Implement a function to compute ROC curves and AUC scores for evaluating the performance of a Siamese Network in a One-Shot Learning task

Question

Can you explain how to implement a function to compute ROC curves and AUC scores for evaluating the performance of a Siamese Network in a One-Shot Learning task?

Answer 1

You can compute ROC curves and AUC scores for evaluating the performance of a Siamese Network in a One-Shot Learning task by using sklearn.metrics.roc_curve and roc_auc_score after obtaining the predicted similarity scores.

Here is the code snippet you can refer to:

In the above code we are using the following key strategies:

• Computes ROC curve using true labels and similarity scores.

• Calculates AUC using roc_auc_score for model evaluation.

• Visualizes ROC curve for intuitive performance understanding.

Hence, ROC curve and AUC computation allows for comprehensive evaluation of Siamese Network performance in One-Shot Learning tasks, helping assess both classification and ranking accuracy.