45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import sys
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
import os.path
|
|
|
|
from tensorflow import keras
|
|
from tensorflow.keras import layers
|
|
from tensorflow.keras.layers.experimental import preprocessing
|
|
|
|
arguments = sys.argv[1:]
|
|
|
|
verbose = int(arguments[0])
|
|
epochs = int(arguments[1])
|
|
|
|
pd.set_option("display.max_columns", None)
|
|
|
|
# Wczytanie danych
|
|
train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")
|
|
|
|
# Stworzenie modelu
|
|
columns_to_use = ['Year', 'Runtime', 'Netflix']
|
|
train_X = tf.convert_to_tensor(train_data[columns_to_use])
|
|
train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
|
|
|
|
normalizer = preprocessing.Normalization(input_shape=[3,])
|
|
normalizer.adapt(train_X)
|
|
|
|
model = keras.Sequential([
|
|
keras.Input(shape=(len(columns_to_use),)),
|
|
normalizer,
|
|
layers.Dense(30, activation='relu'),
|
|
layers.Dense(10, activation='relu'),
|
|
layers.Dense(25, activation='relu'),
|
|
layers.Dense(1)
|
|
])
|
|
|
|
model.compile(loss='mean_absolute_error',
|
|
optimizer=tf.keras.optimizers.Adam(0.001),
|
|
metrics=[tf.keras.metrics.RootMeanSquaredError()])
|
|
|
|
model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
|
|
|
|
model.save('linear_regression.h5') |