Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.GENConv.html below:

torch_geometric.nn.conv.GENConv — pytorch_geometric documentation

class GENConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, aggr: Optional[Union[str, List[str], Aggregation]] = 'softmax', t: float = 1.0, learn_t: bool = False, p: float = 1.0, learn_p: bool = False, msg_norm: bool = False, learn_msg_scale: bool = False, norm: str = 'batch', num_layers: int = 2, expansion: int = 2, eps: float = 1e-07, bias: bool = False, edge_dim: Optional[int] = None, **kwargs)[source]

Bases: MessagePassing

The GENeralized Graph Convolution (GENConv) from the “DeeperGCN: All You Need to Train Deeper GCNs” paper.

GENConv supports both \(\textrm{softmax}\) (see SoftmaxAggregation) and \(\textrm{powermean}\) (see PowerMeanAggregation) aggregation. Its message construction is given by:

\[\mathbf{x}_i^{\prime} = \mathrm{MLP} \left( \mathbf{x}_i + \mathrm{AGG} \left( \left\{ \mathrm{ReLU} \left( \mathbf{x}_j + \mathbf{e_{ji}} \right) +\epsilon : j \in \mathcal{N}(i) \right\} \right) \right)\]

Parameters:

• in_channels (int or tuple) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.

• out_channels (int) – Size of each output sample.

• aggr (str or Aggregation, optional) – The aggregation scheme to use. Any aggregation of torch_geometric.nn.aggr can be used, ("softmax", "powermean", "add", "mean", max). (default: "softmax")

• t (float, optional) – Initial inverse temperature for softmax aggregation. (default: 1.0)

• learn_t (bool, optional) – If set to True, will learn the value t for softmax aggregation dynamically. (default: False)

• p (float, optional) – Initial power for power mean aggregation. (default: 1.0)

• learn_p (bool, optional) – If set to True, will learn the value p for power mean aggregation dynamically. (default: False)

• msg_norm (bool, optional) – If set to True, will use message normalization. (default: False)

• learn_msg_scale (bool, optional) – If set to True, will learn the scaling factor of message normalization. (default: False)

• norm (str, optional) – Norm layer of MLP layers ("batch", "layer", "instance") (default: batch)

• num_layers (int, optional) – The number of MLP layers. (default: 2)

• expansion (int, optional) – The expansion factor of hidden channels in MLP layers. (default: 2)

• eps (float, optional) – The epsilon value of the message construction function. (default: 1e-7)

• bias (bool, optional) – If set to False, the layer will not learn an additive bias. (default: True)

• edge_dim (int, optional) – Edge feature dimensionality. If set to None, Edge feature dimensionality is expected to match the out_channels. Other-wise, edge features are linearly transformed to match out_channels of node feature dimensionality. (default: None)

• **kwargs (optional) – Additional arguments of torch_geometric.nn.conv.GenMessagePassing.

Shapes:

• input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\), edge attributes \((|\mathcal{E}|, D)\) (optional)

• output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V}_t|, F_{out})\) if bipartite

forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: Union[Tensor, SparseTensor], edge_attr: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None) → Tensor[source]

Runs the forward pass of the module.

Return type:

Tensor

reset_parameters()[source]

Resets all learnable parameters of the module.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4