def _build(self, inputs):
# create the layers for mean and covariance
output_shape = [-1] + self._output_shape
logits = tf.reshape(snt.Linear(np.prod(self._output_shape), initializers=self._initializers, regularizers=self._regularizers)(inputs),output_shape)
dtype = inputs.dtype
if self._dtype is not None:
dtype = self._dtype
if self._clip_value > 0:
probs = tf.nn.sigmoid(logits)
probs = tf.clip_by_value(probs, self._clip_value, 1 - self._clip_value)
bernoulli = tfd.Bernoulli(probs=probs, dtype=dtype)
else:
bernoulli = tfd.Bernoulli(logits=logits, dtype=dtype)
def reconstruction_node(self):
return self.mean()
bernoulli.reconstruction_node = types.MethodType(reconstruction_node, bernoulli)
def distribution_parameters(self):
return [self.mean()]
bernoulli.distribution_parameters = types.MethodType(distribution_parameters, bernoulli)
def get_probs(self):
return self.probs
bernoulli.get_probs = types.MethodType(get_probs, bernoulli)
return bernoulli
def define_graph(config):
network_tpl = tf.make_template('network', network, config=config)
inputs = tf.placeholder(tf.float32, [None, config.num_inputs])
targets = tf.placeholder(tf.float32, [None, 1])
num_visible = tf.placeholder(tf.int32, [])
batch_size = tf.to_float(tf.shape(inputs)[0])
data_mean, data_noise, data_uncertainty = network_tpl(inputs)
ood_inputs = inputs + tf.random_normal(
tf.shape(inputs), 0.0, config.noise_std)
ood_mean, ood_noise, ood_uncertainty = network_tpl(ood_inputs)
losses = [
-tfd.Normal(data_mean, data_noise).log_prob(targets),
-tfd.Bernoulli(data_uncertainty).log_prob(0),
-tfd.Bernoulli(ood_uncertainty).log_prob(1),
]
if config.center_at_target:
losses.append(-tfd.Normal(ood_mean, ood_noise).log_prob(targets))
loss = sum(tf.reduce_sum(loss) for loss in losses) / batch_size
optimizer = tf.train.AdamOptimizer(config.learning_rate)
gradients, variables = zip(*optimizer.compute_gradients(
loss, colocate_gradients_with_ops=True))
if config.clip_gradient:
gradients, _ = tf.clip_by_global_norm(gradients, config.clip_gradient)
optimize = optimizer.apply_gradients(zip(gradients, variables))
data_uncertainty = tf.sigmoid(data_uncertainty)
if not config.center_at_target:
data_mean = (1 - data_uncertainty) * data_mean + data_uncertainty * 0
data_noise = (1 - data_uncertainty) * data_noise + data_uncertainty * 0.1
return tools.AttrDict(locals())
def call(self, inputs, training=None):
logits = self.logits(inputs)
q = distributions.Bernoulli(
logits=logits,
dtype=tf.float32,
)
if self.beta > 0.0:
kld = q.kl_divergence(
distributions.Bernoulli(
logits=tf.zeros_like(logits),
dtype=tf.float32,
))
self.add_loss(tf.reduce_mean(kld))
return q
def get_distributions_from_tensor(t, dimension, num_mixes):
y_pred = tf.reshape(t, [-1, (2 * num_mixes * dimension + 1) + num_mixes],
name='reshape_ypreds')
out_e, out_pi, out_mus, out_stds = tf.split(y_pred,
num_or_size_splits=[
1, num_mixes,
num_mixes * dimension,
num_mixes * dimension
],
name='mdn_coef_split',
axis=-1)
cat = tfd.Categorical(logits=out_pi)
components_splits = [dimension] * num_mixes
mus = tf.split(out_mus, num_or_size_splits=components_splits, axis=1)
stds = tf.split(out_stds, num_or_size_splits=components_splits, axis=1)
components = [
tfd.MultivariateNormalDiag(loc=mu_i, scale_diag=std_i)
for mu_i, std_i in zip(mus, stds)
]
mix = tfd.Mixture(cat=cat, components=components)
stroke = tfd.Bernoulli(logits=out_e)
return mix, stroke
def generate_2pl_data(n_sample, n_factor, n_item,
alpha, beta, rho,
dtype = tf.float64):
if (n_item % n_factor) != 0:
n_item = n_factor * (n_item // n_factor)
item_per_factor = (n_item // n_factor)
intercept = tf.fill((n_item,), value = tf.constant(alpha, dtype = dtype))
loading = np.zeros((n_item, n_factor))
