def manipulate_latent(model, args):
print('-' * 30 + 'Begin: manipulate' + '-' * 30)
x_test, y_test = fer2013.get_test_data_for_capsnet()
index = np.argmax(y_test, 1) == args.digit
number = np.random.randint(low=0, high=sum(index) - 1)
x, y = x_test[index][number], y_test[index][number]
x, y = np.expand_dims(x, 0), np.expand_dims(y, 0)
noise = np.zeros([1, 10, 16])
x_recons = []
for dim in range(16):
for r in [
-0.25, -0.2, -0.15, -0.1, -0.05, 0, 0.05, 0.1, 0.15, 0.2, 0.25
]:
tmp = np.copy(noise)
tmp[:, :, dim] = r
x_recon = model.predict([x, y, tmp])
x_recons.append(x_recon)
x_recons = np.concatenate(x_recons)
img = combine_images(x_recons, height=16)
image = img * 255
Image.fromarray(image.astype(
np.uint8)).save(args.save_dir + '/manipulate-%d.png' % args.digit)
print('manipulated result saved to %s/manipulate-%d.png' %
(args.save_dir, args.digit))
print('-' * 30 + 'End: manipulate' + '-' * 30)
def test(model, args):
x_test, y_test = fer2013.get_test_data_for_capsnet()
y_pred, x_recon = model.predict(x_test, batch_size=100)
print('-' * 30 + 'Begin: test' + '-' * 30)
print(
'Test acc:',
np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1)) / y_test.shape[0])
img = combine_images(np.concatenate([x_test[:50], x_recon[:50]]))
image = img * 255
Image.fromarray(image.astype(np.uint8)).save(args.save_dir +
"/real_and_recon.png")
print()
print('Reconstructed images are saved to %s/real_and_recon.png' %
args.save_dir)
print('-' * 30 + 'End: test' + '-' * 30)
plt.imshow(plt.imread(args.save_dir + "/real_and_recon.png"))
plt.show()