Dodanie 'training.py'

training.py

@@ -0,0 +1,50 @@
+import pandas
+import os
+from sacred import Experiment
+from sacred.observers import MongoObserver
+from sacred.observers import FileStorageObserver
+from keras.applications.densenet import layers
+import tensorflow
+import random
+
+exint = Experiment('z-s434743-training')
+
+exint.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',
+                                  db_name='sacred'))                                
+exint.observers.append(FileStorageObserver('z-s434743-training/master'))
+
+@exint.config
+def my_config():
+    EPOCHS = int(os.environ['EPOCHS'])
+
+
+@exint.main
+def my_main(EPOCHS, _run):
+    _run.info["epochs"] = EPOCHS,
+    normalize = layers.Normalization()
+    train_data_x = pandas.read_csv('./X_train.csv')
+    _run.info["data"] = train_data_x
+    movies_all = train_data_x.copy()
+    movies_predict = train_data_x.pop('audienceScore')
+    normalize.adapt(movies_all)
+    norm_movies_model = tensorflow.keras.Sequential([
+        normalize,
+        layers.Dense(64),
+        layers.Dense(1)
+    ])
+    norm_movies_model.compile(
+        loss=tensorflow.keras.losses.MeanSquaredError(),
+        optimizer=tensorflow.keras.optimizers.Adam())
+
+    norm_movies_model.fit(movies_all, movies_predict, epochs=EPOCHS)
+    counter = 0
+    while counter < 20:
+        counter+=1
+        value = counter * random.randint(5, 5000)
+        _run.log_scalar("training.accuracy", value * 2)
+    norm_movies_model.save('test')
+
+exint.run()
+
+exint.add_artifact('test/saved_model.pb')
+exint.add_source_file('./ium_z434743/train.py')