oneflow.optim.SGD¶
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class
oneflow.optim.
SGD
(params: Union[Iterator[oneflow.nn.Parameter], List[Dict]], lr: float = 0.001, momentum: float = 0.0, dampening: float = 0.0, weight_decay: float = 0.0, nesterov: bool = False, maximize: bool = False, fused: bool = False)¶ Implements SGD algorithm.
This algorithm takes a random sample’s gradient as an approximate estimate of the overall gradient in small batch gradient descent.
When the momentum = 0, the equation of parameters updating is:
\[param_{new} = param_{old} - learning\_rate * grad\]With momentum, the equation of parameters updating is:
\[ \begin{align}\begin{aligned}& V_t = \beta * V_{t-1} - learning\_rate * (g_t + param_{old} * weight\_decay)\\& param_{new} = param_{old} + V_t\end{aligned}\end{align} \]- Parameters
params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional) – learning rate (default: 1e-3)
momentum (float, optional) – Momentum factor (default: 0.0)
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0.0)
fused (bool, optional) – whether to divide all the parameters into several groups, then update each group of parameters with the fused kernel. (default: False)
For example:
Example 1:
# Assume net is a custom model. sgd = flow.optim.SGD(net.parameters(), lr=1e-3) for epoch in range(epochs): # Read data, Compute the loss and so on. # ... loss.backward() sgd.step() sgd.zero_grad()
Example 2:
# Assume net is a custom model. sgd = flow.optim.SGD( [ { "params": net.parameters(), "lr": learning_rate, "clip_grad_max_norm": 0.5, "clip_grad_norm_type": 2.0, } ], ) for epoch in range(epochs): # Read data, Compute the loss and so on. # ... loss.backward() sgd.clip_grad() sgd.step() sgd.zero_grad()
If you want to use clip_grad, you can refer this example.
For more details of clip_grad_max_norm and clip_grad_norm_type, you can refer to
oneflow.nn.utils.clip_grad_norm_()
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__init__
(params: Union[Iterator[oneflow.nn.Parameter], List[Dict]], lr: float = 0.001, momentum: float = 0.0, dampening: float = 0.0, weight_decay: float = 0.0, nesterov: bool = False, maximize: bool = False, fused: bool = False)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__delattr__
(name, /)Implement delattr(self, name).
__dir__
()Default dir() implementation.
__eq__
(value, /)Return self==value.
__format__
(format_spec, /)Default object formatter.
__ge__
(value, /)Return self>=value.
__getattribute__
(name, /)Return getattr(self, name).
__gt__
(value, /)Return self>value.
__hash__
()Return hash(self).
__init__
(params[, lr, momentum, dampening, …])Initialize self.
__init_subclass__
This method is called when a class is subclassed.
__le__
(value, /)Return self<=value.
__lt__
(value, /)Return self<value.
__ne__
(value, /)Return self!=value.
__new__
(**kwargs)Create and return a new object.
__reduce__
()Helper for pickle.
__reduce_ex__
(protocol, /)Helper for pickle.
__repr__
()Return repr(self).
__setattr__
(name, value, /)Implement setattr(self, name, value).
__sizeof__
()Size of object in memory, in bytes.
__str__
()Return str(self).
__subclasshook__
Abstract classes can override this to customize issubclass().
_check_variables_in_graph
(vars_conf)_check_variables_optimizer_bound
(vars_conf)_fused_update
(param_group)_generate_conf_for_graph
(train_conf, vars_conf)_generate_grad_clip_conf_for_optim_conf
(…)_generate_indexed_slices_optimizer_conf
(…)_generate_lr_scale_for_optim_conf
(…)_parse_input_parameters
(parameters)Supports such parameters:
_single_tensor_update
(param_group)add_param_group
(param_group)Add a param group to the
Optimizer
s param_groups.clip_grad
([error_if_nonfinite])Clips gradient norm of an iterable of parameters.
load_state_dict
(state_dict)Load the state of the optimizer which is created by state_dict function.
state_dict
()Returns the state of the optimizer as a
dict
.step
([closure])Performs a single optimization step. :param closure: A closure that reevaluates the model and returns the loss. :type closure: callable, optional.
zero_grad
([set_to_none])Sets the gradients of all optimized
oneflow.Tensor
s to zero.Attributes
support_sparse
Whether SGD Optimizer support sparse update.