import pandas
import os

from keras.applications.densenet import layers

from sklearn.model_selection import train_test_split
import tensorflow


train_data_x = pandas.read_csv('./X_train.csv')


games_all = train_data_x.copy()
games_predict = train_data_x.pop('User_Score')
normalize = layers.Normalization()
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.fit(games_all, games_predict, epochs=500)

norm_games_model.save('test')