def q_net(observed, x_dim, z_dim, n_z_per_x):
with zs.BayesianNet(observed=observed) as variational:
x = zs.Empirical('x', tf.int32, (None, x_dim))
y = zs.Empirical('y', tf.int32, (None, 10))
x = tf.concat([x, y], 1)
lz_x = tf.layers.dense(tf.to_float(x), 500, activation=tf.nn.relu)
lz_x = tf.layers.dense(lz_x, 500, activation=tf.nn.relu)
z_mean = tf.layers.dense(lz_x, z_dim)
z_logstd = tf.layers.dense(lz_x, z_dim)
z = zs.Normal('z',
z_mean,
logstd=z_logstd,
group_ndims=1,
n_samples=n_z_per_x)
return variational
def cvae(observed, x_dim, y_dim, z_dim, n, n_particles=1):
with zs.BayesianNet(observed=observed) as model:
y = zs.Empirical('y', tf.int32, (n, y_dim))
z_mean = tf.zeros([n, z_dim])
z = zs.Normal('z',
z_mean,
std=1.,
group_ndims=1,
n_samples=n_particles)
z = tf.to_float(z[0])
yz = tf.concat([tf.to_float(y), z], axis=1)
lx_yz = tf.layers.dense(tf.to_float(yz), 500, activation=tf.nn.relu)
lx_yz = tf.layers.dense(lx_yz, 500, activation=tf.nn.relu)
x_logits = tf.layers.dense(lx_yz, x_dim)
x_mean = zs.Implicit('x_mean', tf.sigmoid(x_logits), group_ndims=1)
x = zs.Bernoulli('x', logits=x_logits, group_ndims=1)
return model