Get startedGet started for free

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

View Course

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)
Edit and Run Code