diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 5808446..40f7d01 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -151,7 +151,7 @@
   <component name="PropertiesComponent">
     <property name="WebServerToolWindowFactoryState" value="false" />
     <property name="aspect.path.notification.shown" value="true" />
-    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588157865997" />
+    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588157914652" />
     <property name="go.gopath.indexing.explicitly.defined" value="true" />
     <property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" />
     <property name="nodejs_npm_path_reset_for_default_project" value="true" />
diff --git a/main.py b/main.py
index 173145d..9d6b71b 100644
--- a/main.py
+++ b/main.py
@@ -164,11 +164,11 @@ def main(config):
 
 	if config.do_train:
 		train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
-		model = train(config, train_X, train_Y, valid_X, valid_Y)
-
+		train(config, train_X, train_Y, valid_X, valid_Y)
 
+	if config.do_predict:
 		test_X, test_Y = data_gainer.get_test_data(return_label_data=True)
-		pred_result = predict(config, test_X, model)
+		pred_result = predict(config, test_X)
 		draw(config, data_gainer, pred_result)
 
 
diff --git a/model/model_tensorflow.py b/model/model_tensorflow.py
index 8d7be13..444d6b1 100644
--- a/model/model_tensorflow.py
+++ b/model/model_tensorflow.py
@@ -76,19 +76,19 @@ def train(config, train_X, train_Y, valid_X, valid_Y):
 				if bad_epoch >= config.patience:
 					print(" The training stops early in epoch {}".format(epoch))
 					break
-		return model
 
 
-def predict(config, test_X, model):
+def predict(config, test_X):
 	config.dropout_rate = 0.1
+	tf.reset_default_graph()
 
-	#with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE):
-	#	model = Model(config)
+	with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE):
+		model = Model(config)
 
 	test_len = len(test_X)
 	with tf.Session() as sess:
-		#module_file = tf.train.latest_checkpoint(config.model_save_path)
-		#model.saver.restore(sess, module_file)
+		module_file = tf.train.latest_checkpoint(config.model_save_path)
+		model.saver.restore(sess, module_file)
 
 		result = np.zeros((test_len * config.time_step, config.output_size))
 		for step in range(test_len):