# torch_geometric.nn.aggr.SoftmaxAggregation

class SoftmaxAggregation(t: float = 1.0, learn: bool = False, semi_grad: bool = False, channels: int = 1)[source]

Bases: Aggregation

The softmax aggregation operator based on a temperature term, as described in the “DeeperGCN: All You Need to Train Deeper GCNs” paper

$\mathrm{softmax}(\mathcal{X}|t) = \sum_{\mathbf{x}_i\in\mathcal{X}} \frac{\exp(t\cdot\mathbf{x}_i)}{\sum_{\mathbf{x}_j\in\mathcal{X}} \exp(t\cdot\mathbf{x}_j)}\cdot\mathbf{x}_{i},$

where $$t$$ controls the softness of the softmax when aggregating over a set of features $$\mathcal{X}$$.

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

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

• semi_grad (bool, optional) – If set to True, will turn off gradient calculation during softmax computation. Therefore, only semi-gradients are used during backpropagation. Useful for saving memory and accelerating backward computation when t is not learnable. (default: False)

• channels (int, optional) – Number of channels to learn from $$t$$. If set to a value greater than 1, $$t$$ will be learned per input feature channel. This requires compatible shapes for the input to the forward calculation. (default: 1)

reset_parameters()[source]

Resets all learnable parameters of the module.

forward(x: Tensor, index: = None, ptr: = None, dim_size: = None, dim: int = -2) [source]
Parameters
• x (torch.Tensor) – The source tensor.

• index (torch.Tensor, optional) – The indices of elements for applying the aggregation. One of index or ptr must be defined. (default: None)

• ptr (torch.Tensor, optional) – If given, computes the aggregation based on sorted inputs in CSR representation. One of index or ptr must be defined. (default: None)

• dim_size (int, optional) – The size of the output tensor at dimension dim after aggregation. (default: None)

• dim (int, optional) – The dimension in which to aggregate. (default: -2)