snorkel.slicing.SliceCombinerModule¶
-
class
snorkel.slicing.
SliceCombinerModule
(slice_ind_key='_ind_head', slice_pred_key='_pred_head', slice_pred_feat_key='_pred_transform', temperature=1.0)[source]¶ Bases:
torch.nn.modules.module.Module
A module for combining the weighted representations learned by slices.
- Intended for use with the MultitaskClassifier including:
Indicator operations
Prediction operations
Prediction transform features
NOTE: This module currently only handles binary labels.
- Parameters
slice_ind_key (
str
) – Suffix of operation corresponding to the slice indicator headsslice_pred_key (
str
) – Suffix of operation corresponding to the slice predictor headsslice_pred_feat_key (
str
) – Suffix of operation corresponding to the slice predictor features headstemperature (
float
) – Temperature constant for scaling the weighting between indicator prediction and predictor confidences: SoftMax(indicator_pred * predictor_confidence / tau)
-
__init__
(slice_ind_key='_ind_head', slice_pred_key='_pred_head', slice_pred_feat_key='_pred_transform', temperature=1.0)[source]¶ Initializes internal Module state, shared by both nn.Module and ScriptModule.
- Return type
None
Methods
__init__
([slice_ind_key, slice_pred_key, …])Initializes internal Module state, shared by both nn.Module and ScriptModule.
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.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
(output_dict)Reweight and combine predictor representations given output dict.
half
()Casts all floating point parameters and buffers to
half
datatype.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])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_parameter
(name, param)Adds a parameter to the module.
requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
share_memory
()- rtype
~T
state_dict
([destination, prefix, keep_vars])Returns a dictionary containing a whole state of the module.
to
(*args, **kwargs)Moves and/or casts the parameters and buffers.
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