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)

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 heads

• slice_pred_key (str) – Suffix of operation corresponding to the slice predictor heads

• slice_pred_feat_key (str) – Suffix of operation corresponding to the slice predictor features heads

• temperature (float) – Temperature constant for scaling the weighting between indicator prediction and predictor confidences: SoftMax(indicator_pred * predictor_confidence / tau)

slice_ind_key

See above

slice_pred_key

See above

slice_pred_feat_key

See above

__init__(slice_ind_key='_ind_head', slice_pred_key='_pred_head', slice_pred_feat_key='_pred_transform', temperature=1.0)

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