torch_geometric.nn.models.AttentiveFP

class AttentiveFP(in_channels: int, hidden_channels: int, out_channels: int, edge_dim: int, num_layers: int, num_timesteps: int, dropout: float = 0.0)

Bases: Module

Attentive FP模型用于分子表示学习，源自 “Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism” 论文，基于 图注意力机制。

Parameters:

• in_channels (int) – Size of each input sample.

• hidden_channels (int) – 隐藏节点特征的维度。

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

• edge_dim (int) – 边缘特征维度。

• num_layers (int) – GNN层的数量。

• num_timesteps (int) – 全局读取的迭代细化步骤数。

• dropout (float, optional) – Dropout概率。（默认值：0.0）

forward(x: Tensor, edge_index: Tensor, edge_attr: Tensor, batch: Tensor) → Tensor

Return type:

Tensor

reset_parameters()

重置模块的所有可学习参数。