37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
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import tensorflow as tf
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from tensorflow import keras
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from matplotlib import pyplot as plt
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from matplotlib.ticker import MaxNLocator
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import numpy as np
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import pandas as pd
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# Załadowanie modelu z pliku
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model = keras.models.load_model('lego_reg_model')
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# Załadowanie zbioru testowego
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data_test = pd.read_csv('lego_sets_clean_test.csv')
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test_piece_counts = np.array(data_test['piece_count'])
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test_prices = np.array(data_test['list_price'])
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# Prosta ewaluacja (mean absolute error)
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test_results = model.evaluate(
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test_piece_counts,
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test_prices, verbose=0)
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# Zapis wartości liczbowej metryki do pliku
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with open('eval_results.txt', 'a+') as f:
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f.write(str(test_results) + '\n')
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# Wygenerowanie i zapisanie do pliku wykresu
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with open('eval_results.txt') as f:
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scores = [float(line) for line in f if line]
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builds = list(range(1, len(scores) + 1))
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plot = plt.plot(builds, scores)
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plt.xlabel('Build number')
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plt.xticks(range(1, len(scores) + 1))
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plt.ylabel('Mean absolute error')
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plt.title('Model error by build')
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plt.savefig('error_plot.jpg')
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plt.show()
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