for i in range(n_factor):
for j in range(i * item_per_factor,
(i + 1) * item_per_factor):
loading[j, i] = ld
loading = tf.constant(loading, dtype = dtype)
if rho is None:
cor = tf.eye(n_factor, dtype = dtype)
else:
unit = tf.ones((n_factor, 1), dtype = dtype)
identity = tf.eye(n_factor, dtype = dtype)
cor = rho * (unit @ tf.transpose(unit)) + (1 - rho) * identity
dist_eta = tfd.MultivariateNormalTriL(
loc = tf.zeros(n_factor, dtype = dtype), scale_tril = tf.linalg.cholesky(cor))
eta = dist_eta.sample(n_sample)
logits = intercept + eta @ tf.transpose(loading)
x = tfd.Bernoulli(logits=logits, dtype=dtype).sample()
return x
def output_function(self, state):
params = dense_layer(state.h3,
self.output_units,
scope='gmm',
reuse=tf.compat.v1.AUTO_REUSE)
pis, mus, sigmas, rhos, es = self._parse_parameters(params)
mu1, mu2 = tf.split(mus, 2, axis=1)
mus = tf.stack([mu1, mu2], axis=2)
sigma1, sigma2 = tf.split(sigmas, 2, axis=1)
covar_matrix = [
tf.square(sigma1), rhos * sigma1 * sigma2, rhos * sigma1 * sigma2,
tf.square(sigma2)
]
covar_matrix = tf.stack(covar_matrix, axis=2)
covar_matrix = tf.reshape(
covar_matrix,
(self.batch_size, self.num_output_mixture_components, 2, 2))
mvn = tfd.MultivariateNormalFullCovariance(
loc=mus, covariance_matrix=covar_matrix)
b = tfd.Bernoulli(probs=es)
c = tfd.Categorical(probs=pis)
sampled_e = b.sample()
sampled_coords = mvn.sample()
sampled_idx = c.sample()
idx = tf.stack([tf.range(self.batch_size), sampled_idx], axis=1)
coords = tf.gather_nd(sampled_coords, idx)
return tf.concat([coords, tf.cast(sampled_e, tf.float32)], axis=1)
def __call__(self, inputs):
out = self.get('out', tfkl.Dense, np.prod(self._shape))(inputs)
out = tf.reshape(out, tf.concat([tf.shape(inputs)[:-1], self._shape], 0))
out = tf.cast(out, tf.float32)
if self._dist in ('normal', 'tanh_normal', 'trunc_normal'):
std = self.get('std', tfkl.Dense, np.prod(self._shape))(inputs)
std = tf.reshape(std, tf.concat([tf.shape(inputs)[:-1], self._shape], 0))
std = tf.cast(std, tf.float32)
if self._dist == 'mse':
dist = tfd.Normal(out, 1.0)
return tfd.Independent(dist, len(self._shape))
if self._dist == 'normal':
dist = tfd.Normal(out, std)
return tfd.Independent(dist, len(self._shape))
if self._dist == 'binary':
dist = tfd.Bernoulli(out)
return tfd.Independent(dist, len(self._shape))
if self._dist == 'tanh_normal':
mean = 5 * tf.tanh(out / 5)
std = tf.nn.softplus(std + self._init_std) + self._min_std
dist = tfd.Normal(mean, std)
dist = tfd.TransformedDistribution(dist, common.TanhBijector())
dist = tfd.Independent(dist, len(self._shape))
return common.SampleDist(dist)
if self._dist == 'trunc_normal':
std = 2 * tf.nn.sigmoid((std + self._init_std) / 2) + self._min_std
dist = common.TruncNormalDist(tf.tanh(out), std, -1, 1)
return tfd.Independent(dist, 1)
if self._dist == 'onehot':
return common.OneHotDist(out)
raise NotImplementedError(self._dist)
def _build(self, inputs):
prior = self._prior()
enc_out = self._encoder(inputs['s'])
loc = self._loc(enc_out)
log_scale = self._log_scale(enc_out)
scale = tf.nn.softplus(
log_scale + softplus_inverse(1.0)
) # idk what this is for. maybe ensuring center around 1.0
q = tfd.MultivariateNormalDiag(loc=loc, scale_diag=scale,
name='code') # approximate posterior
q_sample = q.sample(
self.FLAGS['num_vae_samples']) # approximate posterior sample
logits = self._decoder_conv(loc)
#logits = self._decoder_conv(q_sample)
phat = tfd.Independent(tfd.Bernoulli(logits=logits),
reinterpreted_batch_ndims=len(IMAGE_SHAPE),
name="image")
return dict(prior=prior,
q=q,
q_mean=loc,
q_sample=q_sample,
phat=phat,
phi_s=loc,
logits=logits)
def sample(self, time, outputs, state, name=None):
"""Gets a sample for one step."""
