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  3. Introduction to TensorFlow in Python

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.