Update 'train.py'

train.py

@@ -1,31 +1,44 @@
 import pandas
 import os
-
+from sacred import Experiment
+from sacred.observers import MongoObserver
+from sacred.observers import FileStorageObserver
 from keras.applications.densenet import layers
-
-from sklearn.model_selection import train_test_split
 import tensorflow
 
-EPOCHS = int(os.environ['EPOCHS'])
+exint = Experiment('z-s434686-training')
 
-train_data_x = pandas.read_csv('./X_train.csv')
+exint.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',
+                                  db_name='sacred'))
+exint.observers.append(FileStorageObserver('my_runs'))
+@exint.config
+def my_config():
+    EPOCHS = int(os.environ['EPOCHS'])
+    train_data_x = exint.open_resource('./X_train.csv', 'r')
+    games_all = train_data_x.copy()
+    games_predict = train_data_x.pop('User_Score')
+    normalize = layers.Normalization()
+    _run.info["epochs"] = EPOCHS
 
 
-games_all = train_data_x.copy()
-games_predict = train_data_x.pop('User_Score')
-normalize = layers.Normalization()
-normalize.adapt(games_all)
+@exint.main
+def my_main(EPOCHS, games_all, games_predict,normalize):
+    _run.info["prepare_message_ts"] = str(datetime.now())
+    normalize.adapt(games_all)
+    norm_games_model = tensorflow.keras.Sequential([
+        normalize,
+        layers.Dense(64),
+        layers.Dense(1)
+    ])
+    norm_games_model.compile(
+        loss=tensorflow.keras.losses.MeanSquaredError(),
+        optimizer=tensorflow.keras.optimizers.Adam())
 
-norm_games_model = tensorflow.keras.Sequential([
-  normalize,
-  layers.Dense(64),
-  layers.Dense(1)
-])
+    norm_games_model.fit(games_all, games_predict, epochs=EPOCHS)
 
-norm_games_model.compile(
-    loss=tensorflow.keras.losses.MeanSquaredError(),
-    optimizer=tensorflow.keras.optimizers.Adam())
+    norm_games_model.save('test')
+    print(f'done:')
 
-norm_games_model.fit(games_all, games_predict, epochs=EPOCHS)
-
-norm_games_model.save('test')
+exint.run()
+exint.add_artifact("test")
+exint.add_source_file('train.py')