Combining multiple probabilities from a classifier. Propagating probabilities
If you feed the model with 20 images for testing then your output should look like array of [20x10]. For each row represents the probabilities of all classes(in your case 10). Let's see the example below, and 1-index is dog with 0.9 probability, so your model classified it correctly. If your model classifies 19 times dog-correct and 1 times cat-wrong then your model's test accuracy will be calculated as correct_preds / (correct_preds+wrong_preds)
[[0.0, 0.9, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0],
 [0.0, 0.8, 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.0, 0.0],
 ..,
 [0.0, 0.75, 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.05, 0.0]]