From the course: Transfer Learning for Images Using PyTorch: Essential Training
Unlock the full course today
Join today to access over 22,600 courses taught by industry experts or purchase this course individually.
Train the extractor
From the course: Transfer Learning for Images Using PyTorch: Essential Training
Train the extractor
- [Instructor] So I'm going to expand the Training the Fixed Feature Extractor section. We're going to be using the Adam optimizer, and the second line sends our model to the GPU. And, finally, we add our model parameters as arguments to the Adam optimizer. Now, the next section of code is Training the Fixed Feature Extractor, which is what we've looked at. So I'm going to run this cell. So this should take between five and 10 minutes. So now that we've completed the section, I'm going to close it off.
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Contents
-
-
-
-
Creating a fixed feature extractor5m 30s
-
(Locked)
Understanding loss: CrossEntropyLoss() and NLLLoss()3m 37s
-
(Locked)
Autograd1m 33s
-
(Locked)
Using autograd4m 9s
-
(Locked)
Training the fixed feature extractor3m 24s
-
(Locked)
Optimizers1m 49s
-
(Locked)
CPU to GPU59s
-
(Locked)
Train the extractor37s
-
(Locked)
Evaluate the network and viewing images2m 22s
-
(Locked)
Viewing images and normalization5m 52s
-
(Locked)
Accuracy of the model2m 40s
-
-
-
-