Model_pred_obj

for images, labels in val_data.take(1):
    for i in range(6):
        print("True_class:",val_data.class_names[labels[i]])
        x = image.img_to_array(images[i])
        x = np.expand_dims(x, axis=0)
        p=np.argmax(model.predict(x))
        if p==0:
            print("Predicted Image: Electric Bus")
        else:
            print("Predicted Image: Electric Car")
            
            
 Output:
 
 True_class: ELectric Bus
Predicted Image: Electric Bus
Predicted class: 0
 
True_class: ELectric Bus
Predicted Image: Electric Bus
Predicted class: 0
 
True_class: Electric Car
Predicted Image: Electric Car
Predicted class: 1
 
True_class: ELectric Bus
Predicted Image: Electric Bus
Predicted class: 0