with ops.name_scope(name, "ScheduledOutputTrainingHelperSample",
[time, outputs, state]):
sampler = tfpd.Bernoulli(probs=self._sampling_probability)
return sampler.sample(sample_shape=self.batch_size,
seed=self._seed)
def testBernoulliLogProb(self, logits, n):
rv = ed.Bernoulli(logits)
dist = tfd.Bernoulli(logits)
x = rv.distribution.sample(n)
rv_log_prob, dist_log_prob = self.evaluate(
[rv.distribution.log_prob(x), dist.log_prob(x)])
self.assertAllEqual(rv_log_prob, dist_log_prob)
def decoder(state_sample, observation_dist="gaussian"):
"""Compute the data distribution of an observation from its state [1]."""
check_in(
"observation_dist",
observation_dist,
("gaussian", "laplace", "bernoulli", "multinomial"),
)
timesteps = tf.shape(state_sample)[1]
if self.pixel_observations:
# original decoder from [1] for deepmind lab envs
hidden = tf.layers.dense(state_sample, 1024, None)
kwargs = dict(strides=2, activation=tf.nn.relu)
hidden = tf.reshape(hidden, [-1, 1, 1, hidden.shape[-1]])
# 1 x 1
hidden = tf.layers.conv2d_transpose(hidden, 128, 5, **kwargs)
# 5 x 5 x 128
hidden = tf.layers.conv2d_transpose(hidden, 64, 5, **kwargs)
# 13 x 13 x 64
hidden = tf.layers.conv2d_transpose(hidden, 32, 6, **kwargs)
# 30 x 30 x 32
mean = 255 * tf.layers.conv2d_transpose(
hidden, 3, 6, strides=2, activation=tf.nn.sigmoid)
# 64 x 64 x 3
assert mean.shape[1:].as_list() == [64, 64, 3], mean.shape
else:
# decoder for gridworlds / structured observations
hidden = state_sample
d = self._hidden_layer_size
for _ in range(4):
hidden = tf.layers.dense(hidden, d, tf.nn.relu)
mean = tf.layers.dense(hidden, np.prod(self.data_shape), None)
mean = tf.reshape(mean, [-1, timesteps] + list(self.data_shape))
check_in(
"observation_dist",
observation_dist,
("gaussian", "laplace", "bernoulli", "multinomial"),
)
if observation_dist == "gaussian":
dist = tfd.Normal(mean, self._obs_stddev)
elif observation_dist == "laplace":
dist = tfd.Laplace(mean, self._obs_stddev / np.sqrt(2))
elif observation_dist == "bernoulli":
dist = tfd.Bernoulli(probs=mean)
else:
mean = tf.reshape(mean, [-1, timesteps] +
[np.prod(list(self.data_shape))])
dist = tfd.Multinomial(total_count=1, probs=mean)
reshape = tfp.bijectors.Reshape(
event_shape_out=list(self.data_shape))
dist = reshape(dist)
return dist
dist = tfd.Independent(dist, len(self.data_shape))
return dist
def __call__(self):
"""Get the distribution object from the backend"""
if get_backend() == 'pytorch':
import torch.distributions as tod
return tod.bernoulli.Bernoulli(logits=self.logits,
probs=self.probs)
else:
from tensorflow_probability import distributions as tfd
return tfd.Bernoulli(logits=self.logits, probs=self.probs)
def _common(cls, node, **kwargs):
x = kwargs["tensor_dict"][node.inputs[0]]
dtype = node.attrs.get("dtype", x.dtype)
dist = tfd.Bernoulli(probs=x, dtype=dtype)
if 'seed' in node.attrs:
ret = dist.sample(seed=int(node.attrs.get('seed')))
else:
ret = dist.sample()
return [tf.cast(tf.reshape(ret, x.shape), dtype)]
def __call__(self, features):
x = features
for index in range(self._layers):
x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
x = self.get(f'hout', tfkl.Dense, np.prod(self._shape))(x)
x = tf.reshape(x, tf.concat([tf.shape(features)[:-1], self._shape], 0))
if self._dist == 'normal':
return tfd.Independent(tfd.Normal(x, 1), len(self._shape))
elif self._dist == 'binary':
return tfd.Independent(tfd.Bernoulli(x), len(self._shape))
raise NotImplementedError(self._dist)
def __call__(self, observation, action):
x = self._dynamics(tf.concat([observation, action], axis=1))
