Open mikel-brostrom opened 2 years ago
This piece of code :
for x in np.arange(0.6, 0.9, 0.1): print('Eval with TH:', x) metrics = [ cdp.utils.metrics.Fscore(activation='argmax2d', threshold=x), cdp.utils.metrics.Precision(activation='argmax2d', threshold=x), cdp.utils.metrics.Recall(activation='argmax2d', threshold=x), ] valid_epoch = cdp.utils.train.ValidEpoch( model, loss=loss, metrics=metrics, device=DEVICE, verbose=True, ) valid_logs = valid_epoch.run(valid_loader) print(valid_logs)
Give me the following result:
Eval with TH: 0.6 valid: 100%|█████████████████████████████████████████████████████████████| 505/505 [01:12<00:00, 6.98it/s, cross_entropy_loss - 0.08708, fscore - 0.8799, precision - 0.8946, recall - 0.8789] {'cross_entropy_loss': 0.0870812193864016, 'fscore': 0.8798528309538921, 'precision': 0.8946225793644936, 'recall': 0.8789094516579565} Eval with TH: 0.7 valid: 100%|█████████████████████████████████████████████████████████████| 505/505 [01:12<00:00, 6.99it/s, cross_entropy_loss - 0.08708, fscore - 0.8799, precision - 0.8946, recall - 0.8789] {'cross_entropy_loss': 0.08708121913835626, 'fscore': 0.8798528309538921, 'precision': 0.8946225793644936, 'recall': 0.8789094516579565} Eval with TH: 0.7999999999999999 valid: 100%|█████████████████████████████████████████████████████████████| 505/505 [01:11<00:00, 7.02it/s, cross_entropy_loss - 0.08708, fscore - 0.8799, precision - 0.8946, recall - 0.8789] {'cross_entropy_loss': 0.08708121978843793, 'fscore': 0.8798528309538921, 'precision': 0.8946225793644936, 'recall': 0.8789094516579565}
This piece of code :
Give me the following result: