Question

我创建了一个包含 500 个图的数据集，如下所示：

data_list = []
ngraphs = 500
for i in range(ngraphs):
      num_nodes = randint(10,500)
      num_edges = randint(10,num_nodes*(num_nodes - 1))
      f1 = np.random.randint(10, size=(num_nodes))
      f2 = np.random.randint(10,20, size=(num_nodes))
      f3 = np.random.randint(20,30, size=(num_nodes))
      f_final = np.stack((f1,f2,f3), axis=1)
      capital = 2*f1 + f2 - f3
      f1_t = torch.from_numpy(f1)
      f2_t = torch.from_numpy(f2)
      f3_t =  torch.from_numpy(f3)   
      capital_t =  torch.from_numpy(capital)
      capital_t = capital_t.type(torch.FloatTensor)
      x = torch.from_numpy(f_final)
      x = x.type(torch.FloatTensor)
      #edge_index = torch.randint(low=0, high=num_nodes, size=(2, num_edges), dtype=torch.long)
      edge_index = torch.randint(low=0, high=num_nodes, size=(num_edges,2), dtype=torch.long)
      edge_attr  = torch.randint(low=0, high=50, size=(num_edges,1), dtype=torch.long)
      data = Data(x = x, edge_index = edge_index.t().contiguous(), y = capital_t, edge_attr=edge_attr )
      data_list.append(data)

我正在尝试编写图神经网络模型：

import torch
from tqdm import tqdm
import torch.nn.functional as F
from torch.nn import Linear, LayerNorm, ReLU
from torch_scatter import scatter
from torch_geometric.nn import GENConv, DeepGCNLayer
from torch_geometric.data import RandomNodeSampler
class DeeperGCN(torch.nn.Module):
    def __init__(self, hidden_channels, num_layers):
        super(DeeperGCN, self).__init__()

        self.node_encoder = Linear(data.x.size(-1), hidden_channels)
        self.edge_encoder = Linear(data.edge_attr.size(-1), hidden_channels)

        self.layers = torch.nn.ModuleList()
        for i in range(1, num_layers + 1):
            conv = GENConv(hidden_channels, hidden_channels, aggr='softmax',
                           t=1.0, learn_t=True, num_layers=2, norm='layer')
            norm = LayerNorm(hidden_channels, elementwise_affine=True)
            act = ReLU(inplace=True)

            layer = DeepGCNLayer(conv, norm, act, block='res+', dropout=0.5,
                                 ckpt_grad=i % 3)
            self.layers.append(layer)

        self.lin = Linear(hidden_channels, 1)

    def forward(self, x, edge_index, edge_attr):
        x = self.node_encoder(x)
        edge_attr = self.edge_encoder(edge_attr)

        x = self.layers[0].conv(x, edge_index, edge_attr)

        for layer in self.layers[1:]:
            x = layer(x, edge_index, edge_attr)

        x = self.layers[0].act(self.layers[0].norm(x))
        

        return self.lin(x)

我有一个基本的训练功能：

def train():
      model.train()
      optimizer.zero_grad()  # Clear gradients.
      out = model(data.x, data.edge_index, data.edge_attr)  # 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

##Train the model
for epoch in range(1, 500):
    loss = train()
    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')

当我运行上面的时候，我不断收到以下错误：

<块引用>

运行时错误：预期标量类型为 Float，但发现为 Long

当我按数据集制作时，我曾尝试解决所有数据类型问题。所以我不确定发生了什么。如果有人可以提供帮助。

训练 GNN 模型时的数据类型错误

0 个答案: