knowledge Transfer

PyTorch provides many classes to make data loading easy and code more readable. In this tutorial, we will see how to load and preprocess/augment custom datasets.

Network would already be able to extract generic features from your dataset. The network will not have to learn to extract generic features from scratch.

It is sometimes desirable to use a separate penalty with a different coefficient for each layer of the network.

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.