archive model

Jenkins/Jenkinsfile.training

@@ -24,7 +24,7 @@ node {
             }
         }
         stage('Archive artifacts') {
-            archiveArtifacts "neural_network_evaluation.txt, neural_network_model"
+            archiveArtifacts "neural_network_evaluation.txt, model/**"
         }
     }
 }

Scripts/train_neural_network.py

@@ -1,4 +1,5 @@
 #!/usr/bin/python
+import datetime
 import os
 import pprint
 import sys
@@ -40,8 +41,9 @@ def load_trained_model(abs_path, model_name):
     return load_model(os.path.join(abs_path, model_name))
 
 
-def save_model(model, abs_path, model_name):
-    model.save(os.path.join(abs_path, model_name), save_format='h5')
+def save_model(model):
+    model_name = 'neural_net_' + datetime.datetime.today().strftime('%d-%b-%Y-%H:%M:%S')
+    model.save(os.path.join(os.getcwd(), 'model', model_name), save_format='h5', overwrite=True)
 
 
 def train_model(model, x_train, y_train):
@@ -82,7 +84,7 @@ def main():
     x_train, x_test, vocab_size = tokenize(pd.concat([x_train, x_test]), x_train, x_test, 100)
     model = get_model(50, vocab_size)
     train_model(model, x_train, y_train)
-    save_model(model, abs_data_path, 'neural_network_model')
+    save_model(model)
     evaluate_and_save(model, x_test, y_test, abs_data_path)