Dodanie obsługi parametru epochs do lab 06 zad 1
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## Klasyfikacja jakości diamentu
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import os
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import pandas as pd
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import numpy as np
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import pickle
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@ -10,6 +10,9 @@ from tensorflow.keras.callbacks import History
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from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
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from tensorflow.keras.utils import to_categorical
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#Wyświetlenie zbioru danych
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epochs = int(os.environ.get('EPOCHS', 10))
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# Wczytanie danych
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data_train = pd.read_csv('dane/diamonds_train.csv')
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data_test = pd.read_csv('dane/diamonds_test.csv')
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@ -74,7 +77,7 @@ model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['ac
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# Trenowanie
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history = History()
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model.fit(X_train_scaled, y_train_encoded, epochs=10, batch_size=32, validation_data=(X_val_scaled, y_val_encoded), callbacks=[history])
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model.fit(X_train_scaled, y_train_encoded, epochs=epochs, batch_size=32, validation_data=(X_val_scaled, y_val_encoded), callbacks=[history])
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# Zapisywanie modelu do pliku
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saved_model = [model,
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