Making predictions with matrix multiplication
In later chapters, you will learn to train linear regression models. This process will yield a vector of parameters that can be multiplied by the input data to generate predictions. In this exercise, you will use input data, features
, and a target vector, bill
, which are taken from a credit card dataset we will use later in the course.
\(features = \begin{bmatrix} 2 & 24 \\ 2 & 26 \\ 2 & 57 \\ 1 & 37 \end{bmatrix}\), \(bill = \begin{bmatrix} 3913 \\ 2682 \\ 8617 \\ 64400 \end{bmatrix}\), \(params = \begin{bmatrix} 1000 \\ 150 \end{bmatrix}\)
The matrix of input data, features
, contains two columns: education level and age. The target vector, bill
, is the size of the credit card borrower's bill.
Since we have not trained the model, you will enter a guess for the values of the parameter vector, params
. You will then use matmul()
to perform matrix multiplication of features
by params
to generate predictions, billpred
, which you will compare with bill
. Note that we have imported matmul()
and constant()
.
This exercise is part of the course
Introduction to TensorFlow in Python
Exercise instructions
- Define
features
,params
, andbill
as constants. - Compute the predicted value vector,
billpred
, by multiplying the input data,features
, by the parameters,params
. Use matrix multiplication, rather than the element-wise product. - Define
error
as the targets,bill
, minus the predicted values,billpred
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Define features, params, and bill as constants
features = ____([[2, 24], [2, 26], [2, 57], [1, 37]])
params = ____([[1000], [150]])
bill = ____([[3913], [2682], [8617], [64400]])
# Compute billpred using features and params
billpred = ____
# Compute and print the error
error = ____ - ____
print(error.numpy())