Hi all, I am new to deep learning. Kindly help me on this.

I have trained and saved a model

learn.save('trn_val=0.2_tst_res50-1')

and now, I need to create a new learner with the weights from the saved model. The new learner is something like the following learn = cnn_learner(data_1, models.resnet50, metrics = accuracy)

Hi @nbharatula ,
I should first load using learn.load( 'trn_val=0.2_tst_res50-1') and then training a new model like learn_new = cnn_learner(data_1, models.resnet50, metrics = accuracy) so that my new learner learn_new will start learning with the weights developed by the learner ‘learn’ right?

Create the new learner first then load the weights to the new learner. Does this new learner have a significant difference from the old? Same architecture? Same number of classes?

Thanks.
Just for clarification,
Should I first generate a new learner like learn_new = cnn_learner(data_1, models.resnet50, metrics = accuracy)
then learn.load('learn_old')
and then training further with learn_new.fit(data_1, models.resnet50, metrics = accuracy) ?

Yes, sorry. That (learn.fit) was a mistake in typing. So, learn_new = cnn_learner(data_1, models.resnet50, metrics = accuracy) learn_new.load('learn_old') learn_new.fit(x)