Exercise

# Coding how weight changes affect accuracy

Now you'll get to change weights in a real network and see how they affect model accuracy!

Have a look at the following neural network:

Its weights have been pre-loaded as `weights_0`

. Your task in this exercise is to update a **single** weight in `weights_0`

to create `weights_1`

, which gives a perfect prediction (in which the predicted value is equal to `target_actual`

: 3).

Use a pen and paper if necessary to experiment with different combinations. You'll use the `predict_with_network()`

function, which takes an array of data as the first argument, and weights as the second argument.

Instructions

**100 XP**

- Create a dictionary of weights called
`weights_1`

where you have changed**1**weight from`weights_0`

(You only need to make 1 edit to`weights_0`

to generate the perfect prediction). - Obtain predictions with the new weights using the
`predict_with_network()`

function with`input_data`

and`weights_1`

. - Calculate the error for the new weights by subtracting
`target_actual`

from`model_output_1`

. - Hit 'Submit Answer' to see how the errors compare!