2023-05-10 20:30:03 +02:00
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import pandas
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import os
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2023-05-11 21:24:52 +02:00
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from sacred import Experiment
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from sacred.observers import MongoObserver
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from sacred.observers import FileStorageObserver
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2023-05-10 20:30:03 +02:00
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from keras.applications.densenet import layers
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import tensorflow
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2023-05-11 21:24:52 +02:00
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exint = Experiment('z-s434686-training')
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exint.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',
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db_name='sacred'))
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exint.observers.append(FileStorageObserver('my_runs'))
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@exint.config
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def my_config():
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EPOCHS = int(os.environ['EPOCHS'])
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train_data_x = exint.open_resource('./X_train.csv', 'r')
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games_all = train_data_x.copy()
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games_predict = train_data_x.pop('User_Score')
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normalize = layers.Normalization()
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_run.info["epochs"] = EPOCHS
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@exint.main
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def my_main(EPOCHS, games_all, games_predict,normalize):
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_run.info["prepare_message_ts"] = str(datetime.now())
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normalize.adapt(games_all)
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norm_games_model = tensorflow.keras.Sequential([
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normalize,
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layers.Dense(64),
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layers.Dense(1)
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])
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norm_games_model.compile(
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loss=tensorflow.keras.losses.MeanSquaredError(),
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optimizer=tensorflow.keras.optimizers.Adam())
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norm_games_model.fit(games_all, games_predict, epochs=EPOCHS)
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norm_games_model.save('test')
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print(f'done:')
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exint.run()
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exint.add_artifact("test")
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exint.add_source_file('train.py')
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