Python torch.optim.ASGD Examples

The following are 14 code examples of torch.optim.ASGD(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module torch.optim , or try the search function .
Example #1
Source File: optimization.py    From SeaRNN-open with MIT License 6 votes vote down vote up
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 
Example #2
Source File: make_optimizer.py    From MAMS-for-ABSA with Apache License 2.0 6 votes vote down vote up
def make_optimizer(config, model):
    mode = config['mode']
    config = config['aspect_' + mode + '_model'][config['aspect_' + mode + '_model']['type']]
    lr = config['learning_rate']
    weight_decay = config['weight_decay']
    opt = {
        'sgd': optim.SGD,
        'adadelta': optim.Adadelta,
        'adam': optim.Adam,
        'adamax': optim.Adamax,
        'adagrad': optim.Adagrad,
        'asgd': optim.ASGD,
        'rmsprop': optim.RMSprop,
        'adabound': adabound.AdaBound
    }
    if 'momentum' in config:
        optimizer = opt[config['optimizer']](model.parameters(), lr=lr, weight_decay=weight_decay, momentum=config['momentum'])
    else:
        optimizer = opt[config['optimizer']](model.parameters(), lr=lr, weight_decay=weight_decay)
    return optimizer 
Example #3
Source File: optimizer.py    From XenonPy with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def __init__(self, *, lr=0.002, lambd=0.0001, alpha=0.75, t0=1000000.0, weight_decay=0):
        """Implements Averaged Stochastic Gradient Descent.

        It has been proposed in `Acceleration of stochastic approximation by
        averaging`_.

        Arguments:
            lr (float, optional): learning rate (default: 1e-2)
            lambd (float, optional): decay term (default: 1e-4)
            alpha (float, optional): power for eta update (default: 0.75)
            t0 (float, optional): point at which to start averaging (default: 1e6)
            weight_decay (float, optional): weight decay (L2 penalty) (default: 0)

        .. _Acceleration of stochastic approximation by averaging:
            http://dl.acm.org/citation.cfm?id=131098
        """

        super().__init__(optim.ASGD, lr=lr, lambd=lambd, alpha=alpha, t0=t0, weight_decay=weight_decay) 
Example #4
Source File: get_optimizer.py    From PyMIC with Apache License 2.0 5 votes vote down vote up
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)) 
Example #5
Source File: utils.py    From DeMa-BWE with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
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 
Example #6
Source File: model.py    From DiPS with Apache License 2.0 5 votes vote down vote up
def _optim(self):
        self.params = list(self.encoder.parameters()) + list(self.decoder.parameters())

        if self.config.opt     == 'adam':
            self.optimizer = optim.Adam(self.params, lr=self.config.lr)
        elif self.config.opt   == 'adadelta':
            self.optimizer = optim.Adadelta(self.params, lr=self.config.lr)
        elif self.config.opt   == 'asgd':
            self.optimizer = optim.ASGD(self.params, lr=self.config.lr)
        else:
            self.optimizer = optim.SGD(self.params, lr=self.config.lr) 
Example #7
Source File: learner.py    From emmental with MIT License 4 votes vote down vote up
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}") 
Example #8
Source File: mutils.py    From e-SNLI with MIT License 4 votes vote down vote up
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 
Example #9
Source File: utils.py    From e-SNLI with MIT License 4 votes vote down vote up
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 
Example #10
Source File: mutils.py    From neural_chat with MIT License 4 votes vote down vote up
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 
Example #11
Source File: utils.py    From dl4mt-seqgen with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up
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) 
Example #12
Source File: utils.py    From transferable_sent2vec with MIT License 4 votes vote down vote up
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 
Example #13
Source File: mutils.py    From T2F with MIT License 4 votes vote down vote up
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 
Example #14
Source File: utils.py    From nlp-recipes with MIT License 4 votes vote down vote up
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