def __init__(self,
settings: utilities.ExperimentSettings.ExperimentSettings):
self.__space = settings.initial_space
self.__selector = settings.selector
self.__networks = []
self.__nodes = []
self.__settings = settings
self._test_dao = test_dao.TestDAO(db='database.db')
self.__test_result_dao = test_result_dao.TestResultDAO(
db='database.db')
self.__test_model_dao = test_model_dao.TestModelDAO(db='database.db')
self.__best_network = None
if self.__settings.loadedNetwork == None:
self.__best_network = self.__create_network(
dna=settings.initial_dna)
else:
self.__best_network = self.__settings.loadedNetwork
self.__actions = []
self.__fileManager = FileManager.FileManager()
if self.__settings.save_txt == True:
self.__fileManager.setFileName(self.__settings.test_name)
self.__fileManager.writeFile("")
self.__memoryManager = MemoryManager.MemoryManager()
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import matplotlib.backends.backend_pdf as pdf_manager
from DAO.database.dao import test_model_dao, test_dao, test_result_dao
import os
testDAO = test_dao.TestDAO()
testModelDAO = test_model_dao.TestModelDAO()
testResultDAO = test_result_dao.TestResultDAO()
test_list = testDAO.findAll()
def findAccByAlaiRange(min_alai, max_alai, modelList, last_acc):
acc = last_acc
for model in modelList:
if model.current_alai_time >= min_alai and model.current_alai_time <= max_alai:
acc = model.model_weight
if acc == 0:
acc = last_acc
return acc
tests_to_graph = int(input("Enter the number of tests to graph: "))
selected_test_list = []
def convertPosition(y):
convert = (y-0.7)*(1/0.3)
return convert
def __init__(self, db="database_product.db"):
self.__testDao = test_dao.TestDAO(db)
self.__value = None
def __init__(self, testId):
self.__testId = testId
self.__testDao = test_dao.TestDAO()
self.__testResultDao = test_result_dao.TestResultDAO()
self.__value = None
def run(status):
loaded_network = bool(input("any input to run loaded network"))
print("Loaded network: ", loaded_network)
status.Transfer = tran.TransferRemote(status, 'remote2local.txt',
'local2remote.txt')
#status.Transfer.readLoad()
print(f'status.settings is {status.settings}')
create_objects(status, loaded_network)
print('The value of typos after loading is')
print(status.typos)
print("objects created")
status.print_DNA()
status.Transfer.un_load()
status.Transfer.write()
k = 0
test_id = 0
testDao = test_dao.TestDAO()
testResultDao = test_result_dao.TestResultDAO()
testModelDao = test_model_dao.TestModelDAO()
print("cuda=", status.cuda)
print("max layers: ", status.max_layer_conv2d)
settings = status.settings
if loaded_network == False:
network = nw.Network(
status.Center,
cuda_flag=settings.cuda,
momentum=settings.momentum,
weight_decay=settings.weight_decay,
enable_activation=settings.enable_activation,
enable_track_stats=settings.enable_track_stats,
dropout_value=settings.dropout_value,
dropout_function=settings.dropout_function,
enable_last_activation=settings.enable_last_activation,
version=settings.version,
eps_batchnorm=settings.eps_batchorm)
print("starting pre-training")
print("iterations per epoch = ", status.iterations_per_epoch)
dt_array = status.Alai.get_increments(20 * status.iterations_per_epoch)
if status.save2database == True:
test_id = testDao.insert(testName=status.experiment_name,
dt=status.dt_Max,
dt_min=status.dt_min,
batch_size=status.S,
max_layers=status.max_layer_conv2d,
max_filters=status.max_filter,
max_filter_dense=status.max_filter_dense,
max_kernel_dense=status.max_kernel_dense,
max_pool_layer=status.max_pool_layer,
max_parents=status.max_parents)
network.training_custom_dt(dataGenerator=status.Data_gen,
dt_array=dt_array,
ricap=settings.ricap,
evalLoss=settings.evalLoss)
else:
path = os.path.join("saved_models", "product_database",
"7_test_final_experiment_model_6044")
network = NetworkStorage.load_network(fileName=None,
settings=settings,
path=path)
network.generate_accuracy(status.Data_gen)
acc = network.get_accuracy()
print("Acc loaded network: ", acc)
print("Alai time loaded: ", status.Alai.computeTime())
L_1 = status.Alai.computeTime() // status.save_space_period
L_2 = status.Alai.computeTime() // status.save_net_period
print("L_1= ", L_1)
print("L_2= ", L_2)
time.sleep(2)
if status.save2database == True:
test_id = testDao.insert(testName=status.experiment_name,
dt=status.dt_Max,
dt_min=status.dt_min,
batch_size=status.S,
max_layers=status.max_layer_conv2d,
max_filters=status.max_filter,
max_filter_dense=status.max_filter_dense,
max_kernel_dense=status.max_kernel_dense,
max_pool_layer=status.max_pool_layer,
max_parents=status.max_parents)
status.stream.add_node(network.dna)
status.stream.link_node(network.dna, network)
if status.save2database == True and loaded_network == False:
dna_graph = status.Dynamics.phase_space.DNA_graph
testResultDao.insert(idTest=test_id,
iteration=0,
dna_graph=dna_graph,
current_alai_time=status.Alai.computeTime(),
reset_count=status.Alai.reset_count)
save_model(status, 0, testModelDao, test_id, TrainingType.PRE_TRAINING)
#update(status)
while False:
update(status)
status.Transfer.un_load()
status.Transfer.write()
transfer = status.Transfer.status_transfer
k = k + 1
pass
L_1 = 1
L_2 = 1
save_17_layers = True
save_18_layers = True
save_19_layers = True
save_24_layers = True
save_25_layers = True
save_26_layers = True
save_27_layers = True
save_30_layers = True
save_33_layers = True
save_36_layers = True
save_39_layers = True
save_42_layers = True
save_45_layers = True
save_48_layers = True
save_51_layers = True
if loaded_network == True:
save_6_layers = False
save_17_layers = False
save_18_layers = False
save_19_layers = False
L_1 = status.Alai.computeTime() // status.save_space_period
L_2 = status.Alai.computeTime() // status.save_net_period
while k < status.max_iter:
#\begin{with gui}
#status.Transfer.readLoad()
#\end{with gui}
#\begin{wituhout gui}
status.active = True
#\end{without gui}
if status.active:
update(status)
print(f'The iteration number is: {k}')
#if k % 20 == 0:
# status.print_accuracy()
#status.print_energy()
status.print_predicted_actions()
if status.Alai:
status.Alai.update()
#status.print_particles()
#status.print_particles()
#status.print_max_particles()
#print(status.typos)
#status.print_signal()
#status.print_difussion_filed()
# print_nets(status)
# time.sleep(0.5)0
center_dna = status.Dynamics.phase_space.center()
layers_count = 0
if center_dna is not None:
layers_count = countLayers(center_dna)
print("current layers: ", layers_count)
if status.save2database == True:
if status.Alai.computeTime() >= L_1 * status.save_space_period:
print("saving space: ", L_1)
L_1 += 1
dna_graph = status.Dynamics.phase_space.DNA_graph
testResultDao.insert(
idTest=test_id,
iteration=k + 1,
dna_graph=dna_graph,
current_alai_time=status.Alai.computeTime(),
reset_count=status.Alai.reset_count)
if status.Alai.computeTime() >= L_2 * status.save_net_period:
print("saving model: ", L_2)
L_2 += 1
save_model(status, k + 1, testModelDao, test_id,
TrainingType.MUTATION)
if layers_count >= 17 and save_17_layers == True:
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
save_17_layers = False
elif layers_count >= 18 and save_18_layers == True:
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
save_18_layers = False
elif layers_count >= 19 and save_19_layers == True:
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
save_19_layers = False
elif layers_count >= 24 and save_24_layers == True:
save_24_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 25 and save_25_layers == True:
save_25_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 26 and save_26_layers == True:
save_26_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 27 and save_27_layers == True:
save_27_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 30 and save_30_layers == True:
save_30_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 33 and save_33_layers == True:
save_33_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 36 and save_36_layers == True:
save_36_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 39 and save_39_layers == True:
save_39_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 42 and save_42_layers == True:
save_42_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 45 and save_45_layers == True:
save_45_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 48 and save_48_layers == True:
save_48_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
elif layers_count >= 51 and save_51_layers == True:
save_51_layers = False
save_checkpoint(status, testResultDao, layers_count,
test_id, testModelDao, k)
if layers_count >= 39:
print("STOPPED MAX LAYERS: ", layers_count)
break
else:
#print('inactive')
pass
k = k + 1
def run(status):
status.Transfer = tran.TransferRemote(status, 'remote2local.txt',
'local2remote.txt')
#status.Transfer.readLoad()
create_objects(status)
print('The value of typos after loading is')
print(status.typos)
print("objects created")
status.print_DNA()
status.Transfer.un_load()
status.Transfer.write()
k = 0
#update(status)
test_id = 0
testDao = test_dao.TestDAO()
testResultDao = test_result_dao.TestResultDAO()
testModelDao = test_model_dao.TestModelDAO()
if status.save2database == True:
test_id = testDao.insert(testName=status.experiment_name,
periodSave=status.save_space_period,
dt=status.dt_Max,
total=status.max_iter,
periodCenter=1)
while False:
update(status)
status.Transfer.un_load()
status.Transfer.write()
transfer = status.Transfer.status_transfer
k = k + 1
pass
while k < status.max_iter:
#\begin{with gui}
#status.Transfer.readLoad()
#\end{with gui}
#\begin{wituhout gui}
status.active = True
#\end{without gui}
if status.active:
update(status)
print(f'The iteration number is: {k}')
#status.print_energy()
status.print_predicted_actions()
if status.Alai:
status.Alai.update()
if status.save2database == True:
if k % status.save_space_period == status.save_space_period - 1:
dna_graph = status.Dynamics.phase_space.DNA_graph
testResultDao.insert(idTest=test_id,
iteration=k + 1,
dna_graph=dna_graph)
if k % status.save_net_period == status.save_net_period - 1:
save_model(status, k + 1, testModelDao, test_id)
#status.print_particles()
#status.print_particles()
#status.print_max_particles()
#print(status.typos)
#status.print_signal()
#status.print_difussion_filed()
# print_nets(status)
# time.sleep(0.5)
else:
#print('inactive')
pass
k = k + 1