def make_model(proposal_type, model_type, data_dim, mean, global_step):
"""Create model graph."""
kl_weight = make_kl_weight(global_step, FLAGS.anneal_kl_step)
# Bernoulli VAE proposal gets that data mean because it is proposing images.
# Other proposals don't because they are proposing latent states.
decoder_hidden_sizes = [
int(x.strip()) for x in FLAGS.decoder_hidden_sizes.split(",")
]
q_hidden_sizes = [int(x.strip()) for x in FLAGS.q_hidden_sizes.split(",")]
energy_hidden_sizes = [
int(x.strip()) for x in FLAGS.energy_hidden_sizes.split(",")
]
if model_type in ["nis", "his", "lars", "conv_nis", "identity"]:
proposal_data_dim = data_dim
elif model_type in [
"bernoulli_vae", "gaussian_vae", "hisvae", "conv_gaussian_vae",
"conv_bernoulli_vae"
]:
proposal_data_dim = [FLAGS.latent_dim]
if proposal_type == "bernoulli_vae":
proposal = vae.BernoulliVAE(
latent_dim=FLAGS.latent_dim,
data_dim=proposal_data_dim,
data_mean=mean,
decoder_hidden_sizes=decoder_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
kl_weight=kl_weight,
reparameterize_sample=FLAGS.reparameterize_proposal,
temperature=FLAGS.gst_temperature,
dtype=tf.float32)
if proposal_type == "conv_bernoulli_vae":
proposal = vae.ConvBernoulliVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=mean,
scale_min=FLAGS.scale_min,
kl_weight=kl_weight,
dtype=tf.float32)
elif proposal_type == "gaussian_vae":
proposal = vae.GaussianVAE(
latent_dim=FLAGS.latent_dim,
data_dim=proposal_data_dim,
decoder_hidden_sizes=decoder_hidden_sizes,
decoder_nn_scale=FLAGS.vae_decoder_nn_scale,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
kl_weight=kl_weight,
dtype=tf.float32)
elif proposal_type == "nis":
proposal = nis.NIS(
K=FLAGS.K,
data_dim=proposal_data_dim,
energy_hidden_sizes=energy_hidden_sizes,
dtype=tf.float32)
elif proposal_type == "rejection_sampling":
proposal = rejection_sampling.RejectionSampling(
T=FLAGS.K,
data_dim=proposal_data_dim,
energy_hidden_sizes=energy_hidden_sizes,
dtype=tf.float32)
elif proposal_type == "gaussian":
proposal = base.get_independent_normal(proposal_data_dim)
elif proposal_type == "his":
proposal = his.FullyConnectedHIS(
T=FLAGS.his_T,
data_dim=proposal_data_dim,
energy_hidden_sizes=energy_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
learn_temps=FLAGS.learn_his_temps,
learn_stepsize=FLAGS.learn_his_stepsize,
init_alpha=FLAGS.his_init_alpha,
init_step_size=FLAGS.his_init_stepsize,
dtype=tf.float32)
elif proposal_type == "lars":
proposal = lars.LARS(
K=FLAGS.K,
T=FLAGS.lars_T,
data_dim=proposal_data_dim,
accept_fn_layers=energy_hidden_sizes,
proposal=None,
data_mean=None,
ema_decay=0.99,
is_eval=FLAGS.mode == "eval",
dtype=tf.float32)
if model_type == "bernoulli_vae":
model = vae.BernoulliVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=mean,
decoder_hidden_sizes=decoder_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
proposal=proposal,
kl_weight=kl_weight,
reparameterize_sample=False,
dtype=tf.float32)
elif model_type == "gaussian_vae":
model = vae.GaussianVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
decoder_hidden_sizes=decoder_hidden_sizes,
decoder_nn_scale=FLAGS.vae_decoder_nn_scale,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
proposal=proposal,
kl_weight=kl_weight,
dtype=tf.float32)
elif model_type == "conv_gaussian_vae":
model = vae.ConvGaussianVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
