def show_images() -> None:
random.seed(10)
dp = DataProvider.load_from_folder(dataset_folder)
nn = NeuralNet(sizes=[784, 128, 10], epochs=10)
nn.train(dp.get_train_x(), dp.get_hot_encoded_train_y(),
dp.get_test_x(), dp.get_hot_encoded_test_y())
properly_classified, misclassified = nn.get_properly_classified_and_misclassified_images(
dp.get_test_x(), dp.get_hot_encoded_test_y())
print('properly classified')
plt.imshow(properly_classified[0].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(properly_classified[1].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(properly_classified[2].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(properly_classified[3].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(properly_classified[4].reshape(28, 28), cmap=cm.binary)
plt.show()
print('missclasified')
plt.imshow(misclassified[0].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(misclassified[1].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(misclassified[2].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(misclassified[3].reshape(28, 28), cmap=cm.binary)
plt.show()
plt.imshow(misclassified[4].reshape(28, 28), cmap=cm.binary)
plt.show()
