Micro and Macro Averages for imbalance multiclass classification
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.
How to change the learning rate in the PyTorch using Learning Rate Scheduler?
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.
Filters, kernel size, input shape in Conv2d layer
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”.