Source code for pykeen.nn.perceptron

# -*- coding: utf-8 -*-

"""Perceptron-like modules."""

from typing import Optional, Union

import torch
from torch import nn

__all__ = [

[docs]class ConcatMLP(nn.Sequential): """A 2-layer MLP with ReLU activation and dropout applied to the flattened token representations. This is for conveniently choosing a configuration similar to the paper. For more complex aggregation mechanisms, pass an arbitrary callable instead. .. seealso:: """ def __init__( self, input_dim: int, output_dim: Optional[int] = None, dropout: float = 0.1, ratio: Union[int, float] = 2, flatten_dims: int = 2, ): """Initialize the module. :param input_dim: the input dimension :param output_dim: the output dimension. defaults to input dim :param dropout: the dropout value on the hidden layer :param ratio: the ratio of the output dimension to the hidden layer size. :param flatten_dims: the number of trailing dimensions to flatten """ output_dim = output_dim or input_dim hidden_dim = int(ratio * output_dim) super().__init__( nn.Linear(input_dim, hidden_dim), nn.Dropout(dropout), nn.ReLU(), nn.Linear(hidden_dim, output_dim), ) self.flatten_dims = flatten_dims
[docs] def forward(self, xs: torch.FloatTensor, dim: int) -> torch.FloatTensor: """Forward the MLP on the given dimension. :param xs: The tensor to forward :param dim: Only a parameter to match the signature of torch.mean / torch.sum this class is not thought to be usable from outside :returns: The tensor after applying this MLP """ assert dim == -2 return super().forward(xs.view(*xs.shape[: -self.flatten_dims], -1))