Python optim.Rprop方法代码示例

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def create_optimizer(parameters, opt):
    lr = opt.learning_rate
    # default learning rates:
    # sgd - 0.5, adagrad - 0.01, adadelta - 1, adam - 0.001, adamax - 0.002, asgd - 0.01, rmsprop - 0.01, rprop - 0.01
    optim_method = opt.optim_method.casefold()
    if optim_method == 'sgd':
        optimizer = optim.SGD(parameters, lr=lr if lr else 0.5, weight_decay=opt.weight_decay)
    elif optim_method == 'adagrad':
        optimizer = optim.Adagrad(parameters, lr=lr if lr else 0.01, weight_decay=opt.weight_decay)
    elif optim_method == 'adadelta':
        optimizer = optim.Adadelta(parameters, lr=lr if lr else 1, weight_decay=opt.weight_decay)
    elif optim_method == 'adam':
        optimizer = optim.Adam(parameters, lr=lr if lr else 0.001, weight_decay=opt.weight_decay)
    elif optim_method == 'adamax':
        optimizer = optim.Adamax(parameters, lr=lr if lr else 0.002, weight_decay=opt.weight_decay)
    elif optim_method == 'asgd':
        optimizer = optim.ASGD(parameters, lr=lr if lr else 0.01, t0=5000, weight_decay=opt.weight_decay)
    elif optim_method == 'rmsprop':
        optimizer = optim.RMSprop(parameters, lr=lr if lr else 0.01, weight_decay=opt.weight_decay)
    elif optim_method == 'rprop':
        optimizer = optim.Rprop(parameters, lr=lr if lr else 0.01)
    else:
        raise RuntimeError("Invalid optim method: " + opt.optim_method)
    return optimizer 

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def get_optimiser(name, net_params, optim_params):
    lr = optim_params['learning_rate']
    momentum = optim_params['momentum']
    weight_decay = optim_params['weight_decay']
    if(name == "SGD"):
        return optim.SGD(net_params, lr, 
            momentum = momentum, weight_decay = weight_decay)
    elif(name == "Adam"):
        return optim.Adam(net_params, lr, weight_decay = 1e-5)
    elif(name == "SparseAdam"):
        return optim.SparseAdam(net_params, lr)
    elif(name == "Adadelta"):
        return optim.Adadelta(net_params, lr, weight_decay = weight_decay)
    elif(name == "Adagrad"):
        return optim.Adagrad(net_params, lr, weight_decay = weight_decay)
    elif(name == "Adamax"):
        return optim.Adamax(net_params, lr, weight_decay = weight_decay)
    elif(name == "ASGD"):
        return optim.ASGD(net_params, lr, weight_decay = weight_decay)
    elif(name == "LBFGS"):
        return optim.LBFGS(net_params, lr)
    elif(name == "RMSprop"):
        return optim.RMSprop(net_params, lr, momentum = momentum,
            weight_decay = weight_decay)
    elif(name == "Rprop"):
        return optim.Rprop(net_params, lr)
    else:
        raise ValueError("unsupported optimizer {0:}".format(name)) 

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def get_optimizer(s):
    """
    Parse optimizer parameters.
    Input should be of the form:
        - "sgd,lr=0.01"
        - "adagrad,lr=0.1,lr_decay=0.05"
    """
    if "," in s:
        method = s[:s.find(',')]
        optim_params = {}
        for x in s[s.find(',') + 1:].split(','):
            split = x.split('=')
            assert len(split) == 2
            assert re.match("^[+-]?(\d+(\.\d*)?|\.\d+)$", split[1]) is not None
            optim_params[split[0]] = float(split[1])
    else:
        method = s
        optim_params = {}

    if method == 'adadelta':
        optim_fn = optim.Adadelta
    elif method == 'adagrad':
        optim_fn = optim.Adagrad
    elif method == 'adam':
        optim_fn = optim.Adam
    elif method == 'adamax':
        optim_fn = optim.Adamax
    elif method == 'asgd':
        optim_fn = optim.ASGD
    elif method == 'rmsprop':
        optim_fn = optim.RMSprop
    elif method == 'rprop':
        optim_fn = optim.Rprop
    elif method == 'sgd':
        optim_fn = optim.SGD
        assert 'lr' in optim_params
    else:
        raise Exception('Unknown optimization method: "%s"' % method)

    return optim_fn, optim_params 

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def __init__(self, *, lr=0.01, etas=(0.5, 1.2), step_sizes=(1e-06, 50)):
        """Implements the resilient backpropagation algorithm.

