mirror of
https://github.com/wassname/keras-js.git
synced 2026-09-20 12:50:17 +08:00
implement activation functions with tests
This commit is contained in:
Leon Chen
@@ -31,6 +31,7 @@
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},
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"homepage": "https://github.com/transcranial/keras-js#readme",
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"dependencies": {
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"cwise": "^1.0.9",
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"ndarray": "^1.0.18",
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"ndarray-ops": "^1.2.2"
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},
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+84
-6
@@ -1,5 +1,6 @@
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import ndarray from 'ndarray'
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import ops from 'ndarray-ops'
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import cwise from 'cwise'
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/**
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* Softmax activation function. In-place operation.
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@@ -23,12 +24,38 @@ export function softmax (x) {
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return this
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}
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export function softplus (x) {
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const _softplus = cwise({
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args: ['array'],
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body: function (_x) {
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_x = Math.log(Math.exp(_x) + 1)
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}
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})
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/**
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* Softplus activation function. In-place operation.
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* @param {Tensor} x
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* @returns {Tensor} `this`
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*/
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export function softplus (x) {
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_softplus(x.tensor)
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return this
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}
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export function softsign (x) {
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const _softsign = cwise({
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args: ['array'],
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body: function (_x) {
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_x /= 1 + Math.abs(_x)
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}
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})
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/**
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* Softsign activation function. In-place operation.
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* @param {Tensor} x
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* @returns {Tensor} `this`
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*/
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export function softsign (x) {
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_softsign(x.tensor)
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return this
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}
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/**
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@@ -56,18 +83,69 @@ export function relu (x, opts = {}) {
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return this
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}
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const _tanh = cwise({
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args: ['array'],
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body: function (_x) {
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_x = Math.tanh(_x)
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}
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})
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/**
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* Tanh activation function. In-place operation.
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* @param {Tensor} x
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* @returns {Tensor} `this`
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*/
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export function tanh (x) {
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_tanh(x.tensor)
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return this
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}
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const _sigmoid = cwise({
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args: ['array'],
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body: function (_x) {
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_x = 1 / (1 + Math.exp(-_x))
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}
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})
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/**
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* Sigmoid activation function. In-place operation.
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* @param {Tensor} x
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* @returns {Tensor} `this`
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*/
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export function sigmoid (x) {
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_sigmoid(x.tensor)
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return this
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}
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// Reference hard sigmoid with slope and shift values from theano, see
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// https://github.com/Theano/Theano/blob/master/theano/tensor/nnet/sigm.py
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const _hardSigmoid = cwise({
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args: ['array'],
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body: function (_x) {
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_x = (_x * 0.2) + 0.5
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if (_x <= 0) {
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_x = 0
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} else if (_x >= 1) {
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_x = 1
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}
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}
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})
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/**
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* Hard-sigmoid activation function. In-place operation.
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* @param {Tensor} x
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* @returns {Tensor} `this`
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*/
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export function hardSigmoid (x) {
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_hardSigmoid(x.tensor)
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return this
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}
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/**
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* Linear activation function. In-place operation.
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* @param {Tensor} x
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* @returns {Tensor} `this`
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*/
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export function linear (x) {
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return x
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return this
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}
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@@ -8,6 +8,10 @@ const approxEquals = testUtils.approxEquals
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const logTime = testUtils.logTime
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describe('activations', function () {
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/*********************************************************
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* softmax
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*********************************************************/
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describe('softmax', function () {
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it('should work for 1D tensor', function () {
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console.log('\n%cactivations', styles.h1)
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@@ -46,6 +50,126 @@ describe('activations', function () {
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})
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})
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/*********************************************************
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* softplus
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*********************************************************/
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describe('softplus', function () {
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it('should work for 1D tensor', function () {
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console.log('\n%csoftplus', styles.h2)
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console.log('\n%c1D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.softplus(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928])
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const shapeExpected = [6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 2D tensor', function () {
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console.log('\n%c2D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.softplus(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928, 0.67826, 0.854355, 0.693147, 1.171101, 0.554355, 1.313262])
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const shapeExpected = [2, 6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 3D tensor', function () {
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console.log('\n%c3D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.softplus(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.693147, 0.798139, 0.474077, 0.644397, 1.313262, 2.126928, 0.67826, 2.395545, 0.693147, 1.171101, 0.554355, 1.313262])
