# oneflow.optim.SGD¶

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, contiguous_params: 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)

• contiguous_params (bool, optional) – whether to use contiguous ParamGroup which puts all parameters of the same type, device and group into the same tensor and update them together. (default: False)

• 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()


Example 2:

# Assume net is a custom model.
sgd = flow.optim.SGD(
[
{
"params": net.parameters(),
"lr": learning_rate,
}
],
)

for epoch in range(epochs):
# Read data, Compute the loss and so on.
# ...
loss.backward()
sgd.step()


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_().

__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, contiguous_params: 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

Attributes

 support_sparse Whether SGD Optimizer support sparse update.