def __init__(self,
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-8):
super(AdamOptimizer, self).__init__()
self.updater = updaters.AdamUpdater(learning_rate, beta1, beta2,
epsilon)
def __init__(self,
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-8,
use_locking=False,
name='Adam'):
super(AdamOptimizer, self).__init__(use_locking, name)
self.updater = updaters.AdamUpdater(learning_rate, beta1, beta2,
epsilon)
def __init__(self, prototxt):
super(AdamSolver, self).__init__(prototxt=prototxt)
self._updater = updaters.AdamUpdater(**self._update_param)
# generates update targets
for layer, blobs in self._net.params.items(): self._lr_blobs.extend(blobs)
for idx, blob in enumerate(self._lr_blobs):
if self._net._lr_mults[idx] > 0:
if blob.diff is None: continue
self._updater.append((blob.data, blob.diff),
self._net._lr_mults[idx], self._net._decay_mults[idx])
self.train = self._net.function
self.tests = [test_net.function for test_net in self._test_nets]
self.update = function(updater=self._updater)
def __init__(
self,
learning_rate,
decay=0.9,
momentum=0.0,
epsilon=1e-10,
use_locking=False,
centered=False,
name='RMSProp',
):
super(RMSPropOptimizer, self).__init__(use_locking, name)
if momentum > 0.:
self.updater = _updaters.AdamUpdater(
learning_rate, momentum, decay, epsilon)
else:
self.updater = _updaters.RMSPropUpdater(
learning_rate, decay, epsilon)
self._set_dynamic_lr(learning_rate)
def __init__(self, prototxt):
super(AdamSolver, self).__init__(prototxt=prototxt)
self._optimizer = updaters.AdamUpdater(**self._update_param)
self.BuildOptimizer()
def __init__(self, proto_txt):
super(AdamSolver, self).__init__(proto_txt=proto_txt)
self.optimizer = _updaters.AdamUpdater(**self._optimizer_arguments)
self.BuildOptimizer()