mlr_pipeops_torch_optimizer {mlr3torch} | R Documentation |
Optimizer Configuration
Description
Configures the optimizer of a deep learning model.
Parameters
The parameters are defined dynamically from the optimizer that is set during construction.
Input and Output Channels
There is one input channel "input"
and one output channel "output"
.
During training, the channels are of class ModelDescriptor
.
During prediction, the channels are of class Task
.
State
The state is the value calculated by the public method shapes_out()
.
Internals
During training, the optimizer is cloned and added to the ModelDescriptor
.
Note that the parameter set of the stored TorchOptimizer
is reference-identical to the parameter set of the
pipeop itself.
Super class
mlr3pipelines::PipeOp
-> PipeOpTorchOptimizer
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
PipeOpTorchOptimizer$new( optimizer = t_opt("adam"), id = "torch_optimizer", param_vals = list() )
Arguments
optimizer
(
TorchOptimizer
orcharacter(1)
ortorch_optimizer_generator
)
The optimizer (or something convertible viaas_torch_optimizer()
).id
(
character(1)
)
Identifier of the resulting object.param_vals
(
list()
)
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.
Method clone()
The objects of this class are cloneable with this method.
Usage
PipeOpTorchOptimizer$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
See Also
Other PipeOp:
mlr_pipeops_module
,
mlr_pipeops_torch_callbacks
Other Model Configuration:
ModelDescriptor()
,
mlr_pipeops_torch_callbacks
,
mlr_pipeops_torch_loss
,
model_descriptor_union()
Examples
po_opt = po("torch_optimizer", "sgd", lr = 0.01)
po_opt$param_set
mdin = po("torch_ingress_num")$train(list(tsk("iris")))
mdin[[1L]]$optimizer
mdout = po_opt$train(mdin)
mdout[[1L]]$optimizer