from ui  import *
startUI()




































































































# # - read the input data:

# import MnistLoader
# training_data, validation_data, test_data = MnistLoader.load_data_wrapper()
# training_data = list(training_data)

# # ---------------------
# # - network.py example:
# from Network import Network, vectorized_result
# from NetworkLoader import save, load


# # netPath = "E:\\ITMO University\\Интеллектуальные системы и технологии\\Lab5\Lab\\Models\\model_5epochs.json";
# # net = load(netPath)

# # # imgPath = "E:\\ITMO University\\Интеллектуальные системы и технологии\\Lab5\\Lab\\HandTestImages\\0.png"

# # # predict(imgPath, 7, net)

# # # net = Network([784, 30, 10])
# # # net.run(training_data, 5, 10, 3.0, test_data=test_data, monitor_evaluation_cost=True,
# # #             monitor_evaluation_accuracy=True,
# # #             monitor_training_cost=True,
# # #             monitor_training_accuracy=True)

# # imgPath = "E:\\ITMO University\\Интеллектуальные системы и технологии\\Lab5\\Lab\\HandTestImages\\0.png"

# # #predict(imgPath, net)

# # save(net, "E:\ITMO University\Интеллектуальные системы и технологии\Lab5\Lab\Models\model_5epochs.json")

# from ui  import *
# net = ""
# startUI()








# # ----------------------
# # - network2.py example:
# # import network2


# # net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost)
# # #net.large_weight_initializer()
# # net.SGD(training_data, 30, 10, 0.1, lmbda = 5.0,evaluation_data=validation_data,
# #     monitor_evaluation_accuracy=True)

