OptimizerIgnite {torch} | R Documentation |
Abstract Base Class for LibTorch Optimizers
Description
Abstract base class for wrapping LibTorch C++ optimizers.
Super class
torch::torch_optimizer
-> OptimizerIgnite
Methods
Public methods
Method new()
Initializes the optimizer with the specified parameters and defaults.
Usage
OptimizerIgnite$new(params, defaults)
Arguments
params
(
list()
)
Either a list of tensors or a list of parameter groups, each containing theparams
to optimizer as well as the optimizer options such as the learning rate, weight decay, etc.defaults
(
list()
)
A list of default optimizer options.
Method state_dict()
Returns the state dictionary containing the current state of the optimizer.
The returned list()
contains two lists:
-
param_groups
: The parameter groups of the optimizer (lr
, ...) as well as to which parameters they are applied (params
, integer indices) -
state
: The states of the optimizer. The names are the indices of the parameters to which they belong, converted to character.
Usage
OptimizerIgnite$state_dict()
Returns
(list()
)
Method load_state_dict()
Loads the state dictionary into the optimizer.
Usage
OptimizerIgnite$load_state_dict(state_dict)
Arguments
state_dict
(
list()
)
The state dictionary to load into the optimizer.
Method step()
Performs a single optimization step.
Usage
OptimizerIgnite$step(closure = NULL)
Arguments
closure
(
function()
)
A closure that conducts the forward pass and returns the loss.
Returns
(numeric()
)
The loss.
Method zero_grad()
Zeros out the gradients of the parameters.
Usage
OptimizerIgnite$zero_grad()
Method add_param_group()
Adds a new parameter group to the optimizer.
Usage
OptimizerIgnite$add_param_group(param_group)
Arguments
param_group
(
list()
)
A parameter group to add to the optimizer. This should contain theparams
to optimize as well as the optimizer options. For all options that are not specified, the defaults are used.
Method clone()
The objects of this class are cloneable with this method.
Usage
OptimizerIgnite$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.