def network(x_dim, scope, factor):
with tf.variable_scope(scope):
net = Sequential([
Zip([
Linear(x_dim, 10, scope='embed_1', factor=1.0 / 3),
Linear(x_dim, 10, scope='embed_2', factor=factor * 1.0 / 3),
Linear(2, 10, scope='embed_3', factor=1.0 / 3),
lambda _: 0.,
]),
sum,
tf.nn.relu,
Linear(10, 10, scope='linear_1'),
tf.nn.relu,
Parallel([
Sequential([
Linear(10, x_dim, scope='linear_s', factor=0.001),
ScaleTanh(x_dim, scope='scale_s')
]),
Linear(10, x_dim, scope='linear_t', factor=0.001),
Sequential([
Linear(10, x_dim, scope='linear_f', factor=0.001),
ScaleTanh(x_dim, scope='scale_f'),
])
])
])
return net
def net_factory(x_dim, scope, factor):
with tf.variable_scope(scope):
net = Sequential([
Zip([
Linear(hps.latent_dim, size1, scope='embed_1', factor=0.33),
Linear(hps.latent_dim, size1, scope='embed_2', factor=factor * 0.33),
Linear(2, size1, scope='embed_3', factor=0.33),
encoder_sampler,
]),
sum,
tf.nn.relu,
Linear(size1, size2, scope='linear_1'),
tf.nn.relu,
Parallel([
Sequential([
Linear(size2, hps.latent_dim, scope='linear_s', factor=0.01),
ScaleTanh(hps.latent_dim, scope='scale_s')
]),
Linear(size2, hps.latent_dim, scope='linear_t', factor=0.01),
Sequential([
Linear(size2, hps.latent_dim, scope='linear_f', factor=0.01),
ScaleTanh(hps.latent_dim, scope='scale_f'),
])
])
])