What is numerical gradient checking?

What is numerical gradient checking?

What is Gradient Checking? We describe a method for numerically checking the derivatives computed by your code to make sure that your implementation is correct. Carrying out the derivative checking procedure significantly increase your confidence in the correctness of your code.

What is a positive and negative gradient?

Lines with a positive gradient slope upwards, from left to right. Lines with a negative gradient slope downwards from left to right. Lines with a zero gradient are horizontal. When drawing a line, c gives the position where the line cuts the vertical axis.

How to debug a neural network with gradient checking?

Therefore, a method to debug this step could potentially save a lot of time and headaches when debugging a neural network. Here, the method of gradient checking will be introduced. Briefly, this methods consists in approximating the gradient using a numerical approach.

What can numerical gradient checking do for You?

It’s important to stress that numerical gradient checking, won’t in any way help you debug where the error might be. It can only increase your certainty that your implementation is correct or incorrect and in which degree.

What are some common mistakes in gradient checking?

While implementing numerical gradient checking isn’t that hard of a task, people often at their first try fall into one of these common mistakes: Calculating the slope of the output and not the error. Sometimes when implementing gradient checking, people use a simple network with a single output.

How to check the slope of a gradient?

First, take all your computed derivatives and transform into a single vector. And then do the same with your estimated slope values. And then divide the norm of the difference by the norm of the sum of the vectors: The Error should be around or less if you have calculated your derivatives correctly.