本文整理汇总了Python中torch.optim.set_parameters方法的典型用法代码示例。如果您正苦于以下问题：Python optim.set_parameters方法的具体用法？Python optim.set_parameters怎么用？Python optim.set_parameters使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.optim的用法示例。
在下文中一共展示了optim.set_parameters方法的13个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: set_parameters
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def set_parameters(self, params):
""" ? """
self.params = []
self.sparse_params = []
for k, p in params:
if p.requires_grad:
if self.method != 'sparseadam' or "embed" not in k:
self.params.append(p)
else:
self.sparse_params.append(p)
if self.method == 'sgd':
self.optimizer = optim.SGD(self.params, lr=self.learning_rate)
elif self.method == 'adagrad':
self.optimizer = optim.Adagrad(self.params, lr=self.learning_rate)
for group in self.optimizer.param_groups:
for p in group['params']:
self.optimizer.state[p]['sum'] = self.optimizer\
.state[p]['sum'].fill_(self.adagrad_accum)
elif self.method == 'adadelta':
self.optimizer = optim.Adadelta(self.params, lr=self.learning_rate)
elif self.method == 'adam':
self.optimizer = optim.Adam(self.params, lr=self.learning_rate,
betas=self.betas, eps=1e-9)
else:
raise RuntimeError("Invalid optim method: " + self.method) 示例2: build_optim
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def build_optim(opt, model):
""" Build optimizer """
optim = Optimizer(
opt.optim, opt.learning_rate, opt.max_grad_norm,
lr_decay=opt.learning_rate_decay,
start_decay_steps=opt.start_decay_steps,
decay_steps=opt.decay_steps,
beta1=opt.adam_beta1,
beta2=opt.adam_beta2,
adagrad_accum=opt.adagrad_accumulator_init,
decay_method=opt.decay_method,
warmup_steps=opt.warmup_steps,
model_size=opt.encoder_size
)
parameters = [[n, p] for n, p in model.named_parameters() if p.requires_grad]
optim.set_parameters(parameters)
return optim 示例3: create_optimizer
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def create_optimizer(model_or_iterable, options=None):
if options is None: options = copy.deepcopy(onmt.standard_options.stdOptions)
if not isinstance(options, dict):
options = mhf.convertToDictionary(options)
options = handle_options(options)
options = mhf.convertToNamedTuple(options)
optim = onmt.Optim(
options.optim, options.learning_rate, options.max_grad_norm,
lr_decay=options.learning_rate_decay,
start_decay_at=options.start_decay_at,
opt=options)
try:
optim.set_parameters(model_or_iterable.parameters())
except AttributeError:
optim.set_parameters(model_or_iterable)
return optim 示例4: set_parameters
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def set_parameters(self, model):
""" ? """
params = [p for p in model.parameters() if p.requires_grad]
if self.method == 'sgd':
self.optimizer = optim.SGD(params, lr=self.learning_rate)
elif self.method == 'adagrad':
self.optimizer = optim.Adagrad(
self.params,
lr=self.learning_rate,
initial_accumulator_value=self.adagrad_accum)
elif self.method == 'adadelta':
self.optimizer = optim.Adadelta(params, lr=self.learning_rate)
elif self.method == 'adafactor':
self.optimizer = AdaFactor(params, non_constant_decay=True,
enable_factorization=True,
weight_decay=0)
elif self.method == 'adam':
self.optimizer = optim.Adam(params, lr=self.learning_rate,
betas=self.betas, eps=1e-9)
elif self.method == 'sparseadam':
dense = []
