netcal.regularization.ConfidencePenalty¶
- class netcal.regularization.ConfidencePenalty(weight: float = 1.0, threshold: float = -1.0, reduction='mean')¶
Confidence penalty regularization implementation for PyTorch. This penalty term can be applied to any loss function as a regularizer [1].
- Parameters:
weight (float) – Weight of entropy.
threshold (float, optional, default: None) – Entropy threshold (no penalty is assigned above threshold).
reduction ('str') – Specifies the reduction to apply to the output.
References
- __init__(weight: float = 1.0, threshold: float = -1.0, reduction='mean')¶
Constructor. For parameter description, see class docstring.
Methods
__init__
([weight, threshold, reduction])Constructor.
add_module
(name, module)Adds a child module to the current module.
apply
(fn)Applies
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Returns an iterator over module buffers.
children
()Returns an iterator over immediate children modules.
cpu
()Moves all model parameters and buffers to the CPU.
cuda
([device])Moves all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.entropy
(input, calibrated)Cross entropy between input tensor and calibrated counter part
eval
()Sets the module in evaluation mode.
extra_repr
()Set the extra representation of the module
float
()Casts all floating point parameters and buffers to
float
datatype.forward
(input, *ig_args, **ig_kwargs)Forward call.
get_buffer
(target)Returns the buffer given by
target
if it exists, otherwise throws an error.get_extra_state
()Returns any extra state to include in the module's state_dict.
get_parameter
(target)Returns the parameter given by
target
if it exists, otherwise throws an error.get_submodule
(target)Returns the submodule given by
target
if it exists, otherwise throws an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Moves all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict])Copies parameters and buffers from
state_dict
into this module and its descendants.modules
()Returns an iterator over all modules in the network.
named_buffers
([prefix, recurse])Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children
()Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse])Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Returns an iterator over module parameters.
register_backward_hook
(hook)Registers a backward hook on the module.
register_buffer
(name, tensor[, persistent])Adds a buffer to the module.
register_forward_hook
(hook)Registers a forward hook on the module.
register_forward_pre_hook
(hook)Registers a forward pre-hook on the module.
register_full_backward_hook
(hook)Registers a backward hook on the module.
register_load_state_dict_post_hook
(hook)Registers a post hook to be run after module's
load_state_dict
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Adds a parameter to the module.
requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)This function is called from
load_state_dict()
to handle any extra state found within the state_dict.share_memory
()See
torch.Tensor.share_memory_()
state_dict
(*args[, destination, prefix, ...])Returns a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Moves and/or casts the parameters and buffers.
to_empty
(*, device)Moves the parameters and buffers to the specified device without copying storage.
train
([mode])Sets the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Moves all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Sets gradients of all model parameters to zero.
Attributes
T_destination
dump_patches
epsilon