Source code for cogdl.layers.mlp_layer

import torch.nn as nn
import torch.nn.functional as F

from cogdl.utils import get_activation

[docs]class MLP(nn.Module): r"""Multilayer perception with normalization .. math:: x^{(i+1)} = \sigma(W^{i}x^{(i)}) Parameters ---------- in_feats : int Size of each input sample. out_feats : int Size of each output sample. hidden_dim : int Size of hidden layer dimension. use_bn : bool, optional Apply batch normalization if True, default: `True). """ def __init__( self, in_feats, out_feats, hidden_size, num_layers, dropout=0.0, activation="relu", norm=None, act_first=False, bias=True, ): super(MLP, self).__init__() self.norm = norm self.activation = get_activation(activation) self.act_first = act_first self.dropout = dropout shapes = [in_feats] + [hidden_size] * (num_layers - 1) + [out_feats] self.mlp = nn.ModuleList( [nn.Linear(shapes[layer], shapes[layer + 1], bias=bias) for layer in range(num_layers)] ) if norm is not None and num_layers > 1: if norm == "layernorm": self.norm_list = nn.ModuleList(nn.LayerNorm(x) for x in shapes[1:-1]) elif norm == "batchnorm": self.norm_list = nn.ModuleList(nn.BatchNorm1d(x) for x in shapes[1:-1]) else: raise NotImplementedError(f"{norm} is not implemented in CogDL.") self.reset_parameters()
[docs] def reset_parameters(self): for layer in self.mlp: layer.reset_parameters() if hasattr(self, "norm_list"): for n in self.norm_list: n.reset_parameters()
[docs] def forward(self, x): for i, fc in enumerate(self.mlp[:-1]): x = fc(x) if self.act_first: x = self.activation(x, inplace=True) if self.norm: x = self.norm_list[i](x) if not self.act_first: x = self.activation(x, inplace=True) x = F.dropout(x, p=self.dropout, x = self.mlp[-1](x) return x