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.

This image depicts an neural network with 5 input nodes and 3 output nodes.

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.

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.

Take Hint (-15 XP)

Exercise

The linear algebra of dense layers

Instructions 1/2