concision_neural/
lib.rs

1/*
2    Appellation: concision-neural <library>
3    Contrib: @FL03
4*/
5//! Various components, implementations, and traits for creating neural networks. The crate
6//! builds off of the [`concision_core`] crate, making extensive use of the [`ParamsBase`](cnc::ParamsBase)
7//! type to define the parameters of layers within a network.
8//!
9//! ## Overview
10//!
11//! Neural networks are a fundamental part of machine learning, and this crate provides a
12//! comprehensive set of tools to build, configure, and train neural network models. Listed
13//! below are several key components of the crate:
14//!
15//! - [`Model`]: A trait for defining a neural network model.
16//! - [`StandardModelConfig`]: A standard configuration for the models
17//! - [`ModelFeatures`]: A default implementation of the [`ModelLayout`] trait that
18//!   sufficiently defines both shallow and deep neural networks.
19//!
20//! ### _Model Parameters_
21//!
22//! Additionally, the crate defines a sequential
23//!
24//! **Note**: You should stick with the type aliases for the [`ModelParamsBase`] type, as they
25//! drastically simplify the type-face of the model parameters. Attempting to generalize over
26//! the hidden layers of the model might lead to excessive complexity. That being said, there
27//! are provided methods and routines to convert from a shallow to deep model, and vice versa.
28//!
29//! - [`DeepModelParams`]: An owned representation of the [`ModelParamsBase`] for deep
30//!   neural networks.
31//! - [`ShallowModelParams`]: An owned representation of the [`ModelParamsBase`] for shallow
32//!   neural networks.
33//!
34//! ### Traits
35//!
36//! This crate extends the [`Forward`](cnc::Forward) and [`Backward`](cnc::Backward) traits
37//! from the [`core`](cnc) crate to provide additional functionality for neural networks.
38//!
39//! - [`Predict`]: A more robust implementation of the [`Forward`] trait
40//! - [`Train`]: A trait for training a neural network model.
41//!
42#![cfg_attr(not(feature = "std"), no_std)]
43#![allow(
44    clippy::missing_saftey_doc,
45    clippy::module_inception,
46    clippy::needless_doctest_main,
47    clippy::upper_case_acronyms
48)]
49// ensure that either `std` or `alloc` feature is enabled
50#[cfg(not(any(feature = "std", feature = "alloc")))]
51compile_error! {
52    "At least one of the 'std' or 'alloc' features must be enabled."
53}
54
55extern crate concision_core as cnc;
56
57#[cfg(feature = "alloc")]
58extern crate alloc;
59
60#[doc(inline)]
61pub use self::{
62    config::prelude::*,
63    error::*,
64    layers::{Layer, LayerBase},
65    layout::prelude::*,
66    params::prelude::*,
67    train::prelude::*,
68    traits::*,
69    types::*,
70};
71
72#[macro_use]
73pub(crate) mod macros {
74    #[macro_use]
75    pub mod seal;
76}
77
78pub mod config;
79pub mod error;
80pub mod layers;
81pub mod layout;
82pub mod params;
83pub mod train;
84
85pub(crate) mod traits {
86    #[doc(inline)]
87    pub use self::prelude::*;
88
89    mod hidden;
90    mod models;
91    mod predict;
92
93    mod prelude {
94        #[doc(inline)]
95        pub use super::hidden::*;
96        #[doc(inline)]
97        pub use super::models::*;
98        #[doc(inline)]
99        pub use super::predict::*;
100    }
101}
102
103pub(crate) mod types {
104    #[doc(inline)]
105    pub use self::prelude::*;
106
107    mod dropout;
108    mod key_value;
109
110    mod prelude {
111        #[doc(inline)]
112        pub use super::dropout::*;
113        #[doc(inline)]
114        pub use super::key_value::*;
115    }
116}
117
118#[doc(hidden)]
119pub mod prelude {
120    #[doc(no_inline)]
121    pub use super::config::prelude::*;
122    #[doc(hidden)]
123    pub use crate::layers::prelude::*;
124    #[doc(no_inline)]
125    pub use crate::layout::prelude::*;
126    #[doc(no_inline)]
127    pub use crate::params::prelude::*;
128    #[doc(no_inline)]
129    pub use crate::train::prelude::*;
130    #[doc(no_inline)]
131    pub use crate::traits::*;
132    #[doc(no_inline)]
133    pub use crate::types::*;
134}
concision_neural/lib.rs

concision_neural/
lib.rs
