Exercise

# The low-level approach with multiple examples

In this exercise, we'll build further intuition for the low-level approach by constructing the first dense hidden layer for the case where we have multiple examples. We'll assume the model is trained and the first layer weights, `weights1`

, and bias, `bias1`

, are available. We'll then perform matrix multiplication of the `borrower_features`

tensor by the `weights1`

variable. Recall that the `borrower_features`

tensor includes education, marital status, and age. Finally, we'll apply the sigmoid function to the elements of `products1 + bias1`

, yielding `dense1`

.

\(products1 = \begin{bmatrix} 3 & 3 & 23 \\ 2 & 1 & 24 \\ 1 & 1 & 49 \\ 1 & 1 & 49 \\ 2 & 1 & 29 \end{bmatrix} \begin{bmatrix} -0.6 & 0.6 \\ 0.8 & -0.3 \\ -0.09 & -0.08 \end{bmatrix}\)

Note that `matmul()`

and `keras()`

have been imported from `tensorflow`

.

Instructions

**100 XP**

- Compute
`products1`

by matrix multiplying the features tensor by the weights. - Use a sigmoid activation function to transform
`products1 + bias1`

. - Print the shapes of
`borrower_features`

,`weights1`

,`bias1`

, and`dense1`

.