def gen_displayable_images():
suffix = '_image.jpg'
for n in range(10):
prefix = './images_RBM/digitwise/'+str(n)+'_'
names = ['original', 'hidden', 'reconstructed']
names = [prefix+name+suffix for name in names]
image_beautifier(names, './images_RBM/'+str(n)+'.jpg')
if __name__ == '__main__':
mnist = MNIST()
train_x, train_y, test_x, test_y = mnist.load_dataset()
vn = train_x.shape[1]
hn = 2500
rbm = RBM(vn, hn)
rbm.load_rbm('mnist_trained_rbm.pt')
for n in range(10):
x = test_x[np.where(test_y==n)[0][0]]
x = x.unsqueeze(0)
hidden_image = []
gen_image = []
for k in range(rbm.k):
_, hk = rbm.sample_h(x)
_, vk = rbm.sample_v(hk)
gen_image.append(vk.numpy())
hidden_image.append(hk.numpy())
hidden_image = np.array(hidden_image)
hidden_image = np.mean(hidden_image, axis=0)
gen_image = np.array(gen_image)
gen_image = np.mean(gen_image, axis=0)
