop():
....
outputs = model(input)
....
.
#similarly for test loop

The architecture and everything of my new iteration is same right now but might want to fiddle in future.
Am I missing something? I have to run for a very long epoch for a huge number of sample so can not afford to wait to see the results then figure out things.
Thank you!