def create_classifier(n_vocab, doc_length, wv_size, filter_sizes, hidden_units, output_channel, initialW, non_static, batch_size, epoch, gpu):
model = NNModel(n_vocab=n_vocab,
doc_length=doc_length,
wv_size=wv_size,
filter_sizes=filter_sizes,
hidden_units=hidden_units,
output_channel=output_channel,
initialW=initialW,
non_static=non_static)
# optimizer = optimizers.Adam()
optimizer = optimizers.AdaDelta()
return (model, ChainerEstimator(model=SoftmaxCrossEntropyClassifier(model),
optimizer=optimizer,
batch_size=batch_size,
device=gpu,
stop_trigger=(epoch, 'epoch')))
python类AdaDelta()的实例源码
def get_optimizer(self, name, lr, momentum=0.9):
if name.lower() == "adam":
return optimizers.Adam(alpha=lr, beta1=momentum)
if name.lower() == "smorms3":
return optimizers.SMORMS3(lr=lr)
if name.lower() == "adagrad":
return optimizers.AdaGrad(lr=lr)
if name.lower() == "adadelta":
return optimizers.AdaDelta(rho=momentum)
if name.lower() == "nesterov" or name.lower() == "nesterovag":
return optimizers.NesterovAG(lr=lr, momentum=momentum)
if name.lower() == "rmsprop":
return optimizers.RMSprop(lr=lr, alpha=momentum)
if name.lower() == "momentumsgd":
return optimizers.MomentumSGD(lr=lr, mommentum=mommentum)
if name.lower() == "sgd":
return optimizers.SGD(lr=lr)
def get_optimizer(name, lr, momentum=0.9):
if name.lower() == "adam":
return optimizers.Adam(alpha=lr, beta1=momentum)
if name.lower() == "eve":
return Eve(alpha=lr, beta1=momentum)
if name.lower() == "adagrad":
return optimizers.AdaGrad(lr=lr)
if name.lower() == "adadelta":
return optimizers.AdaDelta(rho=momentum)
if name.lower() == "nesterov" or name.lower() == "nesterovag":
return optimizers.NesterovAG(lr=lr, momentum=momentum)
if name.lower() == "rmsprop":
return optimizers.RMSprop(lr=lr, alpha=momentum)
if name.lower() == "momentumsgd":
return optimizers.MomentumSGD(lr=lr, mommentum=mommentum)
if name.lower() == "sgd":
return optimizers.SGD(lr=lr)
def update_momentum(self, momentum):
if isinstance(self.optimizer, optimizers.Adam):
self.optimizer.beta1 = momentum
return
if isinstance(self.optimizer, Eve):
self.optimizer.beta1 = momentum
return
if isinstance(self.optimizer, optimizers.AdaDelta):
self.optimizer.rho = momentum
return
if isinstance(self.optimizer, optimizers.NesterovAG):
self.optimizer.momentum = momentum
return
if isinstance(self.optimizer, optimizers.RMSprop):
self.optimizer.alpha = momentum
return
if isinstance(self.optimizer, optimizers.MomentumSGD):
self.optimizer.mommentum = momentum
return
def get_optimizer(name, lr, momentum=0.9):
if name.lower() == "adam":
return chainer.optimizers.Adam(alpha=lr, beta1=momentum)
if name.lower() == "eve":
return Eve(alpha=lr, beta1=momentum)
if name.lower() == "adagrad":
return chainer.optimizers.AdaGrad(lr=lr)
if name.lower() == "adadelta":
return chainer.optimizers.AdaDelta(rho=momentum)
if name.lower() == "nesterov" or name.lower() == "nesterovag":
return chainer.optimizers.NesterovAG(lr=lr, momentum=momentum)
if name.lower() == "rmsprop":
return chainer.optimizers.RMSprop(lr=lr, alpha=momentum)
if name.lower() == "momentumsgd":
return chainer.optimizers.MomentumSGD(lr=lr, mommentum=mommentum)
if name.lower() == "sgd":
return chainer.optimizers.SGD(lr=lr)
raise Exception()
def update_momentum(self, momentum):
if isinstance(self.optimizer, optimizers.Adam):
self.optimizer.beta1 = momentum
return
if isinstance(self.optimizer, Eve):
self.optimizer.beta1 = momentum
return
if isinstance(self.optimizer, optimizers.AdaDelta):
self.optimizer.rho = momentum
return
if isinstance(self.optimizer, optimizers.NesterovAG):
self.optimizer.momentum = momentum
return
if isinstance(self.optimizer, optimizers.RMSprop):
self.optimizer.alpha = momentum
return
if isinstance(self.optimizer, optimizers.MomentumSGD):
self.optimizer.mommentum = momentum
return
def update_momentum(self, momentum):
if isinstance(self._optimizer, optimizers.Adam):
self._optimizer.beta1 = momentum
