From 1becba5f4d06b6e92170c6092e6c69ed2d90056c Mon Sep 17 00:00:00 2001
From: WangGodder <32242917+WangGodder@users.noreply.github.com>
Date: Mon, 9 Dec 2019 12:03:27 +0800
Subject: [PATCH] update GCN

---
 torchcmh/models/.gitignore |  1 -
 torchcmh/models/GCN.py     | 46 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 46 insertions(+), 1 deletion(-)
 create mode 100644 torchcmh/models/GCN.py

diff --git a/torchcmh/models/.gitignore b/torchcmh/models/.gitignore
index 44a978d..e658dee 100644
--- a/torchcmh/models/.gitignore
+++ b/torchcmh/models/.gitignore
@@ -2,7 +2,6 @@
 capsnet.py
 resnet_capsnet.py
 CompactBilinearPooling.py
-GCN.py
 GCNText.py
 GCMH
 QDCMH
diff --git a/torchcmh/models/GCN.py b/torchcmh/models/GCN.py
new file mode 100644
index 0000000..58b8844
--- /dev/null
+++ b/torchcmh/models/GCN.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2019/8/28
+# @Author  : Godder
+# @Github  : https://github.com/WangGodder
+import math
+import torch
+from torch.nn.parameter import Parameter
+from torch import nn
+
+
+class GraphConvolution(nn.Module):
+    """
+    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
+    """
+
+    def __init__(self, in_features, out_features, bias=True):
+        super(GraphConvolution, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
+        if bias:
+            self.bias = Parameter(torch.FloatTensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        stdv = 1. / math.sqrt(self.weight.size(1))
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.uniform_(-stdv, stdv)
+
+    def forward(self, input, adj):
+        support = torch.mm(input, self.weight)
+        output = torch.spmm(adj, support)
+        if self.bias is not None:
+            return output + self.bias
+        else:
+            return output
+
+    def __repr__(self):
+        return self.__class__.__name__ + ' (' \
+               + str(self.in_features) + ' -> ' \
+               + str(self.out_features) + ')'
+
+