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Cora图神经网络Pytorch

Tom2Code

发布于 2024-02-01 04:37:47

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代码可运行

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代码可运行

最近搞到了datawhale的内部资料，学习的同时也来分享一波

首先在加载数据集的时候就报错了：

https://github.com/kimiyoung/planetoid/
raw/master/data/ind.cora.x进行下载Core数据集，
但是没有蹄 子打不开下载不了
出现“TimeoutError: [WinError 10060] 由于连接方
在一段时间后没有正确答复或连接的主机没有反应，连接
尝试失败。”这种错误。

所以我们需要手动的下载数据集，并且解压放到对应的文件夹下：

首先给大家先放上加载数据集的代码：

from torch_geometric.datasets import Planetoid


import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree

dataset = Planetoid(root='D:/data/', name='Cora')
print(dataset)

打印一下数据集的一些属性：

from torch_geometric.datasets import Planetoid


import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
from torch_geometric.transforms import NormalizeFeatures
dataset = Planetoid(root='D:/data/', 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.contains_isolated_nodes()}')
print(f'Contains self-loops: {data.contains_self_loops()}')
print(f'Is undirected: {data.is_undirected()}')

输出结果：

学习资料中是这样介绍的：

然后使用一个简单的MLP神经网络来看看效果怎样：

class MLP(torch.nn.Module):
    def __init__(self, hidden_channels):
        super(MLP, self).__init__()
        torch.manual_seed(12345)
        self.lin1 = Linear(dataset.num_features, hidden_channels)
        self.lin2 = Linear(hidden_channels, dataset.num_classes)
    def forward(self, x):
        x = self.lin1(x)
        x = x.relu()
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.lin2(x)
        return x


model = MLP(hidden_channels=16)
print(model)

model = MLP(hidden_channels=16)
criterion = torch.nn.CrossEntropyLoss() # Define loss criterion.
optimizer = torch.optim.Adam(model.parameters(), lr=0.01,weight_decay=5e-4) # Define optimizer.


def train():
    model.train()
    optimizer.zero_grad() # Clear gradients.
    out = model(data.x) # Perform a single forward pass.
    loss = criterion(out[data.train_mask],data.y[data.train_mask]) # Compute the loss solely based on the
    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}')


def test():
    model.eval()
    out = model(data.x)
    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

输出：

正如我们看到的，效果很差，一个重要的原因就是有标签的节点数量过少，训练的时候会有一些过拟合

接下来我们介绍图卷积神经网络：

然后开始构建我们的图神经网络：

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

然后可视化由未经过训练的GCN图神经网络生成的节点表征：

通过visualize的函数处理，7维特征的节点被映射到2维的平面上，可以看到有点“同类节点群聚”的现象

接下来，构建我们的图神经网络：

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

model = GCN(hidden_channels=16)
print("图神经网络：")
print(model)

model.eval()
out=model(data.x,data.edge_index)
visualize(out,color=data.y)


model = GCN(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 singleforward pass.
    loss = criterion(out[data.train_mask],
    data.y[data.train_mask]) # Compute the loss solely based on thetraining 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}')

def test():
    model.eval()
    out=model(data.x,data.edge_index)
    pred=out.argmax(dim=1)
    test_correct=pred[data.test_mask]==data.y[data.test_mask]
    test_acc=int(test_correct.sum())/int(data.test_mask.sum())
    return test_acc

test_acc=test()
print(f"Test Accuracy:{test_acc:.4f}")