# Course 1 Week 4, what is Activation cache & what are the parameters it contains?

I am confused about the “activation cache” part. the cache contains two tuples, activation_cache, and linear_cache. linear_cache is pretty straight forward it’s basically (A_prev, W, b) but what does activation cache contain?

In Exercise 4 of the first assignment, in which you complete the
`linear_activation_forward()` function, the line before the return statement assigns two separate caches to form a bigger cache: `cache = (linear_cache, activation_cache)`. Formally, a tuple with two elements (which themselves can have multiple elements).

The values assigned to the `activation_cache` will depend on whether it is a `relu` activation or a `sigmoid` activation, set in the `activation` argument of the `linear_activation_forward()`. The mathematics to this is explained in the prelude to Exercise 4. These values are “cached” so that can be used to evaluate the gradient in the backward propagation step.

I hope that this helps! @kenb