Exercise

# The linear algebra of dense layers

There are two ways to define a dense layer in `tensorflow`

. The first involves the use of low-level, linear algebraic operations. The second makes use of high-level `keras`

operations. In this exercise, we will use the first method to construct the network shown in the image below.

The input layer contains 3 features -- education, marital status, and age -- which are available as `borrower_features`

. The hidden layer contains 2 nodes and the output layer contains a single node.

For each layer, you will take the previous layer as an input, initialize a set of weights, compute the product of the inputs and weights, and then apply an activation function. Note that `Variable()`

, `ones()`

, `matmul()`

, and `keras()`

have been imported from `tensorflow`

.

Instructions 1/2

**undefined XP**

- Initialize
`weights1`

as a variable using a 3x2 tensor of ones. - Compute the product of
`borrower_features`

by`weights1`

using matrix multiplication. - Use a sigmoid activation function to transform
`product1 + bias1`

.