第PyG搭建GCN模型實現(xiàn)節(jié)點分類GCNConv參數(shù)詳解目錄前言模型搭建1.前向傳播2.反向傳播3.訓練4.測試完整代碼

前言

在上一篇文章PyG搭建GCN前的準備：了解PyG中的數(shù)據(jù)格式中，大致了解了PyG中的數(shù)據(jù)格式，這篇文章主要是簡單搭建GCN來實現(xiàn)節(jié)點分類，主要目的是了解PyG中GCN的參數(shù)情況。

模型搭建

首先導入包：

fromtorch_geometric.nnimportGCNConv

模型參數(shù)：

in_channels：輸入通道，比如節(jié)點分類中表示每個節(jié)點的特征數(shù)。

out_channels：輸出通道，最后一層GCNConv的輸出通道為節(jié)點類別數(shù)（節(jié)點分類）。

improved：如果為True表示自環(huán)增加，也就是原始鄰接矩陣加上2I而不是I，默認為False。

cached：如果為True，GCNConv在第一次對鄰接矩陣進行歸一化時會進行緩存，以后將不再重復計算。

add_self_loops：如果為False不再強制添加自環(huán)，默認為True。

normalize：默認為True，表示對鄰接矩陣進行歸一化。

bias：默認添加偏置。

于是模型搭建如下：

classGCN(torch.nn.Module):

def__init__(self,num_node_features,num_classes):

super(GCN,self).__init__()

self.conv1=GCNConv(num_node_features,16)

self.conv2=GCNConv(16,num_classes)

defforward(self,data):

x,edge_index=data.x,data.edge_index

x=self.conv1(x,edge_index)

x=F.relu(x)

x=F.dropout(x,training=self.training)

x=self.conv2(x,edge_index)

x=F.relu(x)

x=F.dropout(x,training=self.training)

x=F.softmax(x,dim=1)

returnx

輸出一下模型：

data=Planetoid(root='/data/CiteSeer',name='CiteSeer')model=GCN(data.num_node_features,data.num_classes).to(device)print(model)GCN(

(conv1):GCNConv(3703,16)

(conv2):GCNConv(16,6)

輸出為：

GCN((conv1):GCNConv(3703,16)(conv2):GCNConv(16,6))GCN(

(conv1):GCNConv(3703,16)

(conv2):GCNConv(16,6)

1.前向傳播

查看官方文檔中GCNConv的輸入輸出要求：

可以發(fā)現(xiàn)，GCNConv中需要輸入的是節(jié)點特征矩陣x和鄰接關系edge_index，還有一個可選項edge_weight。因此我們首先：

x,edge_index=data.x,data.edge_index

x=self.conv1(x,edge_index)

x=F.relu(x)

x=F.dropout(x,training=self.training)

此時我們不妨輸出一下x及其size：

tensor([[0.0000,0.1630,0.0000,...,0.0000,0.0488,0.0000],

[0.0000,0.2451,0.1614,...,0.0000,0.0125,0.0000],

[0.1175,0.0262,0.2141,...,0.2592,0.0000,0.0000],

...,

[0.0000,0.0000,0.0000,...,0.0000,0.1825,0.0000],

[0.0000,0.1024,0.0000,...,0.0498,0.0000,0.0000],

[0.0000,0.3263,0.0000,...,0.0000,0.0000,0.0000]],

device='cuda:0',grad_fn=FusedDropoutBackward0)

torch.Size([3327,16])

此時的x一共3327行，每一行表示一個節(jié)點經(jīng)過第一層卷積更新后的狀態(tài)向量。

那么同理，由于：

self.conv2=GCNConv(16,num_classes)

所以經(jīng)過第二層卷積后：

x=self.conv2(x,edge_index)x=F.relu(x)x=F.dropout(x,training=self.training)x=self.conv2(x,edge_index)

x=F.relu(x)

x=F.dropout(x,training=self.training)

此時得到的x的size應該為：

torch.Size([3327,6])

即每個節(jié)點的維度為6的狀態(tài)向量。

由于我們需要進行6分類，所以最后需要加上一個softmax：

x=F.softmax(x,dim=1)

dim=1表示對每一行進行運算，最終每一行之和加起來為1，也就表示了該節(jié)點為每一類的概率。輸出此時的x：

tensor([[0.1607,0.1727,0.1607,0.1607,0.1607,0.1846],[0.1654,0.1654,0.1654,0.1654,0.1654,0.1731],[0.1778,0.1622,0.1733,0.1622,0.1622,0.1622],...,[0.1659,0.1659,0.1659,0.1704,0.1659,0.1659],[0.1667,0.1667,0.1667,0.1667,0.1667,0.1667],[0.1641,0.1641,0.1658,0.1766,0.1653,0.1641]],device='cuda:0',grad_fn=SoftmaxBackward0)tensor([[0.1607,0.1727,0.1607,0.1607,0.1607,0.1846],

[0.1654,0.1654,0.1654,0.1654,0.1654,0.1731],

[0.1778,0.1622,0.1733,0.1622,0.1622,0.1622],

...,

[0.1659,0.1659,0.1659,0.1704,0.1659,0.1659],

[0.1667,0.1667,0.1667,0.1667,0.1667,0.1667],

[0.1641,0.1641,0.1658,0.1766,0.1653,0.1641]],device='cuda:0',

grad_fn=SoftmaxBackward0)

2.反向傳播

在訓練時，我們首先利用前向傳播計算出輸出：

out=model(data)

out即為最終得到的每個節(jié)點的6個概率值，但在實際訓練中，我們只需要計算出訓練集的損失，所以損失函數(shù)這樣寫：

loss=loss_function(out[data.train_mask],data.y[data.train_mask])

然后計算梯度，反向更新！

3.訓練

訓練的完整代碼：

deftrain():optimizer=torch.optim.Adam(model.parameters(),lr=0.01,weight_decay=5e-4)loss_function=torch.nn.CrossEntropyLoss().to(device)model.train()forepochinrange(500):out=model(data)optimizer.zero_grad()loss=loss_function(out[data.train_mask],data.y[data.train_mask])loss.backward()optimizer.step()print('Epoch{:03d}loss{:.4f}'.format(epoch,loss.item()))deftrain():

optimizer=torch.optim.Adam(model.parameters(),lr=0.01,weight_decay=5e-4)

loss_function=torch.nn.CrossEntropyLoss().to(device)

model.train()

forepochinrange(500):

out=model(data)

optimizer.zero_grad()

loss=loss_function(out[data.train_mask],data.y[data.train_mask])

loss.backward()

optimizer.step()

print('Epoch{:03d}loss{:.4f}'.format(epoch,loss.item()))

4.測試

我們首先需要算出模型對所有節(jié)點的預測值：

model(data)

此時得到的是每個節(jié)點的6個概率值，我們需要在每一行上取其最大值：

model(data).max(dim=1)

輸出一下：

torch.return_types.max(

values=tensor([0.9100,0.9071,0.9786,...,0.4321,0.4009,0.8779],device='cuda:0',

grad_fn=MaxBackward0),

indices=tensor([3,1,5,...,3,1,5],device='cuda:0'))

返回的第一項是每一行的最大值，第二項為最大值在這一行中的索引，我們只需要取第二項，那么最終的預測值應該寫為：

_,pred=model(data).max(dim=1)

然后計算預測精度：

correct=int(pred[data.test_m