sparse = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue
# TODO: Find a better way to check for sparse gradients.
if 'embed' in name:
sparse.append(param)
else:
dense.append(param)
self.optimizer = MultipleOptimizer(
[optim.Adam(dense, lr=self.learning_rate,
betas=self.betas, eps=1e-8),
optim.SparseAdam(sparse, lr=self.learning_rate,
betas=self.betas, eps=1e-8)])
else:
raise RuntimeError("Invalid optim method: " + self.method) 示例5: build_optim
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def build_optim(model, opt, checkpoint):
""" Build optimizer """
saved_optimizer_state_dict = None
if opt.train_from:
optim = checkpoint['optim']
# We need to save a copy of optim.optimizer.state_dict() for setting
# the, optimizer state later on in Stage 2 in this method, since
# the method optim.set_parameters(model.parameters()) will overwrite
# optim.optimizer, and with ith the values stored in
# optim.optimizer.state_dict()
saved_optimizer_state_dict = optim.optimizer.state_dict()
else:
optim = Optimizer(
opt.optim, opt.learning_rate, opt.max_grad_norm,
lr_decay=opt.learning_rate_decay,
start_decay_steps=opt.start_decay_steps,
decay_steps=opt.decay_steps,
beta1=opt.adam_beta1,
beta2=opt.adam_beta2,
adagrad_accum=opt.adagrad_accumulator_init,
decay_method=opt.decay_method,
warmup_steps=opt.warmup_steps)
optim.set_parameters(model.named_parameters())
if opt.train_from:
optim.optimizer.load_state_dict(saved_optimizer_state_dict)
if use_gpu(opt):
for state in optim.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
if (optim.method == 'adam') and (len(optim.optimizer.state) < 1):
raise RuntimeError(
"Error: loaded Adam optimizer from existing model" +
" but optimizer state is empty")
return optim 示例6: set_parameters
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def set_parameters(self, params):
""" ? """
self.params = []
self.sparse_params = []
for k, p in params:
if p.requires_grad:
if self.method != 'sparseadam' or "embed" not in k:
self.params.append(p)
else:
self.sparse_params.append(p)
if self.method == 'sgd':
self.optimizer = optim.SGD(self.params, lr=self.learning_rate)
elif self.method == 'adagrad':
self.optimizer = optim.Adagrad(self.params, lr=self.learning_rate)
for group in self.optimizer.param_groups:
for p in group['params']:
self.optimizer.state[p]['sum'] = self.optimizer\
.state[p]['sum'].fill_(self.adagrad_accum)
elif self.method == 'adadelta':
self.optimizer = optim.Adadelta(self.params, lr=self.learning_rate)
elif self.method == 'adam':
self.optimizer = optim.Adam(self.params, lr=self.learning_rate,
betas=self.betas, eps=1e-8, weight_decay=3e-9)
elif self.method == 'sparseadam':
self.optimizer = MultipleOptimizer(
[optim.Adam(self.params, lr=self.learning_rate,
betas=self.betas, eps=1e-8),
optim.SparseAdam(self.sparse_params, lr=self.learning_rate,
betas=self.betas, eps=1e-8)])
else:
raise RuntimeError("Invalid optim method: " + self.method) 示例7: set_state
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def set_state(self, state_dict):
"""
If you want to load the checkpoint of an optimizer, call this function after set_parameters.
Because the method optim.set_parameters(model.parameters()) will overwrite optim.optimizer,
and with ith the values stored in optim.optimizer.state_dict()
"""
self.optimizer.load_state_dict(state_dict)
# Convert back the state values to cuda type if applicable
for state in self.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(self.device) 示例8: set_parameters
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def set_parameters(self, params):
""" ? """
self.params = []
self.sparse_params = []
for k, p in params:
if p.requires_grad:
if self.method != 'sparseadam' or "embed" not in k:
self.params.append(p)
else:
self.sparse_params.append(p)
if self.method == 'sgd':
self.optimizer = optim.SGD(self.params, lr=self.learning_rate)
elif self.method == 'adagrad':
self.optimizer = optim.Adagrad(self.params, lr=self.learning_rate)
for group in self.optimizer.param_groups:
for p in group['params']:
self.optimizer.state[p]['sum'] = self.optimizer\
.state[p]['sum'].fill_(self.adagrad_accum)
elif self.method == 'adadelta':
self.optimizer = optim.Adadelta(self.params, lr=self.learning_rate)
elif self.method == 'adam':
self.optimizer = optim.Adam(self.params, lr=self.learning_rate,
betas=self.betas, eps=1e-9)
elif self.method == 'sparseadam':
self.optimizer = MultipleOptimizer(
[optim.Adam(self.params, lr=self.learning_rate,
betas=self.betas, eps=1e-8),
optim.SparseAdam(self.sparse_params, lr=self.learning_rate,
betas=self.betas, eps=1e-8)])
else:
raise RuntimeError("Invalid optim method: " + self.method) 示例9: set_parameters
