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 run(session, config, model, loader, verbose=False):
total_cost = 0.
total_auc = 0.
total_mayauc = 0.
num_ = 0.
mean_p = 0.
mean_n = 0.
c_p = 0.
c_n = 0.
for batch in loader.generate_batch_data(batchsize=model.batch_size,
mode=model.mode):
batch_id, batch_num, feature_batch, label_batch = batch
feed = {model.input_x: feature_batch, model.input_y: label_batch}
out = [
model.cost, model.optimizer, model.auc_result, model.auc_opt,
model.predict
]
output = session.run(out, feed)
cost, _, auc, _, s_pos = output
if model.mode == "Train":
auc = 0.
total_cost += cost
total_auc = auc
num_ += 1.
if verbose and batch_id % int(
batch_num / 5.) == 1 and model.mode == "Valid":
INFO_LOG("{}/{}, cost: {}, auc: {}".format(
batch_id,
batch_num,
total_cost / num_,
auc # total_auc / num_
))
if model.mode == "Valid":
for idx in range(model.batch_size):
label = int(label_batch[idx][0])
prediction = s_pos[idx][0]
if label == 1:
mean_p += prediction
c_p += 1.
elif label == 0:
mean_n += prediction
c_n += 1.
if model.mode == "Valid":
pass
# print "mean_p : {}, mean_n : {}".format(mean_p / c_p, mean_n / c_n)
return total_cost / num_, total_auc
def main(_):
loader = Loader(flag="azenuz_small")
config = Config(loader, flag="azenuz_small")
config.gpu = 1
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()
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_logloss = 1.
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 % 300 == 0 and epoch != 0:
loader.change_data_list(loader.increase_data_idx())
def run(session, config, model, loader, verbose=False):
total_cost = 0.
total_auc = 0.
total_mayauc = 0.
accuracy = 0.
accuracy1 = 0.
total_accuracy = 0.
num_ = 0.
tp = 0.
tn = 0.
fp = 0.
fn = 0.
mean_p = 0.
mean_n = 0.
c_p = 0.
c_n = 0.
for batch in loader.generate_batch_data(batchsize=model.batch_size,
mode=model.mode):
batch_id, batch_num, feature_batch, label_batch = batch
feed = {model.input_x: feature_batch, model.input_y: label_batch}
# print model.input_x, feature_batch
# print model.input_y, label_batch
out = [
model.cost, model.optimizer, model.auc_result, model.auc_opt,
model.predict
]
output = session.run(out, feed)
# print output
cost, _, auc, _, s_pos = output
if model.mode == "Train":
auc = 0.
total_cost += cost
total_auc += auc
# total_mayauc += may_auc
# print list(label_batch)
# print s_pos
num_ += 1.
if verbose and batch_id % int(
batch_num / 5.) == 1 and model.mode == "Valid":
INFO_LOG("{}/{}, cost: {}, auc: {}".format(
batch_id,
batch_num,
total_cost / num_,
auc # total_auc / num_
))
# print list(label_batch)
# print s_pos
# print model.input_x, feature_batch
# print model.input_y, label_batch
if model.mode == "Valid":
for idx in range(model.batch_size):
label = int(label_batch[idx][0])
prediction = s_pos[idx][0]
# print label, prediction
# print label
# print label == 1, label == 0
if label == 0:
accuracy1 += 1.
if label == 1:
mean_p += prediction
c_p += 1.
if prediction > 0.5:
accuracy += 1.
elif label == 0:
mean_n += prediction
c_n += 1.
if prediction < 0.5:
accuracy += 1.
# print c_p, c_n
print "mean_p : {}, mean_n : {}".format(mean_p / c_p, mean_n / c_n)
print c_p, c_n
print "acc is:", accuracy1 / (num_ * model.batch_size)
return total_cost, total_auc / num_
