@hoff97/tensor-js/dist/lib/ops/cpu/basic.d.ts

PyTorch like deep learning inferrence library

import { CPUTensor } from '../../tensor/cpu/tensor';
import { DType } from '../../types';
declare type UnaryOperator = (o: number) => number;
declare type BinaryOperator = (o1: number, o2: number) => number;
export declare function positionWiseUnaryOp<DTpe extends DType>(a: CPUTensor<DTpe>, op: UnaryOperator): CPUTensor<DTpe>;
export declare function positionWiseBinaryOp<DTpe extends DType>(a: CPUTensor<DTpe>, b: CPUTensor<DTpe>, op: BinaryOperator, resultShape: readonly number[]): CPUTensor<DTpe>;
export declare function exp<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function log<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function sqrt<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function abs<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function sin<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function cos<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function tan<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function asin<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function acos<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function atan<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function sinh<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function cosh<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function tanh<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function asinh<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function acosh<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function atanh<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function floor<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function ceil<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function round<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function sign<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function negate<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function powerScalar<DTpe extends DType>(a: CPUTensor<DTpe>, power: number, factor: number): CPUTensor<DTpe>;
export declare function addMultiplyScalar<DTpe extends DType>(a: CPUTensor<DTpe>, factor: number, add: number): CPUTensor<DTpe>;
export declare function sigmoid<DTpe extends DType>(a: CPUTensor<DTpe>): CPUTensor<DTpe>;
export declare function hardSigmoid<DTpe extends DType>(a: CPUTensor<DTpe>, alpha: number, beta: number): CPUTensor<DTpe>;
export declare function clip<DTpe extends DType>(a: CPUTensor<DTpe>, min?: number, max?: number): CPUTensor<DTpe>;
export declare function add<DTpe extends DType>(a: CPUTensor<DTpe>, b: CPUTensor<DTpe>, resultShape: readonly number[], alpha: number, beta: number): CPUTensor<DTpe>;
export declare function subtract<DTpe extends DType>(a: CPUTensor<DTpe>, b: CPUTensor<DTpe>, resultShape: readonly number[], alpha: number, beta: number): CPUTensor<DTpe>;
export declare function multiply<DTpe extends DType>(a: CPUTensor<DTpe>, b: CPUTensor<DTpe>, resultShape: readonly number[], alpha: number): CPUTensor<DTpe>;
export declare function divide<DTpe extends DType>(a: CPUTensor<DTpe>, b: CPUTensor<DTpe>, resultShape: readonly number[], alpha: number): CPUTensor<DTpe>;
export declare function power<DTpe extends DType>(a: CPUTensor<DTpe>, b: CPUTensor<DTpe>, resultShape: readonly number[]): CPUTensor<DTpe>;
export declare function clipBackward<DTpe extends DType>(value: CPUTensor<DTpe>, grad: CPUTensor<DTpe>, resultShape: readonly number[], min?: number, max?: number): CPUTensor<DTpe>;
export {};