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def set_parameters(self, params):
""" ? """
self.params = []
self.sparse_params = []
for k, p in params:
if p.requires_grad:
if self.method != "sparseadam" or "embed" not in k:
self.params.append(p)
else:
self.sparse_params.append(p)
if self.method == "sgd":
self.optimizer = optim.SGD(self.params, lr=self.learning_rate)
elif self.method == "adagrad":
self.optimizer = optim.Adagrad(self.params, lr=self.learning_rate)
for group in self.optimizer.param_groups:
for p in group["params"]:
self.optimizer.state[p]["sum"] = self.optimizer.state[p][
"sum"
].fill_(self.adagrad_accum)
elif self.method == "adadelta":
self.optimizer = optim.Adadelta(self.params, lr=self.learning_rate)
elif self.method == "adam":
self.optimizer = optim.Adam(
self.params, lr=self.learning_rate, betas=self.betas, eps=1e-9
)
else:
raise RuntimeError("Invalid optim method: " + self.method)
self.param_groups = self.optimizer.param_groups
self.state = self.optimizer.state 示例10: build_optim
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def build_optim(model, opt, checkpoint):
""" Build optimizer """
saved_optimizer_state_dict = None
if opt.train_from:
optim = checkpoint['optim']
# We need to save a copy of optim.optimizer.state_dict() for setting
# the, optimizer state later on in Stage 2 in this method, since
# the method optim.set_parameters(model.parameters()) will overwrite
# optim.optimizer, and with ith the values stored in
# optim.optimizer.state_dict()
saved_optimizer_state_dict = optim.optimizer.state_dict()
else:
optim = Optimizer(
opt.optim, opt.learning_rate, opt.max_grad_norm,
lr_decay=opt.learning_rate_decay,
start_decay_steps=opt.start_decay_steps,
decay_steps=opt.decay_steps,
beta1=opt.adam_beta1,
beta2=opt.adam_beta2,
adagrad_accum=opt.adagrad_accumulator_init,
decay_method=opt.decay_method,
warmup_steps=opt.warmup_steps)
# Stage 1:
# Essentially optim.set_parameters (re-)creates and optimizer using
# model.paramters() as parameters that will be stored in the
# optim.optimizer.param_groups field of the torch optimizer class.
# Importantly, this method does not yet load the optimizer state, as
# essentially it builds a new optimizer with empty optimizer state and
# parameters from the model.
optim.set_parameters(model.named_parameters())
if opt.train_from:
# Stage 2: In this stage, which is only performed when loading an
# optimizer from a checkpoint, we load the saved_optimizer_state_dict
# into the re-created optimizer, to set the optim.optimizer.state
# field, which was previously empty. For this, we use the optimizer
# state saved in the "saved_optimizer_state_dict" variable for
# this purpose.
# See also: https://github.com/pytorch/pytorch/issues/2830
optim.optimizer.load_state_dict(saved_optimizer_state_dict)
# Convert back the state values to cuda type if applicable
if use_gpu(opt):
for state in optim.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
# We want to make sure that indeed we have a non-empty optimizer state
# when we loaded an existing model. This should be at least the case
# for Adam, which saves "exp_avg" and "exp_avg_sq" state
# (Exponential moving average of gradient and squared gradient values)
if (optim.method == 'adam') and (len(optim.optimizer.state) < 1):
raise RuntimeError(
"Error: loaded Adam optimizer from existing model" +
" but optimizer state is empty")
return optim 示例11: build_optim
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def build_optim(model, opt, checkpoint):
""" Build optimizer """
saved_optimizer_state_dict = None
optim = Optimizer(
opt.optim, opt.learning_rate, opt.max_grad_norm,
lr_decay=opt.learning_rate_decay,
start_decay_steps=opt.start_decay_steps,
decay_steps=opt.decay_steps,
beta1=opt.adam_beta1,
beta2=opt.adam_beta2,
adagrad_accum=opt.adagrad_accumulator_init,
decay_method=opt.decay_method,
warmup_steps=opt.warmup_steps,
model_size=opt.rnn_size)
if opt.train_from:
# optim = checkpoint['optim']
# We need to save a copy of optim.optimizer.state_dict() for setting
# the, optimizer state later on in Stage 2 in this method, since
# the method optim.set_parameters(model.parameters()) will overwrite
# optim.optimizer, and with ith the values stored in
# optim.optimizer.state_dict()
saved_optimizer_state_dict = checkpoint['optim']
# Stage 1:
# Essentially optim.set_parameters (re-)creates and optimizer using
# model.paramters() as parameters that will be stored in the
# optim.optimizer.param_groups field of the torch optimizer class.
# Importantly, this method does not yet load the optimizer state, as
# essentially it builds a new optimizer with empty optimizer state and
# parameters from the model.
optim.set_parameters(model.named_parameters())
if opt.train_from:
