Python torch.optim 模块，ASGD 实例源码

我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用torch.optim.ASGD。

项目：pytorch-dist 作者：apaszke | 项目源码 | 文件源码

def test_asgd(self):
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, alpha=0.8),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, alpha=0.8)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, t0=1e3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, t0=1e3)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD([weight, bias], lr=1e-3, t0=100)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD(
                self._build_params_dict(weight, bias, lr=1e-2),
                lr=1e-3, t0=100)
        )

项目：pytorch 作者：tylergenter | 项目源码 | 文件源码

def test_asgd(self):
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, alpha=0.8),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, alpha=0.8)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, t0=1e3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, t0=1e3)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD([weight, bias], lr=1e-3, t0=100)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD(
                self._build_params_dict(weight, bias, lr=1e-2),
                lr=1e-3, t0=100)
        )

项目：pytorch-coriander 作者：hughperkins | 项目源码 | 文件源码

def test_asgd(self):
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, alpha=0.8),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, alpha=0.8)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, t0=1e3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, t0=1e3)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD([weight, bias], lr=1e-3, t0=100)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD(
                self._build_params_dict(weight, bias, lr=1e-2),
                lr=1e-3, t0=100)
        )

项目：pytorch 作者：ezyang | 项目源码 | 文件源码

def test_asgd(self):
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, alpha=0.8),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, alpha=0.8)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, t0=1e3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, t0=1e3)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD([weight, bias], lr=1e-3, t0=100)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD(
                self._build_params_dict(weight, bias, lr=1e-2),
                lr=1e-3, t0=100)
        )

项目：covfefe 作者：deepnn | 项目源码 | 文件源码

def asgd(w, lr=0.01, lam=0.0001, alpha=0.75, t0=1000000.0, w_decay=0):
    return nn.ASGD(params=w, lr=lr,
                   lambd=lam, alpha=alpha,
                   t0=t0, weight_decay=w_decay)

项目：pytorch 作者：pytorch | 项目源码 | 文件源码

def test_asgd(self):
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, alpha=0.8),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, alpha=0.8)
        )
        self._test_rosenbrock(
            lambda params: optim.ASGD(params, lr=1e-3, t0=1e3),
            wrap_old_fn(old_optim.asgd, eta0=1e-3, t0=1e3)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD([weight, bias], lr=1e-3, t0=100)
        )
        self._test_basic_cases(
            lambda weight, bias: optim.ASGD(
                self._build_params_dict(weight, bias, lr=1e-2),
                lr=1e-3, t0=100)
        )

项目：SentEval 作者：facebookresearch | 项目源码 | 文件源码

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

项目：FaderNetworks 作者：facebookresearch | 项目源码 | 文件源码

def get_optimizer(model, 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', 0.5), 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(model.parameters(), **optim_params)

项目：InferSent 作者：facebookresearch | 项目源码 | 文件源码

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