Weight Decay parameter for SGD optimizer in PyTorch
L2 regularization is also referred to as weight decay. The reason for this name is that thinking about SGD and backpropagation, the negative gradient of the L2 regularization term with respect to a parameter w_i is – 2 * lambda * w_i, where lambda is the aforementioned hyperparameter, simply named weight decay in PyTorch.
How loss.backward(), optimizer.step() and optimizer.zero_grad() related in PyTorch
When we call loss.backward(), PyTorch traverses this graph in the reverse direction to compute the gradients and accumulate their values in the grad attribute of those tensors (the leaf nodes of the graph).
Access PyTorch model weights and bise with its name and ‘requires_grad value’
The parameters method to ask any nn.Module for a list of parameters owned by it or any of its submodules. Calling model.parameters() will collect weight and bias from modules. It’s instructive to inspect the parameters in this case by printing their shapes.
Use Image Dataset from Directory with and without Label List in Keras
All images for training are located in one folder and the target labels are in a CSV file. It should be possible to use a list of labels instead of inferring the classes from the directory structure. We have a list of labels corresponding number of files in the directory.
Use Saved PyTorch model to predict single and multiple images.
we will focus on writing the inference code for the single sample image. This will involve two parts, one where we prepare the image so that it can be fed to ResNet, and next, we will write the code to get the actual prediction from the model.