remove text using code line numbers (#217)

tutorials/models/4_rgcn.py

@@ -125,6 +125,7 @@ base. This tutorial shows how to implement R-GCN with DGL.
 #    Each relation type is associated with a different weight. Therefore,
 #    the full weight matrix has three dimensions: relation, input_feature,
 #    output_feature.
+#
 
 import torch
 import torch.nn as nn
@@ -149,10 +150,11 @@ class RGCNLayer(nn.Module):
         if self.num_bases <= 0 or self.num_bases > self.num_rels:
             self.num_bases = self.num_rels
 
-        # add weights
+        # weight bases in equation (3)
         self.weight = nn.Parameter(torch.Tensor(self.num_bases, self.in_feat,
                                                 self.out_feat))
         if self.num_bases < self.num_rels:
+            # linear combination coefficients in equation (3)
             self.w_comp = nn.Parameter(torch.Tensor(self.num_rels, self.num_bases))
 
         # add bias
@@ -171,7 +173,7 @@ class RGCNLayer(nn.Module):
 
     def forward(self, g):
         if self.num_bases < self.num_rels:
-            # generate all weights from basis (equation (3))
+            # generate all weights from bases (equation (3))
             weight = self.weight.view(self.in_feat, self.num_bases, self.out_feat)
             weight = torch.matmul(self.w_comp, weight).view(self.num_rels,
                                                         self.in_feat, self.out_feat)
@@ -204,19 +206,6 @@ class RGCNLayer(nn.Module):
 
 
 ###############################################################################
-# As mentioned above, R-GCN uses decomposition of reduce parameter size
-# (equation (3)). So line 18-19 defines the weight bases (:math:`V_b^{(l)}`),
-# and line 20-21 defines the linear combination coefficients
-# (:math:`a_{rb}^{(l)}`). The forward function of R-GCN layer is similar to
-# GCN, except that at the beginning of forward phase, weights for each
-# relation type is generated from bases (line 41-44).
-#
-# The message function for R-GCN replicates weights onto edges and then
-# generates messages (line 55-59). But for the first layer, since the node
-# feature is the node id, the transformation from node feature to messages
-# can be computed more efficiently by performing an embedding lookup (line
-# 49-53).
-#
 # Define full R-GCN model
 # ~~~~~~~~~~~~~~~~~~~~~~~