knowledge Transfer

You need to reset the weights of the model so that each cross-validation fold starts from some random initial state and not learning from the previous folds. You could call reset_parameters() on all child modules.

A macro-average will compute the metric independently for each class and then take the average hence treating all classes equally, whereas a micro-average will aggregate the contributions of all classes to compute the average metric.

Global Average Pooling does something different. It applies average pooling on the spatial dimensions until each spatial dimension is one, and leaves other dimensions unchanged.

The optimal learning rate will be dependent on both your model architecture and your dataset. While using a default learning rate may provide decent results, you can often improve the performance or speed up training by searching for an optimal learning rate.

We don’t explicitly define the filters that our convolutional layer will use, instead parameterize the filters and let the network learn the best filters to use during training. We need to define “how many filters we’ll use at each layer”.

The choice of loss function must specific to the problem, such as binary, multi-class, or multi-label classification. Further, the configuration of the output layer must also be appropriate for the chosen loss function.

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.

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 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.

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.