| Input: Datasets(train_loader, valid_loader, test_loader), |
| Learning rate, Weight decay, Epochs, Batch size. |
| Output: The predictive value of the test samples and the prediction performance of the model. |
| 1: Training process: |
| 2: Define the model: Three layers of GCN (two hidden layers and one output layer) |
| 3: Load the datasets: train_loader, valid_loader |
| 4: # model.train() |
| 5: for to Epochs do: |
| 6: All data is propagated forward, and the activation function uses Leaky ReLU |
| 7: Use cross-entropy loss function to calculate the loss value |
| 8: Clear the gradient: optimizer.zero_grad() |
| 9: Backpropagate and calculate the gradient of the parameter |
| 10: Use Adam optimizer to update the gradient: optimizer.step() |
| 11: Calculate the accuracy of the current model on the training set |
| 12: Calculate the accuracy of the current model on validation set |
| 13: end for |
| 14: Test process: |
| 15: # model.test() |
| 16: Load the trained model |
| 17: Load the dataset: test_loader |
| 18: Calculate the predicted value y_pred of the test samples |
| 19: Evaluate the model performance: Precision, Recall, F1-score, AUC |
