#!/usr/bin/env python3
import tensorflow as tf
from garage.misc import logger
logger.set_tensorboard_dir("data/local/histogram_example")
N = 400
for i in range(N):
sess = tf.Session()
sess.__enter__()
k_val = i / float(N)
logger.record_histogram_by_type('gamma', key='gamma', alpha=k_val)
logger.record_histogram_by_type('normal',
key='normal',
mean=5 * k_val,
stddev=1.0)
logger.record_histogram_by_type('poisson', key='poisson', lam=k_val)
logger.record_histogram_by_type('uniform',
key='uniform',
maxval=k_val * 10)
logger.record_tabular("app", k_val)
logger.record_histogram("gass", k_val)
logger.dump_tensorboard(step=i)
e = GaussianMLPEmbedding(embed_spec, std_share_network=True)
p = GaussianMLPMultitaskPolicy(env_spec=env_spec,
task_space=task_space,
embedding=e,
std_share_network=True)
my_task = task_space.new_tensor_variable(name="my_task", extra_dims=1)
my_obs = obs_space.new_tensor_variable(name="my_obs", extra_dims=1)
with tf.name_scope("build_opt"):
dist_info = e.dist_info_sym(my_task, name="e_dist_info")
p_dist_info = p.dist_info_sym(my_task, my_obs, name="p_dist_info")
with tf.name_scope("test_fixture"):
a = tf.exp(e.latent)
b = tf.exp(e._latent_std_param)
sess.run(tf.global_variables_initializer())
t = task_space.sample()
o = obs_space.sample()
to = np.concatenate([t, o], axis=0)
p.get_action(to)
z = latent_space.sample()
p.get_action_from_latent(z, o)
logger.dump_tensorboard()
ipdb.set_trace()
print("done!")