def build_vae(d_z=2, d_hidden=256, d_x=784, N=100, total_N=60000):
# MODEL
z = Gaussian(mean=0, std=1.0, shape=(N, d_z), name="z", local=True)
X = neural_bernoulli(z, d_hidden=d_hidden, d_out=d_x, name="X", local=True)
# OBSERVED DATA
x_placeholder = X.observe_placeholder()
# VARIATIONAL MODEL
q_z = neural_gaussian(X=x_placeholder,
d_hidden=d_hidden,
d_out=d_z,
name="q_z")
z.attach_q(q_z)
jm = Model(X, minibatch_ratio=total_N / float(N))
return jm, x_placeholder
def autoencoder():
d_z = 2
d_hidden=256
d_x = 28*28
N=100
from util import get_mnist
Xdata, ydata = get_mnist()
Xbatch = tf.constant(np.float32(Xdata[0:N]))
z = Gaussian(mean=0, std=1.0, shape=(N,d_z), name="z")
X = neural_bernoulli(z, d_hidden=d_hidden, d_out=d_x, name="X")
X.observe(Xbatch)
q_z = neural_gaussian(X=Xbatch, d_hidden=d_hidden, d_out=d_z, name="q_z")
z.attach_q(q_z)
jm = Model(X)
return jm