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@ -103,7 +103,7 @@ plt.legend(['train_loss', 'val_loss'])
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plt.title('Loss by epochs')
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plt.show()
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```
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### Пункт 7
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Применили обученную модель к тестовым данным. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных.
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```python
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@ -141,11 +141,15 @@ plt.legend(['train_loss', 'val_loss'])
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plt.title('Loss by epochs')
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plt.show()
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# Оценка качества работы модели на тестовых данных
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scores = model_1h100.evaluate(X_test, y_test)
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print('Loss on test data:', scores[0])
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print('Accuracy on test data:', scores[1])
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```
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Loss on test data: 0.1981867104768753
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Accuracy on test data: 0.9398000240325928
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При 300 нейронах
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```python
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@ -177,6 +181,10 @@ scores = model_1h300.evaluate(X_test, y_test)
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print('Loss on test data:', scores[0])
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print('Accuracy on test data:', scores[1])
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```
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Loss on test data: 0.22451213002204895
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Accuracy on test data: 0.9320999979972839
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При 500 нейронах
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```python
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