        Arguments:
            lr (float, optional): learning rate (default: 1e-2)
            etas (Tuple[float, float], optional): pair of (etaminus, etaplis), that
                are multiplicative increase and decrease factors
                (default: (0.5, 1.2))
            step_sizes (Tuple[float, float], optional): a pair of minimal and
                maximal allowed step sizes (default: (1e-6, 50))
        """

        super().__init__(optim.Rprop, lr=lr, etas=etas, step_sizes=step_sizes) 

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def _set_optimizer(self, model: EmmentalModel) -> None:
        """Set optimizer for learning process.

        Args:
          model: The model to set up the optimizer.
        """
        optimizer_config = Meta.config["learner_config"]["optimizer_config"]
        opt = optimizer_config["optimizer"]

        # If Meta.config["learner_config"]["optimizer_config"]["parameters"] is None,
        # create a parameter group with all parameters in the model, else load user
        # specified parameter groups.
        if optimizer_config["parameters"] is None:
            parameters = filter(lambda p: p.requires_grad, model.parameters())
        else:
            parameters = optimizer_config["parameters"](model)

        optim_dict = {
            # PyTorch optimizer
            "asgd": optim.ASGD,  # type: ignore
            "adadelta": optim.Adadelta,  # type: ignore
            "adagrad": optim.Adagrad,  # type: ignore
            "adam": optim.Adam,  # type: ignore
            "adamw": optim.AdamW,  # type: ignore
            "adamax": optim.Adamax,  # type: ignore
            "lbfgs": optim.LBFGS,  # type: ignore
            "rms_prop": optim.RMSprop,  # type: ignore
            "r_prop": optim.Rprop,  # type: ignore
            "sgd": optim.SGD,  # type: ignore
            "sparse_adam": optim.SparseAdam,  # type: ignore
            # Customize optimizer
            "bert_adam": BertAdam,
        }

        if opt in ["lbfgs", "r_prop", "sparse_adam"]:
            optimizer = optim_dict[opt](
                parameters,
                lr=optimizer_config["lr"],
                **optimizer_config[f"{opt}_config"],
            )
        elif opt in optim_dict.keys():
            optimizer = optim_dict[opt](
                parameters,
                lr=optimizer_config["lr"],
                weight_decay=optimizer_config["l2"],
                **optimizer_config[f"{opt}_config"],
            )
        elif isinstance(opt, optim.Optimizer):  # type: ignore
            optimizer = opt(parameters)
        else:
            raise ValueError(f"Unrecognized optimizer option '{opt}'")

        self.optimizer = optimizer

        if Meta.config["meta_config"]["verbose"]:
            logger.info(f"Using optimizer {self.optimizer}") 

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def get_optimizer(s):
    """
    Parse optimizer parameters.
    Input should be of the form:
        - "sgd,lr=0.01"
        - "adagrad,lr=0.1,lr_decay=0.05"
    """
    if "," in s:
        method = s[:s.find(',')]
        optim_params = {}
        for x in s[s.find(',') + 1:].split(','):
            split = x.split('=')
            assert len(split) == 2
            assert re.match("^[+-]?(\d+(\.\d*)?|\.\d+)$", split[1]) is not None
            optim_params[split[0]] = float(split[1])
    else:
        method = s
        optim_params = {}

    if method == 'adadelta':
        optim_fn = optim.Adadelta
    elif method == 'adagrad':
        optim_fn = optim.Adagrad
    elif method == 'adam':
        optim_fn = optim.Adam
    elif method == 'adamax':
        optim_fn = optim.Adamax
    elif method == 'asgd':
        optim_fn = optim.ASGD
    elif method == 'rmsprop':
        optim_fn = optim.RMSprop
    elif method == 'rprop':
        optim_fn = optim.Rprop
    elif method == 'sgd':
        optim_fn = optim.SGD
        assert 'lr' in optim_params
    else:
        raise Exception('Unknown optimization method: "%s"' % method)

