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.
Questo esercizio fa parte del corso
Image Modeling with Keras
Istruzioni dell'esercizio
- 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
predictionsarray to calculate the proportion of correct predictions.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Calculate the number of correct predictions
number_correct = ____
print(number_correct)
# Calculate the proportion of correct predictions
proportion_correct = ____
print(proportion_correct)