我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用chainer.optimizers.RMSprop()。
def get_optimizer(model, opt, lr=None, adam_alpha=None, adam_beta1=None, adam_beta2=None, adam_eps=None, weight_decay=None): if opt == 'MomentumSGD': optimizer = optimizers.MomentumSGD(lr=lr, momentum=0.9) elif opt == 'Adam': optimizer = optimizers.Adam( alpha=adam_alpha, beta1=adam_beta1, beta2=adam_beta2, eps=adam_eps) elif opt == 'AdaGrad': optimizer = optimizers.AdaGrad(lr=lr) elif opt == 'RMSprop': optimizer = optimizers.RMSprop(lr=lr) else: raise Exception('No optimizer is selected') # The first model as the master model optimizer.setup(model) if opt == 'MomentumSGD': optimizer.add_hook( chainer.optimizer.WeightDecay(weight_decay)) return optimizer
def get_optimizer(opt, lr=None, adam_alpha=None, adam_beta1=None, adam_beta2=None, adam_eps=None, weight_decay=None): if opt == 'MomentumSGD': optimizer = optimizers.MomentumSGD(lr=lr, momentum=0.9) elif opt == 'Adam': optimizer = optimizers.Adam( alpha=adam_alpha, beta1=adam_beta1, beta2=adam_beta2, eps=adam_eps) elif opt == 'AdaGrad': optimizer = optimizers.AdaGrad(lr=lr) elif opt == 'RMSprop': optimizer = optimizers.RMSprop(lr=lr) else: raise Exception('No optimizer is selected') # The first model as the master model if opt == 'MomentumSGD': optimizer.decay = weight_decay return optimizer
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 test_share_states(self): model = L.Linear(2, 2) opt_a = optimizers.RMSprop() opt_a.setup(model) arrays = async.share_states_as_shared_arrays(opt_a) opt_b = optimizers.RMSprop() opt_b.setup(copy.deepcopy(model)) # In Chainer v2, a model cannot be set up by two optimizers or more. opt_c = optimizers.RMSprop() opt_c.setup(copy.deepcopy(model)) """ Removed the tests by assert_different_pointers since they are trivial now. """ async.set_shared_states(opt_b, arrays) async.set_shared_states(opt_c, arrays) def assert_same_pointers(a, b): a = a.target b = b.target for param_name, param_a in a.namedparams(): param_b = dict(b.namedparams())[param_name] state_a = param_a.update_rule.state state_b = param_b.update_rule.state self.assertTrue(state_a) self.assertTrue(state_b) for state_name, state_val_a in state_a.items(): state_val_b = state_b[state_name] self.assertTrue(isinstance( state_val_a, np.ndarray)) self.assertTrue(isinstance( state_val_b, np.ndarray)) self.assertEqual(state_val_a.ctypes.data, state_val_b.ctypes.data) assert_same_pointers(opt_a, opt_b) assert_same_pointers(opt_a, opt_c)
def __init__(self, model=None, lr=0.045, decay=0.9, eps=1.0, weight_decay=4.0e-5, clip=2.0): super(OptimizerGooglenetV3, self).__init__(model) optimizer = optimizers.RMSprop(lr, decay, eps) weight_decay = chainer.optimizer.WeightDecay(weight_decay) clip = chainer.optimizer.GradientClipping(clip) optimizer.setup(self.model) optimizer.add_hook(weight_decay) optimizer.add_hook(clip) self.optimizer = optimizer
def create(self): return optimizers.RMSprop(0.1)
def train(args): nz = args.nz batch_size = args.batch_size epochs = args.epochs gpu = args.gpu # CIFAR-10 images in range [-1, 1] (tanh generator outputs) train, _ = datasets.get_cifar10(withlabel=False, ndim=3, scale=2) train -= 1.0 train_iter = iterators.SerialIterator(train, batch_size) z_iter = RandomNoiseIterator(GaussianNoiseGenerator(0, 1, args.nz), batch_size) optimizer_generator = optimizers.RMSprop(lr=0.00005) optimizer_critic = optimizers.RMSprop(lr=0.00005) optimizer_generator.setup(Generator()) optimizer_critic.setup(Critic()) updater = WassersteinGANUpdater( iterator=train_iter, noise_iterator=z_iter, optimizer_generator=optimizer_generator, optimizer_critic=optimizer_critic, device=gpu) trainer = training.Trainer(updater, stop_trigger=(epochs, 'epoch')) trainer.extend(extensions.ProgressBar()) trainer.extend(extensions.LogReport(trigger=(1, 'iteration'))) trainer.extend(GeneratorSample(), trigger=(1, 'epoch')) trainer.extend(extensions.PrintReport(['epoch', 'iteration', 'critic/loss', 'critic/loss/real', 'critic/loss/fake', 'generator/loss'])) trainer.run()
