catbrain
Version:
GPU accelerated neural networks made simple for Javascript
19 lines (18 loc) • 1.42 kB
TypeScript
export declare class Activation {
static sigmoid(x: number, reluClip?: number, leakyReluAlpha?: number): number;
static sigmoidDerivative(x: number, reluClip?: number, leakyReluAlpha?: number): number;
static tanh(x: number, reluClip?: number, leakyReluAlpha?: number): number;
static tanhDerivative(x: number, reluClip?: number, leakyReluAlpha?: number): number;
static relu(x: number, reluClip: number, leakyReluAlpha?: number): number;
static reluDerivative(x: number, reluClip: number, leakyReluAlpha?: number): number;
static leakyRelu(x: number, reluClip: number, leakyReluAlpha: number): number;
static leakyReluDerivative(x: number, reluClip: number, leakyReluAlpha: number): number;
static swish(x: number, reluClip: number, leakyReluAlpha?: number): number;
static swishDerivative(x: number, reluClip: number, leakyReluAlpha?: number): number;
static softplus(x: number, reluClip: number, leakyReluAlpha?: number): number;
static softplusDerivative(x: number, reluClip: number, leakyReluAlpha?: number): number;
static mish(x: number, reluClip: number, leakyReluAlpha?: number): number;
static mishDerivative(x: number, reluClip: number, leakyReluAlpha?: number): number;
static linear(x: number, reluClip?: number, leakyReluAlpha?: number): number;
static linearDerivative(x?: number, reluClip?: number, leakyReluAlpha?: number): number;
}