Exercise

# Applying the network to many observations/rows of data

You'll now define a function called `predict_with_network()`

which will generate predictions for multiple data observations, which are pre-loaded as `input_data`

. As before, `weights`

are also pre-loaded. In addition, the `relu()`

function you defined in the previous exercise has been pre-loaded.

Instructions

**100 XP**

- Define a function called
`predict_with_network()`

that accepts two arguments -`input_data_row`

and`weights`

- and returns a prediction from the network as the output. - Calculate the input and output values for each node, storing them as:
`node_0_input`

,`node_0_output`

,`node_1_input`

, and`node_1_output`

.- To calculate the input value of a node, multiply the relevant arrays together and compute their sum.
- To calculate the output value of a node, apply the
`relu()`

function to the input value of the node.

- Calculate the model output by calculating
`input_to_final_layer`

and`model_output`

in the same way you calculated the input and output values for the nodes. - Use a
`for`

loop to iterate over`input_data`

:- Use your
`predict_with_network()`

to generate predictions for each row of the`input_data`

-`input_data_row`

. Append each prediction to`results`

.

- Use your