def test_function():
with tf.Graph().as_default():
x = tf.placeholder(tf.int32, (), name="x")
y = tf.placeholder(tf.int32, (), name="y")
z = 3 * x + 2 * y
lin = function([x, y], z, givens={y: 0})
with single_threaded_session():
initialize()
assert lin(2) == 6
assert lin(2, 2) == 10
def test_multikwargs():
with tf.Graph().as_default():
x = tf.placeholder(tf.int32, (), name="x")
with tf.variable_scope("other"):
x2 = tf.placeholder(tf.int32, (), name="x")
z = 3 * x + 2 * x2
lin = function([x, x2], z, givens={x2: 0})
with single_threaded_session():
initialize()
assert lin(2) == 6
assert lin(2, 2) == 10
def train(num_episodes, seed, space, evaluator, num_episodes_per_batch,
reward_rule):
rank = MPI.COMM_WORLD.Get_rank()
sess = U.single_threaded_session()
sess.__enter__()
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank(
) if seed is not None else None
set_global_seeds(workerseed)
# MAKE ENV_NAS
cs_kwargs = space['create_structure'].get('kwargs')
if cs_kwargs is None:
structure = space['create_structure']['func']()
else:
structure = space['create_structure']['func'](**cs_kwargs)
num_nodes = structure.num_nodes
timesteps_per_actorbatch = num_nodes * num_episodes_per_batch
num_timesteps = timesteps_per_actorbatch * num_episodes
env = NasEnv(space, evaluator, structure)
def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
return lstm_ph.LstmPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
num_units=32)
pposgd_sync.learn(env,
policy_fn,
max_timesteps=num_timesteps,
timesteps_per_actorbatch=timesteps_per_actorbatch,
clip_param=0.2,
entcoeff=0.01,
optim_epochs=4,
optim_stepsize=1e-3,
optim_batchsize=15,
gamma=0.99,
lam=0.95,
schedule='linear',
reward_rule=reward_rule)
env.close()
def train(num_iter, seed, evaluator, num_episodes_per_iter):
rank = MPI.COMM_WORLD.Get_rank()
sess = U.single_threaded_session()
sess.__enter__()
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank(
) if seed is not None else None
set_global_seeds(workerseed)
# MAKE ENV_NAS
timesteps_per_episode = 10
timesteps_per_actorbatch = timesteps_per_episode * num_episodes_per_iter
num_timesteps = timesteps_per_actorbatch * num_iter
env = MathEnv(evaluator)
def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
return lstm_ph.LstmPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
num_units=64)
pposgd_sync_ph.learn(
env,
policy_fn,
max_timesteps=int(num_timesteps),
timesteps_per_actorbatch=timesteps_per_actorbatch,
clip_param=0.2,
entcoeff=0.01, #0.01,
optim_epochs=4,
optim_stepsize=1e-3,
optim_batchsize=10,
gamma=0.99, # 0.99
lam=0.95, # 0.95
schedule='linear',
reward_rule=reward_for_final_timestep)
env.close()