Module module

Expand description

Module for the neural network module.

Structs§

Attribute: Attribute to print in the display method.
ConstantRecord: Record used for constant type implementing the module trait.
Content: Struct to store the attributes of a module for formatting.
DisplaySettings: Custom module display settings.
Ignored: Container to satisfy the Module trait for types that are not modules.
Param: Parameters are the fundamental building blocks of modules where they serve as containers for tensors that can be updated during training, and loaded during inference. If you don’t want to save the tensors and/or don’t want to update it during training, you don’t need this type to wrap your tensor.
ParamId: Parameter ID.
ParamMapper: Applies transformations when loading and saving parameters.
Quantizer: Describes how to quantize a module.
Reinitializer: Overrides float and int tensors of burn modules.
RunningState: A state that can be updated during the forward pass while being thread safe.

Initializer: Enum specifying with what values a tensor should be initialized

AutodiffModule: Module with auto-differentiation backend.
Module: Trait for all neural network modules.
ModuleDisplay: Trait to implement custom display settings for a module.
ModuleDisplayDefault: Default display settings for a module.
ModuleMapper: Module mapper trait for transforming module parameters.
ModuleVisitor: Module visitor trait for traversing and inspecting module parameters.
Parameter: Trait that defines what is necessary for a type to be a parameter.

Devices: Type alias to Vec<B::Device> which supports no_std environments, but automatically using the alloc crate.