There is the torch_geometric.utils.softmax . torch_geometric.utils._softmax — pytorch_geometric documentation
Computes a sparsely evaluated softmax. dropout_adj. Randomly drops edges from the adjacency matrix (edge_index, edge_attr) torch_geometric.utils — pytorch_geometric 1.7.1 documentation
Using an attention pooling for node features · pyg-team PyTorch Geometric provides a softmax function ( torch_geometric.utils.softmax ) that normalizes inputs across the same target nodes. This
torch_geometric.utils.softmax — pytorch_geometric 1.3.1 torch_geometric.utils.softmax — pytorch_geometric documentation CrossEntropyLoss with Pytorch Geometric · Issue #1872 · pyg-team
softmax individually for each group. Parameters. src (Tensor) – The source tensor. index (LongTensor) – The indices of elements for applying the softmax. torch_geometric.utils import scatter, segment from torch_geometric.utils.num_nodes import maybe_num_nodes softmax(src, index) tensor([0.5000, 0.5000, 1.0000,
Computes a sparsely evaluated softmax. Given a value tensor :attr:`src`, this function first groups the values along the first dimension based on the indices Source code for torch_geometric.utils.softmax. from torch_scatter import scatter_max, scatter_add from .num_nodes import maybe_num_nodes. [docs]def softmax(src, pytorch - Implementing a softmax attention pooling in a graph neural
import torch from torch_geometric.utils import softmax from torch_geometric.nn.pool import global_mean_pool from torch_geometric.data import Computes the (unweighted) degree of a given one-dimensional index tensor. softmax. Computes a sparsely evaluated softmax. lexsort.
torch_geometric.utils — pytorch_geometric documentation torch_geometric.utils — pytorch_geometric 1.4.3 documentation softmax "within" will be unaware of this, and not compute the We provide torch_geometric.utils.softmax for this use-case. e,g.: x
Questions on the GAT conv layer · Issue #1851 · pyg-team