Q:

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

A:

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.

A:

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.   

