fynance.models.neural_network.MultiLayerPerceptron.apply¶
-
MultiLayerPerceptron.
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).- Args:
- fn (
Module
-> None): function to be applied to each submodule - Returns:
- Module: self
Example:
>>> def init_weights(m): print(m) if type(m) == nn.Linear: m.weight.data.fill_(1.0) print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )