Esempi in Python per Create_data_binary

Esempio n. 1

File: main_fair_comparaison.py Progetto: AdrienBenamira/Refactoring_le_NN_avance_masque

args = parser.parse_args()


#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#
print("---" * 100)
writer, device, rng, path_save_model, path_save_model_train, name_input = init_all_for_run(args)


print("LOAD CIPHER")
print()
cipher = init_cipher(args)
creator_data_binary = Create_data_binary(args, cipher, rng)

#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("---" * 100)
print("STEP 1 : LOAD/ TRAIN NN REF")
print()
print("COUNTINUOUS LEARNING: "+ str(args.countinuous_learning) +  " | CURRICULUM LEARNING: " +  str(args.curriculum_learning) + " | MODEL: " + str(args.type_model))
print()

"""nombre_round_eval = args.nombre_round_eval
args.nombre_round_eval = nombre_round_eval - 2
nn_model_ref2 = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)
nn_model_ref2.epochs = 10
nn_model_ref2.train_general(name_input)
args.nombre_round_eval = nombre_round_eval - 1
nn_model_ref3 = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)

Esempio n. 2

parser.add_argument("--diff", default=config.train_nn.diff, type=str2hexa)


args = parser.parse_args()


#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#
print("---" * 100)
writer, device, rng, path_save_model, path_save_model_train, name_input = init_all_for_run(args)


print("LOAD CIPHER")
print()
cipher = init_cipher(args)
creator_data_binary = Create_data_binary(args, cipher, rng)

#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("---" * 100)
print("STEP 1 : LOAD/ TRAIN NN REF")
print()
print("COUNTINUOUS LEARNING: "+ str(args.countinuous_learning) +  " | CURRICULUM LEARNING: " +  str(args.curriculum_learning) + " | MODEL: " + str(args.type_model))
print()

"""nombre_round_eval = args.nombre_round_eval
args.nombre_round_eval = nombre_round_eval - 2
nn_model_ref2 = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)
nn_model_ref2.epochs = 10
nn_model_ref2.train_general(name_input)
args.nombre_round_eval = nombre_round_eval - 1
nn_model_ref3 = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)

Esempio n. 3

File: features_as_input_for_mask.py Progetto: AdrienBenamira/Refactoring_le_NN_avance_masque

args = parser.parse_args()

args.logs_tensorboard = args.logs_tensorboard.replace("test", "feature_as_inpput")



#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#
print("---" * 100)
writer, device, rng, path_save_model, path_save_model_train, name_input = init_all_for_run(args)


print("LOAD CIPHER")
print()
cipher = init_cipher(args)
creator_data_binary = Create_data_binary(args, cipher, rng)

#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("---" * 100)
print("STEP 1 : LOAD/ TRAIN NN REF")
print()
print("COUNTINUOUS LEARNING: "+ str(args.countinuous_learning) +  " | CURRICULUM LEARNING: " +  str(args.curriculum_learning) + " | MODEL: " + str(args.type_model))
print()

"""nombre_round_eval = args.nombre_round_eval
args.nombre_round_eval = nombre_round_eval - 2
nn_model_ref2 = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)
nn_model_ref2.epochs = 10
nn_model_ref2.train_general(name_input)
args.nombre_round_eval = nombre_round_eval - 1
nn_model_ref3 = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)