31 lines
699 B
Python
31 lines
699 B
Python
import pandas
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import os
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from keras.applications.densenet import layers
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from sklearn.model_selection import train_test_split
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import tensorflow
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EPOCHS = int(os.environ['EPOCHS'])
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train_data_x = pandas.read_csv('./X_train.csv')
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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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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') |