Exercise

# Neural networks

Let us see the differences between neural networks which apply `ReLU`

and those which do not apply `ReLU`

. We have already initialized the input called `input_layer`

, and three sets of weights, called `weight_1`

, `weight_2`

and `weight_3`

.

We are going to convince ourselves that networks with multiple layers which do not contain non-linearity can be expressed as neural networks with one layer.

The network and the shape of layers and weights is shown below.

Instructions

**100 XP**

- Calculate the first and second hidden layer by multiplying the appropriate inputs with the corresponding weights.
- Calculate and print the results of the output.
- Set
`weight_composed_1`

to the product of`weight_1`

with`weight_2`

, then set`weight`

to the product of`weight_composed_1`

with`weight_3`

. - Calculate and print the output.