How do I initialize weights in PyTorch?
How do I initialize weights and biases of a network (via e.g. He or Xavier initialization)?

Single layer
To initialize the weights of a single layer, use a function from torch.nn.init. For instance:
conv1 = torch.nn.Conv2d(...)
torch.nn.init.xavier_uniform(conv1.weight)

Alternatively, you can modify the parameters by writing to conv1.weight.data (which is a torch.Tensor). Example:
conv1.weight.data.fill_(0.01)

The same applies for biases:
conv1.bias.data.fill_(0.01)

nn.Sequential or custom nn.Module
Pass an initialization function to torch.nn.Module.apply. It will initialize the weights in the entire nn.Module recursively.

apply(fn): Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also torch-nn-init).

Example:
def init_weights(m):
    if isinstance(m, nn.Linear):
        torch.nn.init.xavier_uniform(m.weight)
        m.bias.data.fill_(0.01)

net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
net.apply(init_weights)