# TODO (yarden): maybe it's better to feed the reward and terminals s, a instead of x.
# The world model predicts the difference between next_observation and observation.
return dict(next_observation=tfd.MultivariateNormalDiag(
loc=self._next_observation_residual_mu(x) +
tf.stop_gradient(observation),
scale_diag=self._next_observation_stddev(x)),
reward=tfd.Normal(loc=self._reward_mu(x), scale=1.0),
terminal=tfd.Bernoulli(logits=self._terminal_logit(x),
dtype=tf.float32))
def generate_logistic_reg_data(
n_sample, weight, intercept = 0,
dtype = tf.float64, seed = None):
weight = tf.constant(weight, dtype = dtype)
weight = tf.reshape(weight, shape = (-1, 1))
n_feature = weight.shape[0]
x = tf.random.normal(shape = (n_sample, n_feature),
seed = seed, dtype = dtype)
logits = intercept + x @ weight
y = tfd.Bernoulli(logits=logits, dtype=dtype).sample()
return x, y
def __call__(self, features):
kwargs = dict(strides=2, activation=self._act)
x = self.get('h1', tfkl.Dense, 8 * self._depth, None)(features)
x = tf.reshape(x, [-1, 1, 1, 8 * self._depth])
x = self.get('h2', tfkl.Conv2DTranspose, 4 * self._depth, 5, **kwargs)(x)
x = self.get('h3', tfkl.Conv2DTranspose, 2 * self._depth, 5, **kwargs)(x)
x = self.get('h4', tfkl.Conv2DTranspose, 1 * self._depth, 6, **kwargs)(x)
x = self.get('h5', tfkl.Conv2DTranspose, 1, 6, **kwargs)(x)
shape = tf.concat([tf.shape(features)[:-1], self._shape], axis=0)
x = tf.reshape(x, shape)
return tfd.Independent(tfd.Bernoulli(x), 3) # last 3 dimensions (row, col, chan) define 1 pixel
def sample(self, time, outputs, state, name=None):
"""Gets a sample for one step."""
with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
[time, outputs, state]):
# Return -1s where we did not sample, and sample_ids elsewhere
select_sampler = tfpd.Bernoulli(probs=self._sampling_probability,
dtype=dtypes.bool)
select_sample = select_sampler.sample(sample_shape=self.batch_size,
seed=self._scheduling_seed)
sample_id_sampler = tfpd.Categorical(logits=outputs)
return array_ops.where(select_sample,
sample_id_sampler.sample(seed=self._seed),
gen_array_ops.fill([self.batch_size], -1))
def call(self, x):
x = self._layers(x)
if not getattr(self, '_has_cnn', None):
rbd = 0 if x.shape[
-1] == 1 else 1 # #reinterpreted batch dimensions
x = tf.squeeze(x)
if self._dist == 'normal':
return tfd.Independent(tfd.Normal(x, 1), rbd)
if self._dist == 'binary':
return tfd.Independent(tfd.Bernoulli(x), rbd)
raise NotImplementedError(self._dist)
return x
def decoder(codes):
"""Builds a distribution over images given codes.
Args:
codes: A `Tensor` representing the inputs to be decoded, of shape `[...,
code_size]`.
Returns:
decoder_distribution: A multivariate `Bernoulli` distribution.