scale_min=FLAGS.scale_min,
proposal=proposal,
kl_weight=kl_weight,
dtype=tf.float32)
elif model_type == "conv_bernoulli_vae":
model = vae.ConvBernoulliVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
scale_min=FLAGS.scale_min,
proposal=proposal,
kl_weight=kl_weight,
dtype=tf.float32)
elif model_type == "nis":
model = nis.NIS(
K=FLAGS.K,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
energy_hidden_sizes=energy_hidden_sizes,
proposal=proposal,
reparameterize_proposal_samples=FLAGS.reparameterize_proposal,
dtype=tf.float32)
elif model_type == "conv_nis":
model = nis.ConvNIS(
K=FLAGS.K,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
proposal=proposal,
reparameterize_proposal_samples=FLAGS.reparameterize_proposal,
dtype=tf.float32)
elif model_type == "his":
model = his.FullyConnectedHIS(
proposal=proposal,
T=FLAGS.his_T,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
energy_hidden_sizes=energy_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
learn_temps=FLAGS.learn_his_temps,
learn_stepsize=FLAGS.learn_his_stepsize,
init_alpha=FLAGS.his_init_alpha,
init_step_size=FLAGS.his_init_stepsize,
dtype=tf.float32)
elif model_type == "lars":
model = lars.LARS(
K=FLAGS.K,
T=FLAGS.lars_T,
data_dim=data_dim,
accept_fn_layers=energy_hidden_sizes,
proposal=proposal,
data_mean=None if FLAGS.squash else mean,
ema_decay=0.99,
is_eval=FLAGS.mode == "eval",
dtype=tf.float32)
elif model_type == "identity":
model = proposal
# elif model_type == "hisvae":
# model = his.HISVAE(
# latent_dim=FLAGS.latent_dim,
# proposal=proposal,
# T=FLAGS.his_T,
# data_dim=data_dim,
# data_mean=mean,
# energy_hidden_sizes=energy_hidden_sizes,
# q_hidden_sizes=q_hidden_sizes,
# decoder_hidden_sizes=decoder_hidden_sizes,
# learn_temps=FLAGS.learn_his_temps,
# learn_stepsize=FLAGS.learn_his_stepsize,
# init_alpha=FLAGS.his_init_alpha,
# init_step_size=FLAGS.his_init_stepsize,
# squash=FLAGS.squash,
# kl_weight=kl_weight,
# dtype=tf.float32)
if FLAGS.squash:
model = base.SquashedDistribution(distribution=model, data_mean=mean)
return model
def main(unused_argv):
g = tf.Graph()
with g.as_default():
target = dists.get_target_distribution(
FLAGS.target,
nine_gaussians_variance=FLAGS.nine_gaussians_variance)
energy_fn_layers = [
int(x.strip()) for x in FLAGS.energy_fn_sizes.split(",")
]
if FLAGS.algo == "lars":
print("Running LARS")
loss, train_op, global_step = make_lars_graph(
target_dist=target,
K=FLAGS.K,
batch_size=FLAGS.batch_size,
eval_batch_size=FLAGS.eval_batch_size,
lr=FLAGS.learning_rate,
mlp_layers=energy_fn_layers,
dtype=tf.float32)
else:
if FLAGS.algo == "nis":
print("Running NIS")
model = nis.NIS(K=FLAGS.K,
data_dim=2,
energy_hidden_sizes=energy_fn_layers)
density_image_summary(
lambda x: # pylint: disable=g-long-lambda
(tf.squeeze(model.energy_fn(x)) + model.proposal.log_prob(
x)),
FLAGS.density_num_bins,
"energy/nis")
elif FLAGS.algo == "rejection_sampling":
print("Running Rejection Sampling")
logit_accept_fn = tf.keras.Sequential([
tf.keras.layers.Dense(layer_size, activation="tanh")
for layer_size in energy_fn_layers
] + [tf.keras.layers.Dense(1, activation=None)])
model = rejection_sampling.RejectionSampling(
T=FLAGS.K, data_dim=[2], logit_accept_fn=logit_accept_fn)
density_image_summary(
lambda x: tf.squeeze( # pylint: disable=g-long-lambda
tf.log_sigmoid(model.logit_accept_fn(x)),
axis=-1) + model.proposal.log_prob(x),
