About the gating mechanism

Hi @svjan5 , After reading your source code, I have found some places that make me confused. In the paper, the formula you mentioned is like: And where: while in your code, it is like this:

with tf.name_scope("in_arcs-%s_name-%s_layer-%d" % (lbl, name, layer)):
	inp_in     = tf.tensordot(gcn_in, w_in, axes=[2,0]) + tf.expand_dims(b_in, axis=0)
	adj_matrix = tf.transpose(adj_mat[lbl], [0,2,1])
	in_t 	   = self.aggregate(inp_in, adj_matrix)							
	if self.p.dropout != 1.0: in_t    = tf.nn.dropout(in_t, keep_prob=self.p.dropout)
	if w_gating:
		inp_gin = tf.tensordot(gcn_in, tf.sigmoid(w_gin), axes=[2,0]) + tf.expand_dims(b_gin, axis=0)
		in_act  = self.aggregate(inp_gin, adj_matrix)
	else:
		in_act   = in_t

It seems to me that the calculated in_t or inp_in is never used when enable gating which might not align with the formula where there is a multiplication in between. And the weight w_in and w_out would never be updated in the code. May you please give me some information how the calculated in_t or the w_in and w_out are used under gating mechanism in your code?

Many thanks.

[Punchwes]

Hi @svjan5 , I have created a pull request with changes I mentioned above. Please have a check.

Many thanks

[svjan5]

Thanks for your help!