"""
logits = decoder_net(codes)
return tfd.Independent(tfd.Bernoulli(logits=logits),
reinterpreted_batch_ndims=len(output_shape),
name="decoder_distribution")
def __call__(self, x):
n_sample = len(x)
joint_prob = tfd.JointDistributionSequential([
tfd.Independent(
tfd.Normal(
loc = tf.zeros((n_sample, n_factor), dtype=self.dtype),
scale = 1.0),
reinterpreted_batch_ndims=1),
lambda eta: tfd.Independent(
tfd.Bernoulli(
logits= self.intercept + eta @ tf.transpose(self.loading),
dtype=self.dtype),
reinterpreted_batch_ndims=1)])
joint_prob._to_track=self
return joint_prob
def __call__(self, features):
x = features
for index in range(self.layers_num): # 3
x = self.get(f"h{index}", tf.keras.layers.Dense, self._units,
self._act)(x)
# print("x:",x.shape) # (15, 1250, 400)
x = self.get(f"hout", tf.keras.layers.Dense, np.prod(self._shape))(x)
# print("x:",x.shape)
x = tf.reshape(x, tf.concat([tf.shape(features)[:-1], self._shape], 0))
# print("x:",x.shape)
if self._dist == "normal":
return tfd.Independent(tfd.Normal(x, 1), len(self._shape))
if self._dist == "binary":
return tfd.Independent(tfd.Bernoulli(x), len(self._shape))
raise NotImplementedError(self._dist)
def calculate_log_px_z(x, x_logit, pi):
sample_size = x_logit.shape[4]
n_required = x_logit.shape[5]
# x_broad = tf.repeat(tf.expand_dims(x, 4), axis=4, repeats=pi.shape[1])
# x_broad = tf.reshape(x_broad, shape=(16, 4))
# x_logit = tf.reshape(x_logit, shape=(16, 4))
x_broad = tf.repeat(tf.expand_dims(x, -1), axis=-1, repeats=sample_size)
x_broad = tf.repeat(tf.expand_dims(x_broad, -1), axis=-1, repeats=n_required)
dist = tfpd.Bernoulli(logits=x_logit)
log_px_z = dist.log_prob(x_broad)
log_px_z = tf.reduce_sum(log_px_z, axis=(1, 2, 3))
log_px_z = tf.reduce_mean(log_px_z, axis=1)
log_px_z = tf.reduce_sum(pi[:, :, 0, 0] * log_px_z, axis=1)
log_px_z = tf.reduce_mean(log_px_z, axis=0)
return log_px_z
def decode(code, data_shape):
"""
:param code: number of code units
:param data_shape: dimensionality of the input data
:return: void
"""
with tf.variable_scope('decoder', reuse=tf.AUTO_REUSE):
logit = tf.reshape(
tf.layers.dense(
tf.layers.dense(
tf.layers.dense(code, 256, tf.nn.relu), 784, tf.nn.relu
), np.prod(data_shape)
), [-1] + data_shape
)
return distributions.Independent(distributions.Bernoulli(logit), 2)
def perform_fwd_pass(self):
mean, log_var = self.nets.encoder(self.x)
stddev = tf.exp(0.5 * log_var)
qz_x = tfpd.Normal(loc=mean, scale=stddev)
z = qz_x.sample()
logits = self.nets.decoder(z)
px_z = tfpd.Bernoulli(logits=logits)
p_z = tfpd.Normal(loc=tf.zeros_like(z), scale=tf.ones_like(z))
kl = tf.reduce_sum(tfpd.kl_divergence(qz_x, p_z), axis=1)
expected_log_likelihood = tf.reduce_sum(px_z.log_prob(self.x),
axis=(1, 2, 3))
self.elbo = tf.reduce_mean(expected_log_likelihood - kl, axis=0)
def rollout_in_dream(self, z_init, h_init, video=False):
"""
Inputs:
h_init: (L, B, 1)
z_init: (L, B, latent_dim * n_atoms)
done_init: (L, B, 1)
"""
L, B = h_init.shape[:2]
horizon = self.config.imagination_horizon
z, h = tf.reshape(z_init, [L * B, -1]), tf.reshape(h_init, [L * B, -1])
feats = tf.concat([z, h], axis=-1)
#: s_t, a_t, s_t+1
trajectory = {"state": [], "action": [], 'next_state': []}
for t in range(horizon):
actions = tf.cast(self.policy.sample(feats), dtype=tf.float32)
trajectory["state"].append(feats)
trajectory["action"].append(actions)
h = self.world_model.step_h(z, h, actions)
z, _ = self.world_model.rssm.sample_z_prior(h)
z = tf.reshape(z, [L * B, -1])
feats = tf.concat([z, h], axis=-1)
trajectory["next_state"].append(feats)
trajectory = {k: tf.stack(v, axis=0) for k, v in trajectory.items()}
#: reward_head(s_t+1) -> r_t
#: Distribution.mode()は確立最大値を返すのでNormalの場合は
#: trjactory["reward"] == rewards
