Exercise

# Coding the forward propagation algorithm

In this exercise, you'll write code to do forward propagation (prediction) for your first neural network:

Each data point is a customer. The first input is how many accounts they have, and the second input is how many children they have. The model will predict how many transactions the user makes in the next year. You will use this data throughout the first 2 chapters of this course.

The input data has been pre-loaded as `input_data`

, and the weights are available in a dictionary called `weights`

. The array of weights for the first node in the hidden layer are in `weights['node_0']`

,
and the array of weights for the second node in the hidden layer are in `weights['node_1']`

.

The weights feeding into the output node are available in `weights['output']`

.

NumPy will be pre-imported for you as `np`

in all exercises.

Instructions

**100 XP**

- Calculate the value in node 0 by multiplying
`input_data`

by its weights`weights['node_0']`

and computing their sum. This is the 1st node in the hidden layer. - Calculate the value in node 1 using
`input_data`

and`weights['node_1']`

. This is the 2nd node in the hidden layer. - Put the hidden layer values into an array. This has been done for you.
- Generate the prediction by multiplying
`hidden_layer_outputs`

by`weights['output']`

and computing their sum. - Hit 'Submit Answer' to print the output!