Evaluation & Calculate Top-N Accuracy: Top 1 and Top 5

I have come across few (Machine learning-classification problem) journal papers mentioned about evaluate accuracy with Top-N approach. Data was show that Top 1 accuracy = 42.5%, and Top-5 accuracy = 72.5% in the same training, testing condition. I wonder how to calculate this percentage of top-1 and top-5?

Can some one show me example and steps to calculate this?

Thanks

Top-1 accuracy is the conventional accuracy: the model answer (the one with highest probability) must be exactly the expected answer.

Top-5 accuracy means that any of your model 5 highest probability answers must match the expected answer.

For instance, let's say you're applying machine learning to object recognition using a neural network. A picture of a cat is shown, and these are the outputs of your neural network:

• Tiger: 0.4
• Dog: 0.3
• Cat: 0.1
• Lynx: 0.09
• Lion: 0.08
• Bird: 0.02
• Bear: 0.01

Using top-1 accuracy, you count this output as wrong, because it predicted a tiger.

Using top-5 accuracy, you count this output as correct, because cat is among the top-5 guesses.

The Complement of the accuracy is the error, The top-1 error is the percentage of time that the classifier did not give the correct class highest probability score. The top-5 error:- The percentage of time that the classifier did not include the correct class among the top 5 probabilities or guesses.