def gen_displayable_images():
suffix = '_image.jpg'
for n in range(10):
prefix = './images_DBN/digitwise/' + str(n) + '_'
names = ['original', 'hidden', 'reconstructed']
names = [prefix + name + suffix for name in names]
image_beautifier(names, './images_DBN/' + str(n) + '.jpg')
if __name__ == '__main__':
mnist = MNIST()
train_x, train_y, test_x, test_y = mnist.load_dataset()
layers = [512, 128, 64, 10]
dbn = DBN(train_x.shape[1], layers)
dbn.layer_parameters = torch.load('mnist_trained_dbn.pt')
for n in range(10):
x = test_x[np.where(test_y == n)[0][0]]
x = x.unsqueeze(0)
gen_image, hidden_image = dbn.reconstructor(x)
gen_image = gen_image.numpy()
hidden_image = hidden_image.numpy()
image = x.numpy()
image = mnist.inv_transform_normalizer(image)[0]
hidden_image = (hidden_image * 255)[0]
gen_image = mnist.inv_transform_normalizer(gen_image)[0]
image = np.reshape(image, (28, 28))
hidden_image = np.reshape(hidden_image, (5, 2))
