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Merge pull request '新增了对文件的部分注释' (#10) from xinran/openGauss-server:master into master

This commit is contained in:
xiangxinyong 2022-08-20 22:56:56 +08:00
parent 4b7af41aff 991925bfe1
commit 8e9089b6fe
2 changed files with 8 additions and 2 deletions
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9
src/gausskernel/dbmind/tools/components/predictor/python/model.py
View File

@ -539,13 +539,14 @@ class RnnModel():
keras.backend.clear_session() keras.backend.clear_session()
set_session(self.session) set_session(self.session)
with self.graph.as_default(): with self.graph.as_default():
# Judge whether the model needs to be initialized according to the changes of the model input and output dimensions.
feature, label, need_init = self.parse(filename) feature, label, need_init = self.parse(filename)
os.environ['CUDA_VISIBLE_DEVICES'] = '0' os.environ['CUDA_VISIBLE_DEVICES'] = '0'
epsilon = self.model_info.make_epsilon() epsilon = self.model_info.make_epsilon()
if need_init: if need_init:# Cold start training
epoch_start = 0 epoch_start = 0
self.model = self._build_model(epsilon) self.model = self._build_model(epsilon)
else: else:# Incremental training
epoch_start = int(self.model_info.last_epoch) epoch_start = int(self.model_info.last_epoch)
ratio_error = ratio_error_loss_wrapper(epsilon) ratio_error = ratio_error_loss_wrapper(epsilon)
ratio_acc_2 = ratio_error_acc_wrapper(epsilon, 2) ratio_acc_2 = ratio_error_acc_wrapper(epsilon, 2)
@ -556,12 +557,16 @@ class RnnModel():
log_path = os.path.realpath(os.path.join(settings.PATH_LOG, self.model_info.model_name + '_log.json')) log_path = os.path.realpath(os.path.join(settings.PATH_LOG, self.model_info.model_name + '_log.json'))
if not os.path.exists(log_path): if not os.path.exists(log_path):
os.mknod(log_path, mode=0o600) os.mknod(log_path, mode=0o600)
# Training logging callback function
json_logging_callback = LossHistory(log_path, self.model_info.model_name, self.model_info.last_epoch) json_logging_callback = LossHistory(log_path, self.model_info.model_name, self.model_info.last_epoch)
# Data segmentation
X_train, X_val, y_train, y_val = \ X_train, X_val, y_train, y_val = \
train_test_split(feature, label, test_size=0.1) train_test_split(feature, label, test_size=0.1)
# model training
self.model.fit(X_train, y_train, epochs=self.model_info.last_epoch, self.model.fit(X_train, y_train, epochs=self.model_info.last_epoch,
batch_size=int(self.model_info.batch_size), validation_data=(X_val, y_val), batch_size=int(self.model_info.batch_size), validation_data=(X_val, y_val),
verbose=0, initial_epoch=epoch_start, callbacks=[json_logging_callback]) verbose=0, initial_epoch=epoch_start, callbacks=[json_logging_callback])
# save model
self.model.save(self.model_info.model_path) self.model.save(self.model_info.model_path)
val_pred = self.model.predict(X_val) val_pred = self.model.predict(X_val)
val_re = get_ratio_errors_general(val_pred, y_val, epsilon) val_re = get_ratio_errors_general(val_pred, y_val, epsilon)

1
src/gausskernel/dbmind/tools/components/sqldiag/algorithm/duration_time_model/template.py
View File

@ -27,6 +27,7 @@ from . import AbstractModel
class TemplateModel(AbstractModel): class TemplateModel(AbstractModel):
# Initialize algorithm parameters
def __init__(self, params): def __init__(self, params):
super().__init__(params) super().__init__(params)
self.bias = 1e-5 self.bias = 1e-5