    # check that we give good parameters to the optimizer
    expected_args = inspect.getargspec(optim_fn.__init__)[0]
    assert expected_args[:2] == ['self', 'params']
    if not all(k in expected_args[2:] for k in optim_params.keys()):
        raise Exception('Unexpected parameters: expected "%s", got "%s"' % (
            str(expected_args[2:]), str(optim_params.keys())))

    return optim_fn, optim_params 

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def get_optimizer(parameters, s):
    """
    Parse optimizer parameters.
    Input should be of the form:
        - "sgd,lr=0.01"
        - "adagrad,lr=0.1,lr_decay=0.05"
    """
    if "," in s:
        method = s[:s.find(',')]
        optim_params = {}
        for x in s[s.find(',') + 1:].split(','):
            split = x.split('=')
            assert len(split) == 2
            assert re.match("^[+-]?(\d+(\.\d*)?|\.\d+)$", split[1]) is not None
            optim_params[split[0]] = float(split[1])
    else:
        method = s
        optim_params = {}

    if method == 'adadelta':
        optim_fn = optim.Adadelta
    elif method == 'adagrad':
        optim_fn = optim.Adagrad
    elif method == 'adam':
        optim_fn = optim.Adam
        optim_params['betas'] = (optim_params.get('beta1', 0.9), optim_params.get('beta2', 0.999))
        optim_params.pop('beta1', None)
        optim_params.pop('beta2', None)
    elif method == 'adam_inverse_sqrt':
        optim_fn = AdamInverseSqrtWithWarmup
        optim_params['betas'] = (optim_params.get('beta1', 0.9), optim_params.get('beta2', 0.999))
        optim_params.pop('beta1', None)
        optim_params.pop('beta2', None)
    elif method == 'adamax':
        optim_fn = optim.Adamax
    elif method == 'asgd':
        optim_fn = optim.ASGD
    elif method == 'rmsprop':
        optim_fn = optim.RMSprop
    elif method == 'rprop':
        optim_fn = optim.Rprop
    elif method == 'sgd':
        optim_fn = optim.SGD
        assert 'lr' in optim_params
    else:
        raise Exception('Unknown optimization method: "%s"' % method)

    # check that we give good parameters to the optimizer
    expected_args = inspect.getargspec(optim_fn.__init__)[0]
    assert expected_args[:2] == ['self', 'params']
    if not all(k in expected_args[2:] for k in optim_params.keys()):
        raise Exception('Unexpected parameters: expected "%s", got "%s"' % (
            str(expected_args[2:]), str(optim_params.keys())))

    return optim_fn(parameters, **optim_params) 

# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import Rprop [as 别名]
def get_optimizer(s):
    """
    Parse optimizer parameters.
    Input should be of the form:
        - "sgd,lr=0.01"
        - "adagrad,lr=0.1,lr_decay=0.05"
    """
    if "," in s:
        method = s[:s.find(',')]
        optim_params = {}
        for x in s[s.find(',') + 1:].split(','):
            split = x.split('=')
            assert len(split) == 2
            assert re.match("^[+-]?(\d+(\.\d*)?|\.\d+)$", split[1]) is not None
            optim_params[split[0]] = float(split[1])
    else:
        method = s
        optim_params = {}

    if method == 'adadelta':
        optim_fn = optim.Adadelta
    elif method == 'adagrad':
        optim_fn = optim.Adagrad
    elif method == 'adam':
        optim_fn = optim.Adam
    elif method == 'adamax':
        optim_fn = optim.Adamax
    elif method == 'asgd':
        optim_fn = optim.ASGD
    elif method == 'rmsprop':
        optim_fn = optim.RMSprop
    elif method == 'rprop':
        optim_fn = optim.Rprop
    elif method == 'sgd':
        optim_fn = optim.SGD
        assert 'lr' in optim_params
    else:
        raise Exception('Unknown optimization method: "%s"' % method)

    # check that we give good parameters to the optimizer
    expected_args = inspect.getfullargspec(optim_fn.__init__)[0]
    assert expected_args[:2] == ['self', 'params']
    if not all(k in expected_args[2:] for k in optim_params.keys()):
        raise Exception('Unexpected parameters: expected "%s", got "%s"' % (
            str(expected_args[2:]), str(optim_params.keys())))

    return optim_fn, optim_params