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