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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.

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.

Instructions 1/2
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  • 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.