def make_decoder(z, x_shape=(1, 20, 1)):
'''
Decoder: p(x|z)
'''
net = make_nn(z, 20)
logits = tf.reshape(net, tf.concat([[-1], x_shape], axis=0))
return tfd.Independent(tfd.Bernoulli(logits))
def get_inference_dist(n_dims):
with tf.name_scope('InferenceDistribution'):
loc = tf.get_variable('loc', [n_dims], 'float32')
scale = tf.get_variable('scale', [n_dims], 'float32')
inference_dist = tfd.Independent(
tfd.NormalWithSoftplusScale(loc, scale))
return inference_dist
def _make_decoder(code, data_shape):
with tf.variable_scope('decoder'):
x = code
x = tf.layers.dense(x, 200, tf.nn.relu)
x = tf.layers.dense(x, 200, tf.nn.relu)
logit = tf.layers.dense(x, _prod(data_shape))
logit = tf.reshape(logit, [-1] + data_shape)
return tfd.Independent(tfd.Bernoulli(logit), 2)
def get_log_p_2(n_dims):
dist = tfd.Independent(
tfd.Gamma(1.2 * tf.ones([n_dims]), 1.0 * tf.ones([n_dims])))
def log_p(x):
return dist.log_prob(tf.exp(x))
return log_p
def _build(self,
inputs,
nb_samples=10,
seed=0,
encoder_param_type='natural'):
### vae encode
emb = self._encoder(inputs)
enc_eta1 = self._mu_net(emb)
enc_eta2_diag = self._sigma_net(emb)
if encoder_param_type == 'natural':
enc_eta2_diag *= -1. / 2
# enc_eta2_diag -= 1e-8
enc_eta2 = tf.matrix_diag(enc_eta2_diag)
### GMM natural parameters
gmm_pi, gmm_eta1, gmm_eta2 = self.phi_gmm()
### combined GMM and VAE latent parameters
# eta1_tilde.shape = (N, K, D); eta2_tsilde.shape = (N, K, D, D)
# with tf.control_dependencies([util.matrix_is_pos_def_op(-2 * enc_eta2)]):
eta1_tilde = tf.expand_dims(
enc_eta1, axis=1) + tf.expand_dims(
gmm_eta1, axis=0)
eta2_tilde = tf.expand_dims(
enc_eta2, axis=1) + tf.expand_dims(
gmm_eta2, axis=0)
log_z_given_y_phi = compute_log_z_given_y(enc_eta1, enc_eta2, gmm_eta1,
gmm_eta2, gmm_pi)
# with tf.control_dependencies([util.matrix_is_pos_def_op(-2 * gmm_eta2)]):
mu, cov = gaussian.natural_to_standard(eta1_tilde, eta2_tilde)
posterior_mixture_distribution = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(tf.exp(log_z_given_y_phi)),
components_distribution=tfd.MultivariateNormalFullCovariance(
loc=mu, covariance_matrix=cov))
# sample x for each of the K components
# latent_k_samples.shape == nb_samples, batch_size, nb_components, latent_dim
latent_k_samples = posterior_mixture_distribution.components_distribution.sample(
[nb_samples])
### vae decode
output_mean = snt.BatchApply(self._decoder, n_dims=3)(latent_k_samples)
output_variance = tf.get_variable(
'output_variance',
dtype=tf.float32,
initializer=tf.zeros(output_mean.get_shape().as_list()),
trainable=True) # learned parameter for output distribution
output_distribution = tfd.Independent(
tfd.MultivariateNormalDiagWithSoftplusScale(
loc=output_mean, scale_diag=output_variance),
reinterpreted_batch_ndims=2)
# subsample for each datum in minibatch (go from `nb_samples` per component to `nb_samples` total)
latent_samples = subsample_x(
tf.transpose(latent_k_samples, [1, 0, 2, 3]), log_z_given_y_phi,
seed)
return output_distribution, posterior_mixture_distribution, latent_k_samples, latent_samples, log_z_given_y_phi
def make_decoder(code, data_shape):
x = code
x = tf.layers.dense(x, hidden, tf.nn.relu)
x = tf.layers.dense(x, hidden, tf.nn.relu)
logit = tf.layers.dense(x, np.prod(data_shape))
logit = tf.reshape(logit, [-1] + data_shape)
return tfd.Independent(tfd.Bernoulli(logit), 2)
def make_decoder(z, x_shape=(x_dim,)):
'''
