43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import pandas as pd
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import numpy as np
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import tensorflow as tf
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import os.path
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.layers.experimental import preprocessing
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pd.set_option("display.max_columns", None)
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# Wczytanie danych
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train_data = pd.read_csv("./train.csv")
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test_data = pd.read_csv("./test.csv")
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# Stworzenie modelu
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columns_to_use = ['Year', 'Runtime', 'Netflix']
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train_X = tf.convert_to_tensor(train_data[columns_to_use])
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train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
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test_X = tf.convert_to_tensor(test_data[columns_to_use])
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test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
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normalizer = preprocessing.Normalization(input_shape=[3,])
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normalizer.adapt(train_X)
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if os.path.isdir('linear_regression'):
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model = keras.models.load_model('linear_regression')
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else:
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model = keras.Sequential([
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keras.Input(shape=(len(columns_to_use),)),
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normalizer,
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layers.Dense(30, activation='relu'),
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layers.Dense(10, activation='relu'),
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layers.Dense(25, activation='relu'),
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layers.Dense(1)
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])
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model.compile(loss='mean_absolute_error',
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optimizer=tf.keras.optimizers.Adam(0.001))
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model.fit(train_X, train_Y, verbose=0, epochs=100)
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model.save('linear_regression') |