concision-macros 0.3.1

custom macros for the concision framework
Documentation 
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/*
    appellation: concision-macros <library>
    authors: @FL03
*/
//! # concision-macros
//!
//! this crate defines various procedural macros for the `concision` crate working to
//! streamline the process of creating and developing custom neural networks.
//!

pub(crate) mod ast;
pub(crate) mod attr;
pub(crate) mod impls;
pub(crate) mod kw;

use self::ast::ModelAst;
use proc_macro::TokenStream;
use syn::parse_macro_input;

#[proc_macro]
/// [`model_config!`] is a procedural macro used to define the configuration for a model in the
/// `concision` framework. It allows users to specify various parameters and settings for the model
/// in a concise and structured manner, declaring a name for their instance
pub fn model_config(input: TokenStream) -> TokenStream {
    let data = parse_macro_input! { input as ast::ConfigAst };
    // use the handler to process the input data
    let res = impls::impl_model_config(data);
    // convert the tokens into a TokenStream
    res.into()
}

#[proc_macro]
/// the [`model!`]procedural macro is used to streamline the creation of custom models using the
/// `concision` framework
pub fn model(input: TokenStream) -> TokenStream {
    let data = parse_macro_input! { input as ModelAst };
    // use the handler to process the input data
    let res = impls::impl_model(data);
    // convert the tokens into a TokenStream
    res.into()
}

#[proc_macro]
/// [`nn!`] is a procedural macro designed to streamline the process of creating new neural
/// networks;
///
/// ```ignore
/// nn! {
///     MyNeuralNetwork {
///         layout: { input: 128, output: 10 },
///         params: {
///             learning_rate: 0.01,
///             epochs: 100,
///             ...
///        },
///         layers: [Linear, ReLU, ..., Linear],
///     }     
/// }
/// ```
pub fn nn(input: TokenStream) -> TokenStream {
    let data = parse_macro_input!(input as ModelAst);
    // use the handler to process the input data
    let res = impls::impl_model(data);
    // convert the tokens into a TokenStream
    res.into()
}