def px_graph(z, y):
reuse = len(tf.get_collection(tf.GraphKeys.VARIABLES, scope='px')) > 0
# -- transform z to be a sample from one of the Gaussian mixture components
with tf.variable_scope('z_transform'):
zm = Dense(y, 64, 'zm', reuse=reuse)
zv = Dense(y, 64, 'zv', tf.nn.softplus, reuse=reuse)
# -- p(x)
with tf.variable_scope('px'):
with tf.name_scope('layer1'):
zy = zm + tf.sqrt(zv) * z
h1 = custom_layer(zy, reuse)
h2 = Dense(h1, 512, 'layer2', tf.nn.relu, reuse=reuse)
h3 = Dense(h2, 512, 'layer3', tf.nn.relu, reuse=reuse)
h4 = Dense(h3, 512, 'layer4', tf.nn.relu, reuse=reuse)
px_logit = Dense(h2, 784, 'logit', reuse=reuse)
return px_logit
def px_graph(z, y):
reuse = len(tf.get_collection(tf.GraphKeys.VARIABLES, scope='px')) > 0
# -- p(z)
with tf.variable_scope('pz'):
h1 = Dense(y, 128, 'h1', tf.nn.relu, reuse=reuse)
h2 = Dense(h1, 128, 'h2', tf.nn.relu, reuse=reuse)
zm = Dense(h2, 64, 'zm', reuse=reuse)
zv = Dense(h2, 64, 'zv', tf.nn.softplus, reuse=reuse)
# -- p(x)
with tf.variable_scope('px'):
# h1 = Dense(z, 512, 'layer1', tf.nn.relu, reuse=reuse)
# h2 = Dense(h1, 512, 'layer2', tf.nn.relu, reuse=reuse)
# h3 = Dense(h2, 512, 'layer3', tf.nn.relu, reuse=reuse)
# px_logit = Dense(h3, 784, 'logit', reuse=reuse)
h1 = Dense(z, 512, 'layer1', tf.nn.relu, reuse=reuse)
h2 = Dense(h1, 28 * 14 * 14, 'layer2', tf.nn.relu, reuse=reuse)
h2 = tf.reshape(h2, [-1, 14, 14, 28])
conv1 = tf.layers.conv2d_transpose(h2,
28, [3, 3], (1, 1),
padding="same",
activation=tf.nn.relu,
reuse=reuse)
conv2 = tf.layers.conv2d_transpose(conv1,
28, [3, 3], (1, 1),
padding="same",
activation=tf.nn.relu,
reuse=reuse)
conv3 = tf.layers.conv2d_transpose(conv2,
28, [3, 3], (2, 2),
padding="same",
activation=tf.nn.relu,
reuse=reuse)
conv4 = Conv2d(conv3,
1, [3, 3], [1, 1],
activation=tf.nn.relu,
reuse=reuse,
scope='convlayer3')
px_logit = tf.contrib.layers.flatten(conv4)
return zm, zv, px_logit