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)
)
python类ASGD的实例源码
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)
)
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)
)
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)
)
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)
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)
)
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+)$", split[1]) is not None
optim_params[split[0]] = float(split[1])
else:
method = s
optim_params = {}
if method == 'adadelta':
optim_fn = optim.Adadelta
elif method == 'adagrad':
optim_fn = optim.Adagrad
elif method == 'adam':
optim_fn = optim.Adam
elif method == 'adamax':
optim_fn = optim.Adamax
elif method == 'asgd':
optim_fn = optim.ASGD
elif method == 'rmsprop':
optim_fn = optim.RMSprop
elif method == 'rprop':
optim_fn = optim.Rprop
elif method == 'sgd':
optim_fn = optim.SGD
assert 'lr' in optim_params
else:
raise Exception('Unknown optimization method: "%s"' % method)
# check that we give good parameters to the optimizer
expected_args = inspect.getargspec(optim_fn.__init__)[0]
assert expected_args[:2] == ['self', 'params']
if not all(k in expected_args[2:] for k in optim_params.keys()):
raise Exception('Unexpected parameters: expected "%s", got "%s"' % (
str(expected_args[2:]), str(optim_params.keys())))
return optim_fn, optim_params
def get_optimizer(model, s):
"""
Parse optimizer parameters.
Input should be of the form:
- "sgd,lr=0.01"
- "adagrad,lr=0.1,lr_decay=0.05"
"""
if "," in s:
method = s[:s.find(',')]
optim_params = {}
for x in s[s.find(',') + 1:].split(','):
split = x.split('=')
assert len(split) == 2
assert re.match("^[+-]?(\d+(\.\d*)?|\.\d+)$", 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)
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
