Evaluating a classifier
To evaluate a classifier, we need to test it on images that were not used during training. This is called "cross-validation": a prediction of the class (e.g., t-shirt, dress or shoe) is made from each of the test images, and these predictions are compared with the true labels of these images.
The results of cross-validation are provided as one-hot encoded arrays: test_labels
and predictions
.
This exercise is part of the course
Image Modeling with Keras
Exercise instructions
- Multiply the arrays with each other and sum the result to find the total number of correct predictions.
- Divide the number of correct answers (the sum) by the length of
predictions
array to calculate the proportion of correct predictions.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the number of correct predictions
number_correct = ____
print(number_correct)
# Calculate the proportion of correct predictions
proportion_correct = ____
print(proportion_correct)