Hands-On Transfer Learning with Python

Evaluating the classifier performance

It is now time to put our model to the test, literally. We will use the test dataset to make predictions with our model and then evaluate them against the ground truth labels. For this, we first need to get our model's predictions on the test data and do a reverse mapping from the numeric labels to the actual text labels, using the following snippet:

predictions = model.predict_classes(test_features) 
class_map = {'0' : 'air_conditioner', '1' : 'car_horn',  
             '2' : 'children_playing', '3' : 'dog_bark',  
             '4' : 'drilling', '5' : 'engine_idling',  
             '6' : 'gun_shot', '7' : 'jackhammer',  
             '8' : 'siren', '9' : 'street_music'} 
test_labels_categories = [class_map[str(label)]for label in  test_labels] prediction_labels_categories ...