FLAGS.density_num_bins,
"energy/trs")
elif FLAGS.algo == "his":
print("Running HIS")
model = his.FullyConnectedHIS(
T=FLAGS.his_t,
data_dim=2,
energy_hidden_sizes=energy_fn_layers,
q_hidden_sizes=energy_fn_layers,
init_step_size=FLAGS.his_stepsize,
learn_stepsize=FLAGS.his_learn_stepsize,
init_alpha=FLAGS.his_alpha,
learn_temps=FLAGS.his_learn_alpha)
density_image_summary(
lambda x: -model.hamiltonian_potential(x),
FLAGS.density_num_bins, "energy/his")
sample_image_summary(model,
"density/his",
num_samples=100000,
num_bins=50)
loss, train_op, global_step = make_train_graph(
target_dist=target,
model=model,
batch_size=FLAGS.batch_size,
eval_batch_size=FLAGS.eval_batch_size,
lr=FLAGS.learning_rate)
log_hooks = make_log_hooks(global_step, loss)
with tf.train.MonitoredTrainingSession(
master="",
is_chief=True,
hooks=log_hooks,
checkpoint_dir=os.path.join(FLAGS.logdir, exp_name()),
save_checkpoint_secs=120,
save_summaries_steps=FLAGS.summarize_every,
log_step_count_steps=FLAGS.summarize_every) as sess:
cur_step = -1
while True:
if sess.should_stop() or cur_step > FLAGS.max_steps:
break
# run a step
_, cur_step = sess.run([train_op, global_step])
def main(unused_argv):
g = tf.Graph()
with g.as_default():
energy_fn_layers = [
int(x.strip()) for x in FLAGS.energy_fn_sizes.split(",")
]
if FLAGS.algo == "density":
target = dists.get_target_distribution(FLAGS.target)
plot = make_density_summary(target.log_prob, num_bins=FLAGS.num_bins)
with tf.train.SingularMonitoredSession(
checkpoint_dir=FLAGS.logdir) as sess:
plot = sess.run(plot)
with tf.io.gfile.GFile(os.path.join(FLAGS.logdir, "density"),
"w") as out:
np.save(out, plot)
elif FLAGS.algo == "lars":
tf.logging.info("Running LARS")
proposal = base.get_independent_normal([2], FLAGS.proposal_variance)
model = lars.SimpleLARS(
K=FLAGS.K, data_dim=[2], accept_fn_layers=energy_fn_layers,
proposal=proposal)
plot = make_density_summary(
lambda x: tf.squeeze(model.accept_fn(x)) + model.proposal.log_prob(x),
num_bins=FLAGS.num_bins)
with tf.train.SingularMonitoredSession(
checkpoint_dir=FLAGS.logdir) as sess:
plot = sess.run(plot)
with tf.io.gfile.GFile(os.path.join(FLAGS.logdir, "density"),
"w") as out:
np.save(out, plot)
else:
proposal = base.get_independent_normal([2], FLAGS.proposal_variance)
if FLAGS.algo == "nis":
tf.logging.info("Running NIS")
model = nis.NIS(
K=FLAGS.K, data_dim=[2], energy_hidden_sizes=energy_fn_layers,
proposal=proposal)
elif FLAGS.algo == "his":
tf.logging.info("Running HIS")
model = his.FullyConnectedHIS(
T=FLAGS.his_t,
data_dim=[2],
energy_hidden_sizes=energy_fn_layers,
q_hidden_sizes=energy_fn_layers,
init_step_size=FLAGS.his_stepsize,
learn_stepsize=FLAGS.his_learn_stepsize,
init_alpha=FLAGS.his_alpha,
learn_temps=FLAGS.his_learn_alpha,
proposal=proposal)
elif FLAGS.algo == "rejection_sampling":
model = rejection_sampling.RejectionSampling(
T=FLAGS.K, data_dim=[2], energy_hidden_sizes=energy_fn_layers,
proposal=proposal)
samples = model.sample(FLAGS.batch_size)
with tf.train.SingularMonitoredSession(
checkpoint_dir=FLAGS.logdir) as sess:
make_sample_density_summary(
sess,
samples,
max_samples_per_batch=FLAGS.batch_size,
num_samples=FLAGS.num_samples,
num_bins=FLAGS.num_bins)
def make_model(proposal_type, model_type, data_dim, mean, global_step):
"""Create model graph."""
kl_weight = make_kl_weight(global_step, FLAGS.anneal_kl_step)
# Bernoulli VAE proposal gets that data mean because it is proposing images.
# Other proposals don't because they are proposing latent states.
decoder_hidden_sizes = [
int(x.strip()) for x in FLAGS.decoder_hidden_sizes.split(",")
]
q_hidden_sizes = [int(x.strip()) for x in FLAGS.q_hidden_sizes.split(",")]
energy_hidden_sizes = [
int(x.strip()) for x in FLAGS.energy_hidden_sizes.split(",")
]
if model_type in ["nis", "his"]:
proposal_data_dim = data_dim
elif model_type in [
"bernoulli_vae", "gaussian_vae", "hisvae", "conv_gaussian_vae",
"conv_bernoulli_vae"
]:
proposal_data_dim = FLAGS.latent_dim
if proposal_type == "bernoulli_vae":
proposal = vae.BernoulliVAE(
latent_dim=FLAGS.latent_dim,
data_dim=proposal_data_dim,
data_mean=mean,
decoder_hidden_sizes=decoder_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
kl_weight=kl_weight,
reparameterize_sample=FLAGS.reparameterize_proposal,
temperature=FLAGS.gst_temperature,
dtype=tf.float32)
elif proposal_type == "gaussian_vae":
proposal = vae.GaussianVAE(
latent_dim=FLAGS.latent_dim,
data_dim=proposal_data_dim,
# data_mean=mean,
decoder_hidden_sizes=decoder_hidden_sizes,
decoder_nn_scale=FLAGS.vae_decoder_nn_scale,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
kl_weight=kl_weight,
dtype=tf.float32)
elif proposal_type == "nis":
proposal = nis.NIS(
K=FLAGS.K,
data_dim=proposal_data_dim,
energy_hidden_sizes=energy_hidden_sizes,
dtype=tf.float32)
elif proposal_type == "rejection_sampling":
logit_accept_fn = functools.partial(
base.mlp,
layer_sizes=energy_hidden_sizes + [1],
final_activation=None,
name="rejection_sampling/energy_fn_mlp")
proposal = rejection_sampling.RejectionSampling(
T=FLAGS.K,
data_dim=[proposal_data_dim],
logit_accept_fn=logit_accept_fn,
dtype=tf.float32)
elif proposal_type == "gaussian":
proposal = tfd.MultivariateNormalDiag(
loc=tf.zeros([proposal_data_dim], dtype=tf.float32),
scale_diag=tf.ones([proposal_data_dim], dtype=tf.float32))
elif proposal_type == "his":
proposal = his.FullyConnectedHIS(
T=FLAGS.his_T,
data_dim=proposal_data_dim,
energy_hidden_sizes=energy_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
learn_temps=FLAGS.learn_his_temps,
learn_stepsize=FLAGS.learn_his_stepsize,
init_alpha=FLAGS.his_init_alpha,
init_step_size=FLAGS.his_init_stepsize,
dtype=tf.float32)
if model_type == "bernoulli_vae":
model = vae.BernoulliVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=mean,
decoder_hidden_sizes=decoder_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
prior=proposal,
kl_weight=kl_weight,
reparameterize_sample=False,
dtype=tf.float32)
elif model_type == "gaussian_vae":
model = vae.GaussianVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
decoder_hidden_sizes=decoder_hidden_sizes,
decoder_nn_scale=FLAGS.vae_decoder_nn_scale,
q_hidden_sizes=q_hidden_sizes,
scale_min=FLAGS.scale_min,
prior=proposal,
kl_weight=kl_weight,
dtype=tf.float32)
elif model_type == "conv_gaussian_vae":
model = vae.ConvGaussianVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
base_depth=FLAGS.base_depth,
scale_min=FLAGS.scale_min,
prior=proposal,
kl_weight=kl_weight,
dtype=tf.float32)
elif model_type == "conv_bernoulli_vae":
model = vae.ConvBernoulliVAE(
latent_dim=FLAGS.latent_dim,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
scale_min=FLAGS.scale_min,
prior=proposal,
kl_weight=kl_weight,
dtype=tf.float32)
elif model_type == "nis":
model = nis.NIS(
K=FLAGS.K,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
energy_hidden_sizes=energy_hidden_sizes,
proposal=proposal,
reparameterize_proposal_samples=FLAGS.reparameterize_proposal,
dtype=tf.float32)
elif model_type == "his":
model = his.FullyConnectedHIS(
proposal=proposal,
T=FLAGS.his_T,
data_dim=data_dim,
data_mean=None if FLAGS.squash else mean,
energy_hidden_sizes=energy_hidden_sizes,
q_hidden_sizes=q_hidden_sizes,
learn_temps=FLAGS.learn_his_temps,
learn_stepsize=FLAGS.learn_his_stepsize,
init_alpha=FLAGS.his_init_alpha,
init_step_size=FLAGS.his_init_stepsize,
dtype=tf.float32)
# elif model_type == "hisvae":
# model = his.HISVAE(
# latent_dim=FLAGS.latent_dim,
# proposal=proposal,
# T=FLAGS.his_T,
# data_dim=data_dim,
# data_mean=mean,
# energy_hidden_sizes=energy_hidden_sizes,
# q_hidden_sizes=q_hidden_sizes,
# decoder_hidden_sizes=decoder_hidden_sizes,
# learn_temps=FLAGS.learn_his_temps,
# learn_stepsize=FLAGS.learn_his_stepsize,
# init_alpha=FLAGS.his_init_alpha,
# init_step_size=FLAGS.his_init_stepsize,
# squash=FLAGS.squash,
# kl_weight=kl_weight,
# dtype=tf.float32)
if FLAGS.squash:
model = base.SquashedDistribution(distribution=model, data_mean=mean)
return model
