如何通过Networkx图传播神经网络梯度？

Question

我正在尝试训练我的神经网络，使图中每条边的权重都是10。我从生成随机点(inp)开始，并使用weight = 1使每个相邻的点(使用idx)都有一条边。然后，如果两个邻接点已经有了一条边，边的权重将被发送到NN，该NN输出要添加到它的附加权重。

import warnings
warnings.filterwarnings("ignore", category=UserWarning)

import torch
import torch.nn as nn
import networkx as nx
import matplotlib.pyplot as plt
import torch.optim as optim

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(1,3)
        self.fc2 = nn.Linear(3,1)

    def forward(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        return x

g = nx.DiGraph() 
model = Net()
optimizer = optim.Adam(model.parameters(), lr = 1e-3)

def training(n_iter):
    for epoch in range(n_iter):
        print(epoch)
        inp = torch.randint(0,10,(20,))
        idx = 0
        while idx < len (inp) - 1:
            if  g.has_edge(inp[idx].item(), inp[idx+1].item()): #edge exist

                edge_weight = g[inp[idx].item()][inp[idx+1].item()]["weight"]
                edge_weight_tensor = torch.tensor([edge_weight]).float() #to tensor
                
                added_edge_weight = model(edge_weight_tensor) #value from network
                g[inp[idx].item()][inp[idx+1].item()]["weight"] += added_edge_weight
                idx +=1
            else:
                g.add_edge(inp[idx].item(),inp[idx+1].item(), weight = 1)
                idx +=1

        edges = g.edges()
        weights = [g[u][v]['weight'] for u,v in edges]
        optimizer.zero_grad()

        loss_list = [w for w in weights if not isinstance(w, int)] #only take tensors
        try:
            loss_tensors = (torch.stack(loss_list, dim=0)-10) 
            loss_square = torch.square(loss_tensors)
            loss = torch.sum(loss_square)
            print(loss)
        except RuntimeError: #no tensors - hence create a 0 loss
            loss = torch.tensor(0.0, requires_grad = True)

        loss.backward(retain_graph=True)
        optimizer.step()
    return weights

weights = training(5)

#plot
plt.figure(figsize=(6,6))
pos = nx.spring_layout(g, k = 0.5) 

nx.draw(g, with_labels=True, node_color='skyblue', font_weight='bold',  width=weights, pos=pos)

我的问题是，我不确定梯度是否可以以这种方式传播，而且看起来我不能将NN权重添加到边的权重中-得到以下错误：

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [3, 1]], which is output 0 of TBackward, is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

Answer 1

PyTorch几何是专门为在图形对象上实现PyTorch方法而设计的。具体地说，它的utils.from_networkx功能允许从networkx图转换为PyTorch几何对象，这可能会传播梯度。