ium_z434743/training.py

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')