deepmd.pt.model.network.layernorm

deepmd.pt.model.network.layernorm#

Attributes#

device

Classes#

LayerNorm

Functions#

empty_t(→ torch.Tensor)

Module Contents#

deepmd.pt.model.network.layernorm.device[source]#

deepmd.pt.model.network.layernorm.empty_t(shape: tuple[int, Ellipsis], precision: torch.dtype) → torch.Tensor[source]#

class deepmd.pt.model.network.layernorm.LayerNorm(num_in: int, eps: float = 1e-05, uni_init: bool = True, bavg: float = 0.0, stddev: float = 1.0, precision: str = DEFAULT_PRECISION, trainable: bool = True, seed: int | list[int] | None = None)[source]#

Bases: torch.nn.Module

eps = 1e-05[source]#

uni_init = True[source]#

num_in[source]#

precision = 'float64'[source]#

prec[source]#

matrix[source]#

bias[source]#

trainable = True[source]#

dim_out() → int[source]#

forward(xx: torch.Tensor) → torch.Tensor[source]#

One Layer Norm used by DP model.

Parameters:

xxtorch.Tensor: The input of index.

Returns:

yy: torch.Tensor: The output.

serialize() → dict[source]#

Serialize the layer to a dict.

Returns:

dict: The serialized layer.

classmethod deserialize(data: dict) → LayerNorm[source]#

Deserialize the layer from a dict.

Parameters:

datadict: The dict to deserialize from.