Early Stopping

Decide whether training should stop early. Scan the validation losses in order; an epoch is bad if its loss is not strictly lower than the best loss seen so far, and the bad-epoch counter resets whenever a new best is reached. Return True as soon as the counter reaches patience.

Implement solve(losses,patience).

Returns. Return True if training would have stopped somewhere in the list, otherwise False.

Examples

Example 1

Input:

solve([1.0, 0.9, 0.95, 0.96], 1)

Output:

True

Example 2

Input:

solve([1.0, 0.9, 0.95, 0.96], 3)

Output:

False

Example 3

Input:

solve([1.0, 0.9, 0.8, 0.7], 2)

Output:

False