@am/neuralnetwork@1.0.0
Built and signed on GitHub Actions
Built and signed on GitHub Actions

latest

A simple neural network library for JavaScript and TypeScript.

Published

4 weeks ago (1.0.0)

class Activation

implements Layer

Abstract base class for activation function layers. All activation layers must implement the forward and backward methods.

Methods

abstract



backward(outputGradient: number[][]): number[][]

Performs the backward pass of the activation function. Calculates the gradient of the loss with respect to the input of this layer.

abstract



forward(input: number[][]): number[][]

Performs the forward pass of the activation function.



getConfig(): Record<string, unknown>

Returns the type of this layer.



getName(): string

Returns the type of the layer.

Add Package

deno add jsr:@am/neuralnetwork

Import symbol

import { Activation } from "@am/neuralnetwork/layers";

Import directly with a jsr specifier

import { Activation } from "jsr:@am/neuralnetwork/layers";

Add Package

pnpm i jsr:@am/neuralnetwork

or (using pnpm 10.8 or older)

pnpm dlx jsr add @am/neuralnetwork

Import symbol

import { Activation } from "@am/neuralnetwork/layers";

Add Package

yarn add jsr:@am/neuralnetwork

or (using Yarn 4.8 or older)

yarn dlx jsr add @am/neuralnetwork

Import symbol

import { Activation } from "@am/neuralnetwork/layers";

Add Package

vlt install jsr:@am/neuralnetwork

Import symbol

import { Activation } from "@am/neuralnetwork/layers";

Add Package

npx jsr add @am/neuralnetwork

Import symbol

import { Activation } from "@am/neuralnetwork/layers";

Add Package

bunx jsr add @am/neuralnetwork

Import symbol

import { Activation } from "@am/neuralnetwork/layers";