【論文代碼】GraphSAGE（更新ing）

文章目錄

一、官方代碼

1.1 加載數據

1.2 Unsupervised Loss

1.3 Models

1.4 評估與模型使用

1.5 Main

二、PyG版本

class SAGEConv(MessagePassing):

Reference

一、官方代碼

Cora數據集由機器學習論文組成。 這些論文分為以下七個類別之一：

基于案例

遺傳算法

神經網絡

概率方法

強化學習

規則學習

理論

這些論文的選擇方式是，在最終語料庫中，每篇論文引用或被至少一篇其他論文引用。整個語料庫中有 2708篇論文。

在詞干堵塞和去除詞尾后，只剩下 1433個 唯一的單詞。文檔頻率小于10的所有單詞都被刪除。

1.1 加載數據

1.2 Unsupervised Loss

1.3 Models

1.4 評估與模型使用

1.5 Main

二、PyG版本

x i ′ = W 1 x i + W 2 ? m e a n j ∈ N ( i ) x j \mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \cdot \mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j xi′ =W1 xi +W2 ?meanj∈N(i) xj

class SAGEConv(MessagePassing):

（1）in_channels (int or tuple): Size of each input sample, or :obj:-1 to derive the size from the first input(s) to the forward method.A tuple corresponds to the sizes of source and target dimensionalities.

（2）out_channels (int): Size of each output sample.

（3）normalize (bool, optional): If set to :obj:True, output features will be :math: ? 2 \ell_2 ?2 -normalized, i.e., :math: x i ′ ∥ x i ′ ∥ 2 \frac{\mathbf{x}^{\prime}_i} {\| \mathbf{x}^{\prime}_i \|_2} ∥xi′ ∥2 xi′ . (default: :obj:False)

（4）root_weight (bool, optional): If set to :obj:False, the layer will not add transformed root node features to the output.(default: :obj:True)

（5）bias (bool, optional): If set to :obj:False, the layer will not learn an additive bias. (default: :obj:True)

（6）**kwargs (optional): Additional arguments of

官方代碼：https://github.com/williamleif/graphsage-simple/

如果我們使用pytorch的PyG也能很方便調用：

# -*- coding: utf-8 -*- """ Created on Fri Oct 8 23:16:13 2021 @author: 86493 """ import torch from torch_geometric.datasets import Planetoid from torch_geometric.transforms import NormalizeFeatures dataset = Planetoid(root='C:/dataset/Cora/processed', name='Cora', transform=NormalizeFeatures()) print() print(f'Dataset: {dataset}:') print('======================') print(f'Number of graphs: {len(dataset)}') print(f'Number of features: {dataset.num_features}') print(f'Number of classes: {dataset.num_classes}') data = dataset[0] # Get the first graph object. print() print(data) print('======================') # Gather some statistics about the graph. print(f'Number of nodes: {data.num_nodes}') print(f'Number of edges: {data.num_edges}') print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}') print(f'Number of training nodes: {data.train_mask.sum()}') print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}') print(f'Contains isolated nodes: {data.has_isolated_nodes()}') print(f'Contains self-loops: {data.has_self_loops()}') print(f'Is undirected: {data.is_undirected()}') # 2.可視化節點表征分布的方法 import matplotlib.pyplot as plt from sklearn.manifold import TSNE def visualize(h, color): z = TSNE(n_components=2).fit_transform(h.detach().cpu().numpy()) plt.figure(figsize=(10,10)) plt.xticks([]) plt.yticks([]) plt.scatter(z[:, 0], z[:, 1], s=70, c=color, cmap="Set2") plt.show() # 網絡的構造 import torch from torch.nn import Linear import torch.nn.functional as F """ from torch_geometric.nn import GCNConv class GCN(torch.nn.Module): def __init__(self, hidden_channels): super(GCN, self).__init__() torch.manual_seed(12345) self.conv1 = GCNConv(dataset.num_features, hidden_channels) self.conv2 = GCNConv(hidden_channels, dataset.num_classes) def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = x.relu() x = F.dropout(x, p=0.5, training=self.training) x = self.conv2(x, edge_index) return x """ from torch_geometric.nn import SAGEConv class SAGE(torch.nn.Module): def __init__(self, hidden_channels): super(SAGE, self).__init__() torch.manual_seed(12345) self.conv1 = SAGEConv(dataset.num_features, hidden_channels) self.conv2 = SAGEConv(hidden_channels, dataset.num_classes) def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = x.relu() x = F.dropout(x, p=0.5, training=self.training) x = self.conv2(x, edge_index) return x model = SAGE(hidden_channels=16) print(model) # 可視化由未經訓練的圖神經網絡生成的節點表征 model = SAGE(hidden_channels=16) model.eval() out = model(data.x, data.edge_index) visualize(out, color=data.y) # 圖神經網絡的訓練 model = SAGE(hidden_channels=16) optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) criterion = torch.nn.CrossEntropyLoss() def train(): model.train() optimizer.zero_grad() # Clear gradients. out = model(data.x, data.edge_index) # Perform a single forward pass. loss = criterion(out[data.train_mask], data.y[data.train_mask]) # Compute the loss solely based on the training nodes. loss.backward() # Derive gradients. optimizer.step() # Update parameters based on gradients. return loss for epoch in range(1, 201): loss = train() print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}') # 增加loss折線圖 import pandas as pd df = pd.DataFrame(columns = ["Loss"]) # columns列名 df.index.name = "Epoch" for epoch in range(1, 201): loss = train() #df.loc[epoch] = loss.item() df.loc[epoch] = loss.item() df.plot() # 圖神經網絡的測試 def test(): model.eval() out = model(data.x, data.edge_index) pred = out.argmax(dim=1) # Use the class with highest probability. test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels. test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions. return test_acc test_acc = test() print(f'Test Accuracy: {test_acc:.4f}') # 可視化由訓練后的圖神經網絡生成的節點表征 model.eval() out = model(data.x, data.edge_index) visualize(out, color=data.y)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

打印出的結果為：

Dataset: Cora(): ====================== Number of graphs: 1 Number of features: 1433 Number of classes: 7 Data( x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708] ) ====================== Number of nodes: 2708 Number of edges: 10556 Average node degree: 3.90 Number of training nodes: 140 Training node label rate: 0.05 Contains isolated nodes: False Contains self-loops: False Is undirected: True SAGE( (conv1): SAGEConv(1433, 16) (conv2): SAGEConv(16, 7) )

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

可視化的圖如上所示，也可以可視化loss的200個epoch的折線圖：

Reference

（1）https://github.com/twjiang/graphSAGE-pytorch/tree/master/src

（2）https://zhuanlan.zhihu.com/p/410407148

（3）https://blog.csdn.net/weixin_44027006/article/details/116888648

（4）GraphSAGE 代碼解析(二) - layers.py

（5）https://www.zhihu.com/search?q=GraphSAGE%E4%BB%A3%E7%A0%81PyG%E8%A7%A3%E8%AF%BB&utm_content=search_history&type=content

機器學習 神經網絡

版權聲明：本文內容由網絡用戶投稿，版權歸原作者所有，本站不擁有其著作權，亦不承擔相應法律責任。如果您發現本站中有涉嫌抄襲或描述失實的內容，請聯系我們jiasou666@gmail.com 處理，核實后本網站將在24小時內刪除侵權內容。