模型文件改变

BiddingKG.iml

@@ -2,13 +2,12 @@
 <module type="JAVA_MODULE" version="4">
   <component name="FacetManager">
     <facet type="Python" name="Python">
-      <configuration sdkName="Python 3.5 (dl_nlp)" />
+      <configuration sdkName="" />
     </facet>
   </component>
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.5 (dl_nlp)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.5 (py35)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
-    <orderEntry type="library" exported="" name="Python 3.5 (dl_nlp) interpreter library" level="application" />
   </component>
 </module>

BiddingKG/dl/interface/modelFactory.py

@@ -159,7 +159,7 @@ class Model_person_classify():
     def __init__(self,lazyLoad=getLazyLoad()):
         if USE_PAI_EAS:
             lazyLoad = True
-        self.model_person_file = os.path.dirname(__file__)+"/../person/models/model_person.model.hdf5"
+        self.model_person_file = os.path.dirname(__file__)+"/../person/models/model_person_classify_fjs.model.hdf5"
         self.model_person = None
         self.sess_person = tf.Session(graph=tf.Graph())
         if not lazyLoad:
@@ -183,11 +183,11 @@ class Model_person_classify():
       return self.model_person
             
       
-      '''
-        if self.model_person is None:
-            self.model_person = models.load_model(self.model_person_file,custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
-        return self.model_person
-      '''
+    '''
+      if self.model_person is None:
+          self.model_person = models.load_model(self.model_person_file,custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
+      return self.model_person
+    '''
     '''
     def load_weights(self):
         model = self.getModel()
@@ -195,7 +195,7 @@ class Model_person_classify():
     '''
     
     def encode(self,tokens,begin_index,end_index,**kwargs):
-        return embedding(spanWindow(tokens=tokens,begin_index=begin_index,end_index=end_index,size=10),shape=(2,10,128))
+        return embedding(spanWindow(tokens=tokens,begin_index=begin_index,end_index=end_index,size=35),shape=(2,35,128))
     
     def predict(self,x):
         x = np.transpose(np.array(x),(1,0,2,3))

BiddingKG/dl/interface/predictor.py

@@ -25,6 +25,7 @@ dict_predictor = {"codeName":{"predictor":None,"Lock":RLock()},
               "roleRule":{"predictor":None,"Lock":RLock()},
                   "form":{"predictor":None,"Lock":RLock()}}
 
+
 def getPredictor(_type):
     if _type in dict_predictor:
         with dict_predictor[_type]["Lock"]:

BiddingKG/dl/test/test4.py

@@ -171,6 +171,7 @@ if __name__=="__main__":
     a = time.time()
     print("start")
     # print(predict("12",content))
+    # 评审专家 100005322
     print(predict("投诉处理公告", text))
     #test("12",text)
     print("takes",time.time()-a)

BiddingKG/dl/test/test_model_fjs.py

@@ -8,6 +8,7 @@ from matplotlib import pyplot
 
 from BiddingKG.dl.common.models import *
 from sklearn.metrics import classification_report
+from BiddingKG.dl.interface.predictor import h5_to_graph
 
 
 sys.path.append(os.path.abspath("../.."))
@@ -561,10 +562,25 @@ def plotTrainTestLoss(history_model):
     pyplot.show()
 
 
+def hdf52savemodel():
+    filepath = 'model_person_classify_fjs.model.hdf5'
+    with tf.Graph().as_default() as graph:
+        time_model = models.load_model(filepath, custom_objects={'precision': precision, 'recall': recall, 'f1_score': f1_score})
+        with tf.Session() as sess:
+            sess.run(tf.global_variables_initializer())
+            h5_to_graph(sess, graph, filepath)
+            tf.saved_model.simple_save(sess,
+                                       "./person_save_model/",
+                                       inputs={"input0":time_model.input[0],
+                                               "input1":time_model.input[1]},
+                                       outputs={"outputs":time_model.output})
+
+
 if __name__ == "__main__":
     # getData()
     # train()
-    predict()
-    # predict2Csv()
+    # predict()
+    predict2Csv()
+    # hdf52savemodel()
 
     # getData3()