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.
Latihan ini adalah bagian dari kursus
Image Modeling with Keras
Petunjuk latihan
- 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.
Latihan interaktif praktis
Cobalah latihan ini dengan menyelesaikan kode contoh berikut.
# Calculate the number of correct predictions
number_correct = ____
print(number_correct)
# Calculate the proportion of correct predictions
proportion_correct = ____
print(proportion_correct)