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)
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, (50, 50))
gen_image = np.reshape(gen_image, (28, 28))