Decoder: p(x|z)
'''
with tf.variable_scope("decoder"):
net = make_nn(z, x_dim)
print('decoder net', net)
logits = tf.reshape(net, tf.concat([[nb_z_samples, -1], x_shape], axis=0)) # For the batch
print('logits', logits)
return tfd.Independent(tfd.Bernoulli(logits), reinterpreted_batch_ndims=1)
def _build(self, inputs, hvar_labels, n_samples=10, analytic_kl=True):
datum_shape = inputs.get_shape().as_list()[1:]
enc_repr = self._encoder(inputs)
self.hvar_prior = tfd.ExpRelaxedOneHotCategorical(
temperature=self._temperature, logits=hvar_labels)
self.hvar_posterior = tfd.ExpRelaxedOneHotCategorical(
temperature=self._temperature, logits=self._hvar(enc_repr))
hvar_sample_shape = [n_samples
] + self.hvar_posterior.batch_shape.as_list(
) + self.hvar_posterior.event_shape.as_list()
hvar_sample = tf.reshape(self.hvar_posterior.sample(n_samples),
hvar_sample_shape)
self.latent_posterior = self._latent_posterior_fn(
self._loc(enc_repr), self._scale(enc_repr))
latent_posterior_sample = self.latent_posterior.sample(n_samples)
joint_sample = tf.concat([hvar_sample, latent_posterior_sample],
axis=-1)
sample_decoder = snt.BatchApply(self._decoder)
self.output_distribution = tfd.Independent(
tfd.Bernoulli(logits=sample_decoder(joint_sample)),
reinterpreted_batch_ndims=len(datum_shape))
distortion = -self.output_distribution.log_prob(inputs)
if analytic_kl and n_samples == 1:
rate = tfd.kl_divergence(self.latent_posterior, self.latent_prior)
else:
rate = (self.latent_posterior.log_prob(latent_posterior_sample) -
self.latent_prior.log_prob(latent_posterior_sample))
hrate = self.hvar_posterior.log_prob(
hvar_sample) - self.hvar_prior.log_prob(hvar_sample)
# hrate = tf.Print(hrate, [temperature])
# hrate = tf.Print(hrate, [hvar_sample], summarize=10)
# hrate = tf.Print(hrate, [self.hvar_posterior.log_prob(hvar_sample)])
# hrate = tf.Print(hrate, [self.hvar_prior.log_prob(hvar_sample)])
# hrate = tf.Print(hrate, [hrate], summarize=10)
elbo_local = -(rate + hrate + distortion)
self.elbo = tf.reduce_mean(elbo_local)
self.importance_weighted_elbo = tf.reduce_mean(
tf.reduce_logsumexp(elbo_local, axis=0) -
tf.log(tf.to_float(n_samples)))
self.hvar_sample = tf.exp(tf.split(hvar_sample, n_samples)[0])
self.hvar_cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=hvar_labels, logits=tf.split(hvar_sample, n_samples)[0])
self.hvar_labels = hvar_labels
self.distortion = distortion
self.rate = rate
self.hrate = hrate
def _reconstruction_loss(self):
# E[log p(x|z)]
if self.loss_function == 'cross_entropy':
d_p_x_z = tf.distributions.Bernoulli(self.decoder)
d_p_x_z = tfcd.Independent(d_p_x_z, 3)
log_likelihood = d_p_x_z.log_prob(self.x)
log_likelihood = tf.reduce_mean(log_likelihood, name='log_likelihood')
# log_likelihood = tf.div(log_likelihood, tf.reduce_prod(self.x.shape))
elif self.loss_function == 'mse':
log_likelihood = tf.reduce_sum(tf.squared_difference(self.x, self.decoder), axis=(1, 2, 3))
else:
raise NotImplementedError()
return log_likelihood
def make_decoder(self, z):
x = tf.layers.dense(z, 200, tf.nn.relu)
x = tf.layers.dense(x, 200, tf.nn.relu)
logits = tf.layers.dense(x, self.m_variants)
return tfd.Independent(tfd.Binomial(logits=logits, total_count=2.),
reinterpreted_batch_ndims=1)
def get_log_p_1(n_dims):
target_dist = tfd.Independent(
tfd.NormalWithSoftplusScale(loc=tf.zeros(n_dims),
scale=10 * tf.ones(n_dims)))
return target_dist.log_prob
