oneflow.optim.Adagrad

class oneflow.optim.Adagrad(params: Union[Iterator[oneflow.nn.Parameter], List[Dict]], lr: float = 0.001, lr_decay: float = 0.0, weight_decay: float = 0, initial_accumulator_value: float = 0.0, eps: float = 1e-10, contiguous_params: bool = False)

Implements Adagrad Optimizer.

The formula is:

\[ \begin{align}\begin{aligned}& S_{t} = S_{t-1} + grad \odot grad\\& decay\_lr = \frac{learning\_rate}{(1 + (train\_step - 1) * lr\_decay)}\\& X_{t} = X_{t-1} - \frac{decay\_lr}{\sqrt{S_{t} + \epsilon}} \odot grad\end{aligned}\end{align} \]
Parameters
  • params (Union[Iterator[Parameter], List[Dict]]) – iterable of parameters to optimize or dicts defining

  • groups (parameter) –

  • lr (float, optional) – The learning rate. Defaults to 0.001.

  • lr_decay (float, optional) – The decay factor of learning rate. Defaults to 0.0.

  • weight_decay (float, optional) – The weight decay. Defaults to 0.

  • initial_accumulator_value (float, optional) – The initial value of S. Defaults to 0.0.

  • eps (float, optional) – A small constant terms added to the denominator to improve numerical stability. Defaults to 1e-10.

  • 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)

For example:

Example 1:

# Assume net is a custom model.
adagrad = flow.optim.Adagrad(net.parameters(), lr=1e-3)

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

Example 2:

# Assume net is a custom model.
adagrad = flow.optim.Adagrad(
    [
        {
            "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()
    adagrad.clip_grad()
    adagrad.step()
    adagrad.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_().

__init__(params: Union[Iterator[oneflow.nn.Parameter], List[Dict]], lr: float = 0.001, lr_decay: float = 0.0, weight_decay: float = 0, initial_accumulator_value: float = 0.0, eps: float = 1e-10, contiguous_params: 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, lr_decay, …])

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)

_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:

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.

zero_grad([set_to_none])

Sets the gradients of all optimized oneflow.Tensor s to zero.

Attributes

support_sparse

Whether the Optimizer support sparse update.