/**
 * Simple RNN layer
 */
export default class RNNLayer extends Layer {
    /**
     * @param {object} config object
     * @param {number} config.size Size of recurrent
     * @param {string | object} [config.activation] Name of activation or activation layer object
     * @param {boolean} [config.return_sequences] Return sequences or not
     * @param {number[][] | Matrix | string} [config.w_x] Weight from input to sequence
     * @param {number[][] | Matrix | string} [config.w_h] Weight from sequence to sequence
     * @param {number[][] | Matrix | string} [config.b_x] Bias from input to sequence
     * @param {number[][] | Matrix | string} [config.b_h] Bias from sequence to sequence
     * @param {number} [config.sequence_dim] Dimension of the timesteps
     */
    constructor({ size, activation, return_sequences, w_x, w_h, b_x, b_h, sequence_dim, ...rest }: {
        size: number;
        activation?: string | object;
        return_sequences?: boolean;
        w_x?: number[][] | Matrix | string;
        w_h?: number[][] | Matrix | string;
        b_x?: number[][] | Matrix | string;
        b_h?: number[][] | Matrix | string;
        sequence_dim?: number;
    });
    _size: number;
    _unit: RNNUnitLayer;
    _return_sequences: boolean;
    _sequence_dim: 0 | 1;
    calc(x: any): any;
    _i: any[];
    _o: any[];
    grad(bo: any): any[] | Tensor;
    _grad_bptt(bo: any): any[] | Tensor;
    _bo: any[];
    update(optimizer: any): void;
    toObject(): {
        w_x: string | any[][];
        w_h: string | any[][];
        b_x: string | any[][];
        b_h: string | any[][];
        activation: import("./index.js").PlainLayerObject;
        type: string;
        size: number;
        return_sequences: boolean;
        sequence_dim: number;
    };
}
import Layer from './base.js';
declare class RNNUnitLayer extends Layer {
    constructor({ layer, size, activation, w_x, w_h, b_x, b_h, ...rest }: {
        [x: string]: any;
        layer: any;
        size: any;
        activation?: string;
        w_x?: any;
        w_h?: any;
        b_x?: any;
        b_h?: any;
    });
    _size: any;
    _w_x: Variable;
    _w_h: Variable;
    _b_x: Variable;
    _b_h: Variable;
    _z0: Matrix<number>;
    _i: any[];
    _z: any[];
    _u: any[];
    _bo: any[];
    _bh: any[];
    _activation: Layer;
    calc(x: any, k: any): any;
    grad(bo: any, k: any): any;
    _grad_bptt(bo: any, k: any): any;
    _diff_bptt(): void;
    _dw_x: Matrix<number>;
    _db_x: Matrix<number>;
    _dw_h: Matrix<number>;
    _db_h: Matrix<number>;
    update(optimizer: any): void;
    _update_bptt(optimizer: any): void;
    toObject(): {
        w_x: string | any[][];
        w_h: string | any[][];
        b_x: string | any[][];
        b_h: string | any[][];
        activation: import("./index.js").PlainLayerObject;
    };
}
import Tensor from '../../../util/tensor.js';
import Matrix from '../../../util/matrix.js';
declare class Variable {
    constructor(layer: any, value: any, sizes: any);
    _layer: any;
    _sizes: any;
    _name: string;
    _value: Matrix<any>;
    get name(): string;
    get value(): Matrix<any>;
    get sizes(): number[];
    get(...sizes: any[]): any;
    toObject(): string | any[][];
}
export {};