def test(epoch, mode=1):
import matplotlib.pyplot as plt
from PIL import Image
from networks.capsulenet.helper_function import combine_images
if mode == 1:
num_classes =10
_,(x_test,y_test) = load_cifar_10()
else:
num_classes = 100
_,(x_test,y_test) = load_cifar_100()
model = CapsNetv2(input_shape=[32, 32, 3],
n_class=num_classes,
n_route=3)
model.load_weights('weights/capsule_weights/capsule-cifar-'+str(num_classes)+'weights-{:02d}.h5'.format(epoch))
print("Weights loaded, start validation")
# model.load_weights('weights/capsule-weights-{:02d}.h5'.format(epoch))
y_pred, x_recon = model.predict([x_test, y_test], batch_size=100)
print('-'*50)
# Test acc: 0.7307
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("results/real_and_recon.png")
print('Reconstructed images are saved to ./results/real_and_recon.png')
print('-'*50)
plt.imshow(plt.imread("results/real_and_recon.png", ))
plt.show()
def test(model, data):
x_test, y_test = data
y_pred, x_recon = model.predict([x_test, y_test], batch_size=100)
print('-' * 50)
print(
'Test acc:',
np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1)) / y_test.shape[0])
import matplotlib.pyplot as plt
from networks.capsulenet.helper_function import combine_images
from PIL import Image
img = combine_images(np.concatenate([x_test[:50], x_recon[:50]]))
image = img * 255
Image.fromarray(image.astype(np.uint8)).save("real_and_recon.png")
print()
print('Reconstructed images are saved to ./real_and_recon.png')
print('-' * 50)
plt.imshow(plt.imread("real_and_recon.png", ))
plt.show()
