Challenge: Scaling Error Up to Multiple Data Points

Problem statement

We have seen how different weights will have different accuracies on a single prediction. Usually, we want to measure model accuracy on many points. Now, write code to compare model accuracies for two different sets of weights which have been stored as weights_0 and weights_1.

X is a NumPy array with different inputs and Y is the label. Loop over the training samples and calculate the error on multiple data points.

Sample input

Below is the sample input, i.e., the input features X, weights (weights_0, and weights_1), and the bias value:

X = np.array([[2, 3], [1, 4], [-1, -3], [-4, -5]])
weights_0 = np.array([0.0, 0.0]) 
weights_1 = np.array([1.0, -1.0])
bias = 0.1
Y = np.array([1.0, 1.0, 0.0, 0.0])

Sample output

The cross-entropy error in case of the weights_0 and weights_1 respectively:

2.7775866402942837, 7.797571499245237

Coding exercise

Write your code below. It is recommended​ to solve the exercise before viewing the solution.

Note: There is a ce_two_different_weights function given in the code for testing purposes. Do not modify the function signature.

Good luck!🤞