return
if isinstance(self._optimizer, Eve):
self._optimizer.beta1 = momentum
return
if isinstance(self._optimizer, optimizers.AdaDelta):
self._optimizer.rho = momentum
return
if isinstance(self._optimizer, optimizers.NesterovAG):
self._optimizer.momentum = momentum
return
if isinstance(self._optimizer, optimizers.RMSprop):
self._optimizer.alpha = momentum
return
if isinstance(self._optimizer, optimizers.MomentumSGD):
self._optimizer.mommentum = momentum
return
def get_optimizer(name, lr, momentum=0.9):
if name.lower() == "adam":
return optimizers.Adam(alpha=lr, beta1=momentum)
if name.lower() == "eve":
return Eve(alpha=lr, beta1=momentum)
if name.lower() == "adagrad":
return optimizers.AdaGrad(lr=lr)
if name.lower() == "adadelta":
return optimizers.AdaDelta(rho=momentum)
if name.lower() == "nesterov" or name.lower() == "nesterovag":
return optimizers.NesterovAG(lr=lr, momentum=momentum)
if name.lower() == "rmsprop":
return optimizers.RMSprop(lr=lr, alpha=momentum)
if name.lower() == "momentumsgd":
return optimizers.MomentumSGD(lr=lr, mommentum=mommentum)
if name.lower() == "sgd":
return optimizers.SGD(lr=lr)
test_optimizers_by_linear_model.py 文件源码
项目：chainer-deconv
作者: germanRos
项目源码
文件源码
阅读 18
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def create(self):
return optimizers.AdaDelta(eps=1e-5)
def setUp(self):
fd, path = tempfile.mkstemp()
os.close(fd)
self.temp_file_path = path
child = link.Chain(linear=links.Linear(2, 3))
child.add_param('Wc', (2, 3))
self.parent = link.Chain(child=child)
self.parent.add_param('Wp', (2, 3))
self.optimizer = optimizers.AdaDelta()
self.optimizer.setup(self.parent)
self.savez = numpy.savez_compressed if self.compress else numpy.savez
def setUp(self):
fd, path = tempfile.mkstemp()
os.close(fd)
self.temp_file_path = path
child = link.Chain(linear=links.Linear(2, 3))
child.add_param('Wc', (2, 3))
self.parent = link.Chain(child=child)
self.parent.add_param('Wp', (2, 3))
self.optimizer = optimizers.AdaDelta()
self.optimizer.setup(self.parent)
def setOptimizer(args, EncDecAtt):
# optimizer???
if args.optimizer == 'SGD':
optimizer = chaOpt.SGD(lr=args.lrate)
sys.stdout.write(
'# SET Learning %s: initial learning rate: %e\n' %
(args.optimizer, optimizer.lr))
elif args.optimizer == 'Adam':
# assert 0, "Currently Adam is not supported for asynchronous update"
optimizer = chaOpt.Adam(alpha=args.lrate)
sys.stdout.write(
'# SET Learning %s: initial learning rate: %e\n' %
(args.optimizer, optimizer.alpha))
elif args.optimizer == 'MomentumSGD':
optimizer = chaOpt.MomentumSGD(lr=args.lrate)
sys.stdout.write(
'# SET Learning %s: initial learning rate: %e\n' %
(args.optimizer, optimizer.lr))
elif args.optimizer == 'AdaDelta':
optimizer = chaOpt.AdaDelta(rho=args.lrate)
sys.stdout.write(
'# SET Learning %s: initial learning rate: %e\n' %
(args.optimizer, optimizer.rho))
else:
assert 0, "ERROR"
optimizer.setup(EncDecAtt.model) # ???optimizer?????????
if args.optimizer == 'Adam':
optimizer.t = 1 # warning?????????hack ???????????
return optimizer
def update_learning_rate(self, lr):
if isinstance(self.optimizer, optimizers.Adam):
self.optimizer.alpha = lr
return
if isinstance(self.optimizer, Eve):
self.optimizer.alpha = lr
return
if isinstance(self.optimizer, optimizers.AdaDelta):
# AdaDelta has no learning rate
return
self.optimizer.lr = lr
def update_laerning_rate(self, lr):
if isinstance(self.optimizer, optimizers.Adam):
self.optimizer.alpha = lr
return
if isinstance(self.optimizer, Eve):
self.optimizer.alpha = lr
return
if isinstance(self.optimizer, optimizers.AdaDelta):
# AdaDelta has no learning rate
return
self.optimizer.lr = lr
def update_learning_rate(self, lr):
if isinstance(self._optimizer, optimizers.Adam):
self._optimizer.alpha = lr
return
if isinstance(self._optimizer, Eve):
self._optimizer.alpha = lr
return
if isinstance(self._optimizer, optimizers.AdaDelta):
# AdaDelta has no learning rate
return
self._optimizer.lr = lr
