MetaMultiheadAttention

torchmeta.modules.MetaMultiheadAttention(*args, **kwargs)

Notes

See: torch.nn.MultiheadAttention

MetaBatchNorm1d

torchmeta.modules.MetaBatchNorm1d(num_features, eps=1e-05, momentum=0.1,
    affine=True, track_running_stats=True)

Notes

See: torch.nn.BatchNorm1d

MetaBatchNorm2d

torchmeta.modules.MetaBatchNorm2d(num_features, eps=1e-05, momentum=0.1,
    affine=True, track_running_stats=True)

Notes

See: torch.nn.BatchNorm2d

MetaBatchNorm3d

torchmeta.modules.MetaBatchNorm3d(num_features, eps=1e-05, momentum=0.1,
    affine=True, track_running_stats=True)

Notes

See: torch.nn.BatchNorm3d

MetaSequential

torchmeta.modules.MetaSequential(*args:Any)

Notes

See: torch.nn.Sequential

MetaConv1d

torchmeta.modules.MetaConv1d(in_channels:int, out_channels:int,
    kernel_size:Union[int, Tuple[int]], stride:Union[int, Tuple[int]]=1,
    padding:Union[int, Tuple[int]]=0, dilation:Union[int, Tuple[int]]=1,
    groups:int=1, bias:bool=True, padding_mode:str='zeros')

Notes

See: torch.nn.Conv1d

MetaConv2d

torchmeta.modules.MetaConv2d(in_channels:int, out_channels:int,
    kernel_size:Union[int, Tuple[int, int]], stride:Union[int, Tuple[int,
    int]]=1, padding:Union[int, Tuple[int, int]]=0, dilation:Union[int,
    Tuple[int, int]]=1, groups:int=1, bias:bool=True,
    padding_mode:str='zeros')

Notes

See: torch.nn.Conv2d

MetaConv3d

torchmeta.modules.MetaConv3d(in_channels:int, out_channels:int,
    kernel_size:Union[int, Tuple[int, int, int]], stride:Union[int, Tuple[int,
    int, int]]=1, padding:Union[int, Tuple[int, int, int]]=0,
    dilation:Union[int, Tuple[int, int, int]]=1, groups:int=1, bias:bool=True,
    padding_mode:str='zeros')

Notes

See: torch.nn.Conv3d

MetaLinear

torchmeta.modules.MetaLinear(in_features:int, out_features:int,
    bias:bool=True) -> None

Notes

See: torch.nn.Linear

MetaBilinear

torchmeta.modules.MetaBilinear(in1_features:int, in2_features:int,
    out_features:int, bias:bool=True) -> None

Notes

See: torch.nn.Bilinear

MetaModule

Base class for PyTorch meta-learning modules. These modules accept an additional argument params in their forward method.

torchmeta.modules.MetaModule()

Notes

Objects inherited from MetaModule are fully compatible with PyTorch modules from torch.nn.Module. The argument params is a dictionary of tensors, with full support of the computation graph (for differentiation).

MetaLayerNorm

torchmeta.modules.MetaLayerNorm(normalized_shape:Union[int, List[int],
    torch.Size], eps:float=1e-05, elementwise_affine:bool=True) -> None

Notes

See: torch.nn.LayerNorm

DataParallel

torchmeta.modules.DataParallel(module, device_ids=None, output_device=None,
    dim=0)

Notes

See: torch.nn.Parallel

MetaEmbedding

torchmeta.modules.MetaEmbedding(num_embeddings:int, embedding_dim:int,
    padding_idx:Union[int, NoneType]=None, max_norm:Union[float,
    NoneType]=None, norm_type:float=2.0, scale_grad_by_freq:bool=False,
    sparse:bool=False, _weight:Union[torch.Tensor, NoneType]=None) -> None

Notes

See: torch.nn.Embedding

MetaEmbeddingBag

torchmeta.modules.MetaEmbeddingBag(num_embeddings:int, embedding_dim:int,
    max_norm:Union[float, NoneType]=None, norm_type:float=2.0,
    scale_grad_by_freq:bool=False, mode:str='mean', sparse:bool=False,
    _weight:Union[torch.Tensor, NoneType]=None,
    include_last_offset:bool=False) -> None

Notes

See: torch.nn.EmbeddingBag