# Stage 2: In this stage, which is only performed when loading an
# optimizer from a checkpoint, we load the saved_optimizer_state_dict
# into the re-created optimizer, to set the optim.optimizer.state
# field, which was previously empty. For this, we use the optimizer
# state saved in the "saved_optimizer_state_dict" variable for
# this purpose.
# See also: https://github.com/pytorch/pytorch/issues/2830
optim.optimizer.load_state_dict(saved_optimizer_state_dict)
# Convert back the state values to cuda type if applicable
if use_gpu(opt):
for state in optim.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
# We want to make sure that indeed we have a non-empty optimizer state
# when we loaded an existing model. This should be at least the case
# for Adam, which saves "exp_avg" and "exp_avg_sq" state
# (Exponential moving average of gradient and squared gradient values)
if (optim.method == 'adam') and (len(optim.optimizer.state) < 1):
raise RuntimeError(
"Error: loaded Adam optimizer from existing model" +
" but optimizer state is empty")
return optim 示例12: build_optim
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def build_optim(model, opt, checkpoint):
""" Build optimizer """
saved_optimizer_state_dict = None
if opt.train_from:
optim = checkpoint['optim']
# We need to save a copy of optim.optimizer.state_dict() for setting
# the, optimizer state later on in Stage 2 in this method, since
# the method optim.set_parameters(model.parameters()) will overwrite
# optim.optimizer, and with ith the values stored in
# optim.optimizer.state_dict()
saved_optimizer_state_dict = optim.optimizer.state_dict()
else:
optim = Optimizer(
opt.optim, opt.learning_rate, opt.max_grad_norm,
lr_decay=opt.learning_rate_decay,
start_decay_steps=opt.start_decay_steps,
decay_steps=opt.decay_steps,
beta1=opt.adam_beta1,
beta2=opt.adam_beta2,
adagrad_accum=opt.adagrad_accumulator_init,
decay_method=opt.decay_method,
warmup_steps=opt.warmup_steps,
model_size=opt.rnn_size)
# Stage 1:
# Essentially optim.set_parameters (re-)creates and optimizer using
# model.paramters() as parameters that will be stored in the
# optim.optimizer.param_groups field of the torch optimizer class.
# Importantly, this method does not yet load the optimizer state, as
# essentially it builds a new optimizer with empty optimizer state and
# parameters from the model.
optim.set_parameters(model.named_parameters())
if opt.train_from:
# Stage 2: In this stage, which is only performed when loading an
# optimizer from a checkpoint, we load the saved_optimizer_state_dict
# into the re-created optimizer, to set the optim.optimizer.state
# field, which was previously empty. For this, we use the optimizer
# state saved in the "saved_optimizer_state_dict" variable for
# this purpose.
# See also: https://github.com/pytorch/pytorch/issues/2830
optim.optimizer.load_state_dict(saved_optimizer_state_dict)
# Convert back the state values to cuda type if applicable
if use_gpu(opt):
for state in optim.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
# We want to make sure that indeed we have a non-empty optimizer state
# when we loaded an existing model. This should be at least the case
# for Adam, which saves "exp_avg" and "exp_avg_sq" state
# (Exponential moving average of gradient and squared gradient values)
if (optim.method == 'adam') and (len(optim.optimizer.state) < 1):
raise RuntimeError(
"Error: loaded Adam optimizer from existing model" +
" but optimizer state is empty")
return optim 示例13: build_optim
# 需要导入模块: from torch import optim [as 别名]
# 或者: from torch.optim import set_parameters [as 别名]
def build_optim(model, opt, checkpoint):
""" Build optimizer """
saved_optimizer_state_dict = None
if opt.train_from:
optim = checkpoint["optim"]
# We need to save a copy of optim.optimizer.state_dict() for setting
# the, optimizer state later on in Stage 2 in this method, since
# the method optim.set_parameters(model.parameters()) will overwrite
# optim.optimizer, and with ith the values stored in
# optim.optimizer.state_dict()
# saved_optimizer_state_dict = optim.optimizer.state_dict()
saved_optimizer_state_dict = optim
else:
optim = Optimizer(
opt.optim,
opt.learning_rate,
opt.max_grad_norm,
lr_decay=opt.learning_rate_decay,
start_decay_steps=opt.start_decay_steps,
decay_steps=opt.decay_steps,
beta1=opt.adam_beta1,
beta2=opt.adam_beta2,
adagrad_accum=opt.adagrad_accumulator_init,
decay_method=opt.decay_method,
warmup_steps=opt.warmup_steps,
)
optim.set_parameters(model.named_parameters())
if opt.train_from:
optim.optimizer.load_state_dict(saved_optimizer_state_dict)
if use_gpu(opt):
for state in optim.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
if (optim.method == "adam") and (len(optim.optimizer.state) < 1):
raise RuntimeError(
"Error: loaded Adam optimizer from existing model"
+ " but optimizer state is empty"
)
return optim