def generator(self, Z, reuse=False):
with tf.variable_scope(self.get_name() + "/generator", reuse=reuse):
print 'generator scope: ', tf.get_variable_scope().name
logits = forward(Z, self.decoder_model)
if self.p_x_given_z_func == distributions.Bernoulli:
print 'generator: using bernoulli'
return self.p_x_given_z_func(logits=logits)
elif (self.p_x_given_z_func == distributions.Normal or self.p_x_given_z_func == distributions.Logistic) \
and self.encoder_model.layer_type == 'cnn':
print 'generator: using exponential family [cnn]'
channels = shp(logits)[3]
assert channels % 2 == 0, "need to project to 2x the channels for gaussian p(x|z)"
loc = logits[:, :, :, channels /
2:] # tf.nn.sigmoid(logits[:, :, :, channels/2:])
scale = 1e-6 + tf.nn.softplus(logits[:, :, :, 0:channels / 2])
return self.p_x_given_z_func(loc=loc, scale=scale)
elif (self.p_x_given_z_func == distributions.Normal or self.p_x_given_z_func == distributions.Logistic) \
and self.encoder_model.layer_type == 'dnn':
print 'generator: using exponential family [dnn]'
features = shp(logits)[-1]
assert features % 2 == 0, "need to project to 2x the channels for gaussian p(x|z)"
loc = logits[:, features /
2:] # tf.nn.sigmoid(logits[:, :, :, channels/2:])
scale = 1e-6 + tf.nn.softplus(logits[:, 0:features / 2])
return self.p_x_given_z_func(loc=loc, scale=scale)
else:
raise Exception("unknown distribution provided for likelihood")
def generator(self, Z, reuse=False):
with tf.variable_scope(self.get_name() + "/generator", reuse=reuse):
print 'generator scope: ', tf.get_variable_scope().name
# Use generator to determine mean of
# Bernoulli distribution of reconstructed input
# print 'batch norm for decoder: ', use_ln
return forward(Z, self.decoder_model)
def encoder(self, X, rnd_sample=None, reuse=False, hard=False):
with tf.variable_scope(self.get_name() + "/encoder", reuse=reuse):
encoded = forward(X, self.encoder_model)
return VAE.reparameterize(encoded,
self.num_discrete,
self.tau,
hard=hard,
rnd_sample=rnd_sample)