def main(_):
loader = Loader(flag="azenuz_small")
config = Config(loader, flag="azenuz_small")
config.gpu = 0
if platform.system() == 'Linux':
gpuid = config.gpu
os.environ["CUDA_VISIBLE_DEVICES"] = '{}'.format(gpuid)
device = '/gpu:' + str(gpuid)
else:
device = '/cpu:0'
i = 0
graph = tf.Graph()
with graph.as_default():
trainm = CTR_ggnn(config, device, loader, "Train")
testm = CTR_ggnn(config, device, loader, "Valid")
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
session_config.gpu_options.allow_growth = True
with tf.Session(graph=graph, config=session_config) as session:
# session.run(tf.global_variables_initializer())
CTR_GNN_loader(session, config)
best_auc = 0.
best_epoch = 0
auc = 0.
for epoch in range(config.epoch_num):
# cost, auc = run(session, config, trainm, loader, verbose=True)
# INFO_LOG("Epoch %d Train AUC %.3f" % (epoch + 1, auc))
# INFO_LOG("Epoch %d Train costs %.3f" %
# (epoch, cost))
session.run(tf.local_variables_initializer())
cost, auc = run(session, config, testm, loader, verbose=True)
INFO_LOG("Epoch %d Valid AUC %.3f" % (epoch, auc))
INFO_LOG("Epoch %d Valid cost %.3f" % (epoch, cost))
# #
if best_auc < auc:
best_auc = auc
best_epoch = epoch
# CTR_GNN_saver(session, config, best_auc, best_epoch)
INFO_LOG("*** best AUC now is %.3f in %d epoch" %
(best_auc, best_epoch))
def main(_):
loader = Loader(flag="azenuz_small")
config = Config(loader, flag="azenuz_small")
config.gpu = 0
if platform.system() == 'Linux':
gpuid = config.gpu
os.environ["CUDA_VISIBLE_DEVICES"] = '{}'.format(gpuid)
device = '/gpu:' + str(gpuid)
else:
device = '/cpu:0'
lr_updater = LearningRateUpdater(config.learning_rate, config.decay,
config.decay_epoch)
i = 0
graph = tf.Graph()
INFO_LOG("build model")
with graph.as_default():
trainm = CTR_ggnn(config, device, loader, "Train")
testm = CTR_ggnn(config, device, loader, "Valid")
INFO_LOG("finish build model")
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
session_config.gpu_options.allow_growth = True
with tf.Session(graph=graph, config=session_config) as session:
# session.run(tf.global_variables_initializer())
CTR_GNN_loader(session, config)
best_auc = 0.
best_logloss = 0.
best_epoch_auc = 0.
best_epoch_logloss = 0.
auc = 0.
for epoch in range(config.epoch_num):
trainm.update_lr(session, lr_updater.get_lr())
cost, auc = run(session, config, trainm, loader, verbose=True)
INFO_LOG("Epoch %d Train AUC %.3f" % (epoch + 1, auc))
INFO_LOG("Epoch %d Train costs %.3f" % (epoch, cost))
session.run(tf.local_variables_initializer())
cost, auc = run(session, config, testm, loader, verbose=True)
INFO_LOG("Epoch %d Valid AUC %.3f" % (epoch, auc))
INFO_LOG("Epoch %d Valid cost %.3f" % (epoch, cost))
# #
lr_updater.update(auc, epoch)
if best_auc < auc:
best_auc = auc
best_epoch_auc = epoch
CTR_GNN_saver(session, config, best_auc, best_epoch_auc)
if best_logloss > cost:
best_logloss = cost
best_epoch_logloss = epoch
# CTR_GNN_saver(session, config, best_epoch_logloss, best_epoch_logloss)
INFO_LOG("*** best AUC now is %.3f in %d epoch" %
(best_auc, best_epoch_auc))
INFO_LOG("*** best logloss now is %.3f in %d epoch" %
(best_logloss, best_epoch_logloss))
if epoch % 5 == 0 and epoch != 0:
loader.change_data_list(loader.increase_data_idx())