How to deal with an imbalanced dataset using WeightedRandomSampler in PyTorch.
We use something called samplers for OverSampling. Though we did not use samplers exclusively, PyTorch used it for us internally. When we say shuffle=False, PyTorch ended up using SequentialSampler it gives an index from zero to the length of the dataset. When shuffle=True it ends up using a RandomSampler.
How to modify pre-train PyTorch model for Finetuning and Feature Extraction?
classification layer of the pre-trained model is specific to the original classification task, and subsequently specific to the set of classes on which the model was trained. You simply add a new classifier layer, which will be trained from scratch.
How to use class weight in CrossEntropyLoss for an imbalanced dataset?
how to create a loss function for an imbalanced dataset in which minority class proportionally to its underrepresentation. You will use PyTorch to define the loss function and class weights to help the model learn from the imbalanced data.
How to save Keras training History object to File using Callback?
You can learn a lot about Keras models by observing their History objects after training. In this post, you will discover how you can save the history object into a CSV file of deep learning models training metrics over time during training.