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 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])