## pr.make_predictions(engines, mapping, images, 10)
## if engines[0][2] <= base:
## n.bias -= rand
## else:
## base = engines[0][2]
#end_score = base
#print("\nStart score = " + str(start_score) + ", End score = " + str(end_score))
#non_jit = p.construct_non_jit(mp)
#pickle.dump(non_jit, open("neural_net.txt", "wb"))
#pickle.dump(skip_list, open("skip_list.txt", "wb"))
#input("Finished tuning session")
for i in range(len(images)):
image = images[i]
#print("Testing image " + str(num+1), end='\r')
inputs = pr.get_inputs(image[1], i)
#print(inputs)
result = mp.forward_prop(inputs)
prediction = result.index(max(result))
for i, r in enumerate(result):
print("Amount for character " + chr(mapping[str(i)]) + ": " + str(r))
print("\nPredicted " + str(chr(mapping[str(prediction)])) +
", correct character is " + chr(mapping[image[0]]))
input()
input("\nDone")
print("\n\n===================================")
print(" Image Recognition on Characters")
print("===================================\n")
print(
