tf.Tensor
The output feature
graph = graph.local_var()
if self._norm == "both":
degs = tf.clip_by_value(tf.cast(graph.out_degrees(), tf.float32),
clip_value_min=1,
clip_value_max=np.inf)
norm = tf.pow(degs, -0.5)
shp = norm.shape + (1,) * (feat.ndim - 1)
norm = tf.reshape(norm, shp)
feat = feat * norm
if weight is not None:
if self.weight is not None:
raise DGLError("External weight is provided while at the same time the"
" module has defined its own weight parameter. Please"
" create the module with flag weight=False.")
else:
weight = self.weight
if self._in_feats > self._out_feats:
// mult W first to reduce the feature size for aggregation.
if weight is not None:
feat = tf.matmul(feat, weight)
graph.srcdata["h"] = feat
graph.update_all(fn.copy_src(src="h", out="m"),
fn.sum(msg="m", out="h"))
rst = graph.dstdata["h"]
else:
// aggregate first then mult W
graph.srcdata["h"] = feat
graph.update_all(fn.copy_src(src="h", out="m"),
fn.sum(msg="m", out="h"))
rst = graph.dstdata["h"]
if weight is not None:
rst = tf.matmul(rst, weight)
if self._norm != "none":
degs = tf.clip_by_value(tf.cast(graph.in_degrees(), tf.float32),
clip_value_min=1,
clip_value_max=np.inf)
if self._norm == "both":
norm = tf.pow(degs, -0.5)
else:
norm = 1.0 / degs
shp = norm.shape + (1,) * (feat.ndim - 1)
norm = tf.reshape(norm, shp)
rst = rst * norm
if self.bias is not None:
rst = rst + self.bias
if self._activation is not None:
rst = self._activation(rst)
return rst
def extra_repr(self):
Set the extra representation of the module,
which will come into effect when printing the model.
