69f5869f3b6d190ed99e156a932634393ab361dd,python/dgl/nn/pytorch/conv/sageconv.py,SAGEConv,__init__,#SAGEConv#Any#Any#Any#Any#Any#Any#Any#,50

Before Change

        // aggregator type: mean/pool/lstm/gcn
        if aggregator_type == "pool":
            self.fc_pool = nn.Linear(self._in_src_feats, self._in_src_feats)
        if aggregator_type == "lstm":
            self.lstm = nn.LSTM(self._in_src_feats, self._in_src_feats, batch_first=True)
        if aggregator_type != "gcn":
            self.fc_self = nn.Linear(self._in_dst_feats, out_feats, bias=bias)
        self.fc_neigh = nn.Linear(self._in_src_feats, out_feats, bias=bias)
        self.reset_parameters()

After Change

            self.lstm = nn.LSTM(self._in_src_feats, self._in_src_feats, batch_first=True)
        if aggregator_type != "gcn":
            self.fc_self = nn.Linear(self._in_dst_feats, out_feats, bias=bias)
        self.fc_neigh = nn.Linear(self._in_src_feats, out_feats, bias=bias)
        self.reset_parameters()

    def reset_parameters(self):
        r

        Description
        -----------
        Reinitialize learnable parameters.

        Notes
        -----
        The linear weights :math:`W^{(l)}` are initialized using Glorot uniform initialization.
        The LSTM module is using xavier initialization method for its weights.
        
        gain = nn.init.calculate_gain("relu")
        if self._aggre_type == "pool":
            nn.init.xavier_uniform_(self.fc_pool.weight, gain=gain)
        if self._aggre_type == "lstm":
            self.lstm.reset_parameters()
        if self._aggre_type != "gcn":
            nn.init.xavier_uniform_(self.fc_self.weight, gain=gain)
        nn.init.xavier_uniform_(self.fc_neigh.weight, gain=gain)

    def _lstm_reducer(self, nodes):

Instances