69f5869f3b6d190ed99e156a932634393ab361dd,python/dgl/nn/pytorch/conv/gatconv.py,GATConv,__init__,#GATConv#Any#Any#Any#Any#Any#Any#Any#Any#,47

Before Change

        self.attn_l = nn.Parameter(th.FloatTensor(size=(1, num_heads, out_feats)))
        self.attn_r = nn.Parameter(th.FloatTensor(size=(1, num_heads, out_feats)))
        self.feat_drop = nn.Dropout(feat_drop)
        self.attn_drop = nn.Dropout(attn_drop)
        self.leaky_relu = nn.LeakyReLU(negative_slope)
        if residual:
            if self._in_dst_feats != out_feats:
                self.res_fc = nn.Linear(
                    self._in_dst_feats, num_heads * out_feats, bias=False)
            else:
                self.res_fc = Identity()
        else:
            self.register_buffer("res_fc", None)
        self.reset_parameters()
        self.activation = activation

    def reset_parameters(self):
        Reinitialize learnable parameters.

After Change

            [ 0.5041, -1.3025],
            [ 0.6568,  0.7048]],
            [[-0.2688,  1.0543],
            [-0.0315, -0.9016],
            [ 0.3943,  0.5347]],
            [[-0.6066,  1.0268],
            [-0.5945, -0.4801],
            [ 0.1594,  0.3825]]], grad_fn=<BinaryReduceBackward>)
    
    def __init__(self,
                 in_feats,
                 out_feats,
                 num_heads,
                 feat_drop=0.,
                 attn_drop=0.,
                 negative_slope=0.2,
                 residual=False,
                 activation=None,
                 allow_zero_in_degree=False):
        super(GATConv, self).__init__()
        self._num_heads = num_heads
        self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
        self._out_feats = out_feats
        self._allow_zero_in_degree = allow_zero_in_degree

Instances