units , number of layers,subset (values of entries to be considered for embeddings),epochs ")hidden_units=32num_layers=4subset=34epochs=10v_emb,v_graph=vgcn.get_gcn_embeddings(hidden_units,train_df,source_label,target_label,epochs,num_layers,subset)print(v_emb.shape)returnv_emb,v_graph ...
(MLS) point clouds. To address these issues, we developed SCL-GCN, a Stratified Contrastive Learning Graph Convolution Network with a novel dual-branch architecture for MLS-point cloud-based pavement crack detection. First, a multi-scale graph representation construction module was designed based on...
python import torch import torch.nn.functional as F from torch_geometric.nn import GCNConv, GATConv from torch_geometric.data import Data from torch.nn.utils.rnn import pad_sequence class DynamicGNN(torch.nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_layers): sup...
Finally, a fusion result was generated through a 1 × 1 convolution. The seven output results were calculated with the label to calculate the loss, and the sum of the loss was used to complete the back propagation and parameter optimization. 3.3. Evaluation Metrics The outputs of the model ...
The GCN model defines convolutional operation in the Fourier domain. The convolutional filter captures spatial features of each node from its first-order neighborhood and itself. A typical multi-layered GCN is shown in Figure 4, in which the relationship between two adjacency layers can be ...