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How is fine tuning similar to training a model?
As per my understanding: You train a model on a dataset, use it for training on another dataset. This is fine tuning. This is the same as feature extraction from the first trained model, like in feature extraction also you take the first model and train it on a new dataset.
How is fine tuning used in transfer learning?
They will learn to turn the old features into predictions on a new dataset. Train the new layers on your dataset. A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate.
How many cats are in the ImageNet dataset?
This is pre-trained on the ImageNet dataset, a large dataset consisting of 1.4M images and 1000 classes. ImageNet is a research training dataset with a wide variety of categories like jackfruit and syringe. This base of knowledge will help us classify cats and dogs from our specific dataset.
What’s the difference between feature extraction and fine tuning?
It distinguishes between feature extraction and fine tuning in deep learning. I am not getting the difference as feature extraction is just the same as fine tuning: You train a model on a dataset, use it for training on another dataset. This is fine tuning.
When do weights change in fine tuning strategy?
As shown in figure 2 of {1}, in the fine-tuning strategy all weights are changed when training on the new task (except for the weights of the last layers for the original task), whereas in the feature extraction strategy only the weights of the newly added last layers change during the training phase: {1} Li, Zhizhong, and Derek Hoiem.
What did we learn last time in model tuning?
Last time in Model Tuning (Part 1 – Train/Test Split) we discussed training error, test error, and train/test split. We learned that training a model on all the available data and then testing on that very same data is an awful way to build models because we have no indication as to how well that model will perform on unseen data.