def cached_function(inputs, outputs):
import theano
with Message("Hashing theano fn"):
if hasattr(outputs, '__len__'):
hash_content = tuple(map(theano.pp, outputs))
else:
hash_content = theano.pp(outputs)
cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
cache_dir = Path('~/.hierctrl_cache')
cache_dir = cache_dir.expanduser()
cache_dir.mkdir_p()
cache_file = cache_dir / ('%s.pkl' % cache_key)
if cache_file.exists():
with Message("unpickling"):
with open(cache_file, "rb") as f:
try:
return pickle.load(f)
except Exception:
pass
with Message("compiling"):
fun = compile_function(inputs, outputs)
with Message("picking"):
with open(cache_file, "wb") as f:
pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
return fun
# Immutable, lazily evaluated dict
python类pp()的实例源码
def cached_function(inputs, outputs):
import theano
with Message("Hashing theano fn"):
if hasattr(outputs, '__len__'):
hash_content = tuple(map(theano.pp, outputs))
else:
hash_content = theano.pp(outputs)
cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
cache_dir = Path('~/.hierctrl_cache')
cache_dir = cache_dir.expanduser()
cache_dir.mkdir_p()
cache_file = cache_dir / ('%s.pkl' % cache_key)
if cache_file.exists():
with Message("unpickling"):
with open(cache_file, "rb") as f:
try:
return pickle.load(f)
except Exception:
pass
with Message("compiling"):
fun = compile_function(inputs, outputs)
with Message("picking"):
with open(cache_file, "wb") as f:
pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
return fun
# Immutable, lazily evaluated dict
def cached_function(inputs, outputs):
import theano
with Message("Hashing theano fn"):
if hasattr(outputs, '__len__'):
hash_content = tuple(map(theano.pp, outputs))
else:
hash_content = theano.pp(outputs)
cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
cache_dir = Path('~/.hierctrl_cache')
cache_dir = cache_dir.expanduser()
cache_dir.mkdir_p()
cache_file = cache_dir / ('%s.pkl' % cache_key)
if cache_file.exists():
with Message("unpickling"):
with open(cache_file, "rb") as f:
try:
return pickle.load(f)
except Exception:
pass
with Message("compiling"):
fun = compile_function(inputs, outputs)
with Message("picking"):
with open(cache_file, "wb") as f:
pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
return fun
# Immutable, lazily evaluated dict
def cached_function(inputs, outputs):
import theano
with Message("Hashing theano fn"):
if hasattr(outputs, '__len__'):
hash_content = tuple(map(theano.pp, outputs))
else:
hash_content = theano.pp(outputs)
cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
cache_dir = Path('~/.hierctrl_cache')
cache_dir = cache_dir.expanduser()
cache_dir.mkdir_p()
cache_file = cache_dir / ('%s.pkl' % cache_key)
if cache_file.exists():
with Message("unpickling"):
with open(cache_file, "rb") as f:
try:
return pickle.load(f)
except Exception:
pass
with Message("compiling"):
fun = compile_function(inputs, outputs)
with Message("picking"):
with open(cache_file, "wb") as f:
pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
return fun
# Immutable, lazily evaluated dict
def cached_function(inputs, outputs):
import theano
with Message("Hashing theano fn"):
if hasattr(outputs, '__len__'):
hash_content = tuple(map(theano.pp, outputs))
else:
hash_content = theano.pp(outputs)
cache_key = hex(hash(hash_content) & (2 ** 64 - 1))[:-1]
cache_dir = Path('~/.hierctrl_cache')
cache_dir = cache_dir.expanduser()
cache_dir.mkdir_p()
cache_file = cache_dir / ('%s.pkl' % cache_key)
if cache_file.exists():
with Message("unpickling"):
with open(cache_file, "rb") as f:
try:
return pickle.load(f)
except Exception:
pass
with Message("compiling"):
fun = compile_function(inputs, outputs)
with Message("picking"):
with open(cache_file, "wb") as f:
pickle.dump(fun, f, protocol=pickle.HIGHEST_PROTOCOL)
return fun
# Immutable, lazily evaluated dict
def run(self,
epochs,
steps,
api_key,
rollouts_per_epoch = 20,
updateTargetNetwork = defaultRunSettings['updateTargetNetwork'],
explorationRate = defaultRunSettings['explorationRate'],
miniBatchSize = defaultRunSettings['miniBatchSize'],
learnStart = defaultRunSettings['learnStart'],
renderPerXEpochs = defaultRunSettings['renderPerXEpochs'],
shouldRender = defaultRunSettings['shouldRender'],
experimentId = defaultRunSettings['experimentId'],
force = defaultRunSettings['force'],
upload = defaultRunSettings['upload']):
last100Scores = [0] * 100
last100ScoresIndex = 0
last100Filled = False
stepCounter = 0
if not experimentId == None:
self.env.monitor.start('tmp/'+experimentId, force = force)
for epoch in xrange(epochs):
I = 1
observation = self.env.reset();
for t in xrange(steps):
policyValues = self.runModel(self.policyModel, observation)
action = self.selectActionByProbability(policyValues)
newObservation, reward, done, info = self.env.step(action)
cost, grads = self.get_cost_grads(self.policyModel);
print (theano.pp(grads[1][0]));
if done:
delta = reward + self.discountFactor * self.runModel(self.valueModel, newObservation) - self.runModel(self.valueModel, observation)
else :
delta = reward - self.runModel(self.valueModel, observation) # because the value for new obs is 0
self.env.monitor.close()
if upload:
gym.upload('/tmp/'+experimentId, api_key=api_key)
