I’m using transfer learning with k-fold cross validation. k=9 and k folds are being generated using sklearn. The problem i’m facing is that after evaluation of first fold performance decreases drastically. In first fold i get 54% validation accuracy and after that even the training accuracy doesn’t exceed 4% for all the remaining folds.
First guess was that it is probably because of data difference in all folds. So i have run the model multiple times but this behavior still exists. It doesn’t seem to be because of data as new run generates shuffled new data for all folds. I’m completely out of ideas why its happening. Any help is appreciated. Thank You.
Blockquote
def loadModel():
model = sphere20a(classnum=1000).to(device)
model_state = model.state_dict()
for name, param in model.named_parameters():
if (name[:4] == ‘conv’ or name[:4] == ‘relu’) and name[4] is not ‘4’:
param.requires_grad = False
pretrained_state = torch.load("/kaggle/input/kinmodel/sphere20a_20171020.pth")
pretrained_state = {k:v for k,v in pretrained_state.items() if k in model_state and v.size() == model_state[k].size()}
model_state.update(pretrained_state)
model.load_state_dict(model_state)
return model
training loop.
for train_index, val_index in kf.split(data):
n_epochs=10
learn_rate = 0.01
counter = counter + 1
model = loadModel() #load new model everytime and discard old one
I am deleting the old model after every fold completes. using del model