rewards = self.world_model.reward_head(trajectory['next_state'])
trajectory["reward"] = rewards
disc_logits = self.world_model.discount_head(trajectory['next_state'])
trajectory["discount"] = tfd.Independent(
tfd.Bernoulli(logits=disc_logits),
reinterpreted_batch_ndims=1).mean()
return trajectory
def feed_forward(
features, data_shape, num_layers=2, activation=tf.nn.relu,
mean_activation=None, stop_gradient=False, trainable=True, units=100,
std=1.0, low=-1.0, high=1.0, dist='normal', min_std=1e-2, init_std=1.0):
hidden = features
if stop_gradient:
hidden = tf.stop_gradient(hidden)
for _ in range(num_layers):
hidden = tf.layers.dense(hidden, units, activation, trainable=trainable)
mean = tf.layers.dense(
hidden, int(np.prod(data_shape)), mean_activation, trainable=trainable)
mean = tf.reshape(mean, tools.shape(features)[:-1] + data_shape)
if std == 'learned':
std = tf.layers.dense(
hidden, int(np.prod(data_shape)), None, trainable=trainable)
init_std = np.log(np.exp(init_std) - 1)
std = tf.nn.softplus(std + init_std) + min_std
std = tf.reshape(std, tools.shape(features)[:-1] + data_shape)
if dist == 'normal':
dist = tfd.Normal(mean, std)
dist = tfd.Independent(dist, len(data_shape))
elif dist == 'deterministic':
dist = tfd.Deterministic(mean)
dist = tfd.Independent(dist, len(data_shape))
elif dist == 'binary':
dist = tfd.Bernoulli(mean)
dist = tfd.Independent(dist, len(data_shape))
elif dist == 'trunc_normal':
# https://www.desmos.com/calculator/rnksmhtgui
dist = tfd.TruncatedNormal(mean, std, low, high)
dist = tfd.Independent(dist, len(data_shape))
elif dist == 'tanh_normal':
# https://www.desmos.com/calculator/794s8kf0es
dist = distributions.TanhNormal(mean, std)
elif dist == 'tanh_normal_tanh':
# https://www.desmos.com/calculator/794s8kf0es
mean = 5.0 * tf.tanh(mean / 5.0)
dist = distributions.TanhNormal(mean, std)
elif dist == 'onehot_score':
dist = distributions.OneHot(mean, gradient='score')
elif dist == 'onehot_straight':
dist = distributions.OneHot(mean, gradient='straight')
else:
raise NotImplementedError(dist)
return dist
def __call__(self, codes):
"""Build decoder which takes a code and returns a distribution over images.
Args:
codes: A `float`-like `Tensor` representing the inputs to be decoded.
The first dimension (axis 0) indexes batch elements; all other
dimensions index event elements.
Returns:
decoder: A multivariate `Bernoulli` distribution.
"""
net = self.decoder_net(codes)
new_shape = tf.concat([tf.shape(net)[:-1], self.output_shape], axis=0)
logits = tf.reshape(net, shape=new_shape)
return tfd.Independent(tfd.Bernoulli(logits=logits),
reinterpreted_batch_ndims=len(
self.output_shape),
name="decoder_distribution")
def _compute_log_loss(self, y_true, y_pred, mode):
"""
Inputs:
y_true: (L, B, 1)
y_pred: (L, B, 1)
mode: "reward" or "discount"
"""
if mode == "discount":
dist = tfd.Independent(tfd.Bernoulli(logits=y_pred),
reinterpreted_batch_ndims=1)
elif mode == "reward":
dist = tfd.Independent(tfd.Normal(loc=y_pred, scale=1.),
reinterpreted_batch_ndims=1)
log_prob = dist.log_prob(y_true)
loss = tf.reduce_mean(log_prob)
return loss
def call(self, codes):
"""Builds a distribution over images given codes.
Args:
codes: A `Tensor` representing the inputs to be decoded, of shape `[...,
code_size]`.
Returns:
decoder_distribution: A multivariate `Bernoulli` distribution.
"""
num_samples, batch_size, latent_size, code_size = common_layers.shape_list(
codes)
codes = tf.reshape(codes,
[num_samples * batch_size, latent_size, code_size])
logits = self.decoder_net(codes)
logits = tf.reshape(logits,
[num_samples, batch_size] + list(self.image_shape))
return tfd.Independent(tfd.Bernoulli(logits=logits),
reinterpreted_batch_ndims=len(self.image_shape),
name="decoder_distribution")