Source code for catalyst.utils.initialization

from typing import Callable

from torch import nn

    None: "sigmoid",
    nn.Sigmoid: "sigmoid",
    nn.Tanh: "tanh",
    nn.ReLU: "relu",
    nn.LeakyReLU: "leaky_relu",
    nn.ELU: "relu",

def _nonlinearity2name(nonlinearity):
    if isinstance(nonlinearity, nn.Module):
        nonlinearity = nonlinearity.__class__
    nonlinearity = ACTIVATIONS.get(nonlinearity, nonlinearity)
    nonlinearity = nonlinearity.lower()
    return nonlinearity

[docs]def get_optimal_inner_init( nonlinearity: nn.Module, **kwargs ) -> Callable[[nn.Module], None]: """ Create initializer for inner layers based on their activation function (nonlinearity). Args: nonlinearity: non-linear activation """ nonlinearity: str = _nonlinearity2name(nonlinearity) assert isinstance(nonlinearity, str) if nonlinearity in ["sigmoid", "tanh"]: weignt_init_fn = nn.init.xavier_uniform_ init_args = kwargs elif nonlinearity in ["relu", "leaky_relu"]: weignt_init_fn = nn.init.kaiming_normal_ init_args = {**{"nonlinearity": nonlinearity}, **kwargs} else: raise NotImplementedError def inner_init(layer): if isinstance(layer, (nn.Linear, nn.Conv1d, nn.Conv2d)): weignt_init_fn(, **init_args) if layer.bias is not None: nn.init.zeros_( return inner_init
[docs]def outer_init(layer: nn.Module) -> None: """ Initialization for output layers of policy and value networks typically used in deep reinforcement learning literature. """ if isinstance(layer, (nn.Linear, nn.Conv1d, nn.Conv2d)): v = 3e-3 nn.init.uniform_(, -v, v) if layer.bias is not None: nn.init.uniform_(, -v, v)
__all__ = ["get_optimal_inner_init", "outer_init"]