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const shapeExpected = [2, 2, 3]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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})
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/*********************************************************
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* softsign
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*********************************************************/
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describe('softsign', function () {
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it('should work for 1D tensor', function () {
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console.log('\n%csoftsign', styles.h2)
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console.log('\n%c1D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.softsign(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667])
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const shapeExpected = [6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 2D tensor', function () {
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console.log('\n%c2D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.softsign(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.230769, 0.0, 0.444444, -0.230769, 0.5])
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const shapeExpected = [2, 6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 3D tensor', function () {
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console.log('\n%c3D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.softsign(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.0, 0.166667, -0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.69697, 0.0, 0.444444, -0.230769, 0.5])
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const shapeExpected = [2, 2, 3]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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})
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/*********************************************************
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* relu
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*********************************************************/
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describe('relu', function () {
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it('should work for 1D tensor', function () {
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console.log('\n%crelu', styles.h2)
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@@ -133,4 +257,236 @@ describe('activations', function () {
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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})
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/*********************************************************
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* tanh
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*********************************************************/
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describe('tanh', function () {
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it('should work for 1D tensor', function () {
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console.log('\n%ctanh', styles.h2)
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console.log('\n%c1D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.tanh(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028])
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const shapeExpected = [6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 2D tensor', function () {
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console.log('\n%c2D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.tanh(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.291313, 0.0, 0.664037, -0.291313, 0.761594])
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const shapeExpected = [2, 6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 3D tensor', function () {
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console.log('\n%c3D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.tanh(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.0, 0.197375, -0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.980096, 0.0, 0.664037, -0.291313, 0.761594])
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const shapeExpected = [2, 2, 3]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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})
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/*********************************************************
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* sigmoid
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*********************************************************/
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describe('sigmoid', function () {
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it('should work for 1D tensor', function () {
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console.log('\n%csigmoid', styles.h2)
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console.log('\n%c1D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.sigmoid(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797])
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const shapeExpected = [6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 2D tensor', function () {
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console.log('\n%c2D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.sigmoid(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797, 0.492501, 0.574443, 0.5, 0.689974, 0.425557, 0.731059])
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const shapeExpected = [2, 6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 3D tensor', function () {
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console.log('\n%c3D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.sigmoid(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.5, 0.549834, 0.377541, 0.475021, 0.731059, 0.880797, 0.492501, 0.908877, 0.5, 0.689974, 0.425557, 0.731059])
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const shapeExpected = [2, 2, 3]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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})
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/*********************************************************
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* hardSigmoid
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*********************************************************/
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describe('hardSigmoid', function () {
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it('should work for 1D tensor', function () {
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console.log('\n%chardSigmoid', styles.h2)
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console.log('\n%c1D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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console.log('%cin', styles.h4, t)
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const startTime = performance.now()
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activations.hardSigmoid(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, t)
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logTime(startTime, endTime)
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const dataOut = t.tensor.data
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const shapeOut = t.tensor.shape
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const dataExpected = new Float32Array([0.5, 0.54, 0.6, 0.48, 0.7, 0.9])
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const shapeExpected = [6]
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assert.deepEqual(shapeOut, shapeExpected)
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assert.isTrue(approxEquals(dataOut, dataExpected))
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})
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it('should work for 2D tensor', function () {
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console.log('\n%c2D', styles.h3)
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
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console.log('%cin', styles.h4, t)
|
||||
const startTime = performance.now()
|
||||
activations.hardSigmoid(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, t)
|
||||
logTime(startTime, endTime)
|
||||
const dataOut = t.tensor.data
|
||||
const shapeOut = t.tensor.shape
|
||||
const dataExpected = new Float32Array([0.5, 0.54, 0.6, 0.48, 0.7, 0.9, 0.494, 0.56, 0.5, 0.66, 0.44, 0.7])
|
||||
const shapeExpected = [2, 6]
|
||||
assert.deepEqual(shapeOut, shapeExpected)
|
||||
assert.isTrue(approxEquals(dataOut, dataExpected))
|
||||
})
|
||||
|
||||
it('should work for 3D tensor', function () {
|
||||
console.log('\n%c3D', styles.h3)
|
||||
let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
|
||||
console.log('%cin', styles.h4, t)
|
||||
const startTime = performance.now()
|
||||
activations.hardSigmoid(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, t)
|
||||
logTime(startTime, endTime)
|
||||
const dataOut = t.tensor.data
|
||||
const shapeOut = t.tensor.shape
|
||||
const dataExpected = new Float32Array([0.5, 0.54, 0.4, 0.48, 0.7, 0.9, 0.494, 0.96, 0.5, 0.66, 0.44, 0.7])
|
||||
const shapeExpected = [2, 2, 3]
|
||||
assert.deepEqual(shapeOut, shapeExpected)
|
||||
assert.isTrue(approxEquals(dataOut, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* linear
|
||||
*********************************************************/
|
||||
|
||||
describe('linear', function () {
|
||||
it('should work for 1D tensor', function () {
|
||||
console.log('\n%clinear', styles.h2)
|
||||
console.log('\n%c1D', styles.h3)
|
||||
let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
||||
console.log('%cin', styles.h4, t)
|
||||
const startTime = performance.now()
|
||||
activations.linear(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, t)
|
||||
logTime(startTime, endTime)
|
||||
const dataOut = t.tensor.data
|
||||
const shapeOut = t.tensor.shape
|
||||
const dataExpected = new Float32Array([0, 0.2, 0.5, -0.1, 1, 2])
|
||||
const shapeExpected = [6]
|
||||
assert.deepEqual(shapeOut, shapeExpected)
|
||||
assert.isTrue(approxEquals(dataOut, dataExpected))
|
||||
})
|
||||
|
||||
it('should work for 2D tensor', function () {
|
||||
console.log('\n%c2D', styles.h3)
|
||||
let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
|
||||
console.log('%cin', styles.h4, t)
|
||||
const startTime = performance.now()
|
||||
activations.linear(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, t)
|
||||
logTime(startTime, endTime)
|
||||
const dataOut = t.tensor.data
|
||||
const shapeOut = t.tensor.shape
|
||||
const dataExpected = new Float32Array([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1])
|
||||
const shapeExpected = [2, 6]
|
||||
assert.deepEqual(shapeOut, shapeExpected)
|
||||
assert.isTrue(approxEquals(dataOut, dataExpected))
|
||||
})
|
||||
|
||||
it('should work for 3D tensor', function () {
|
||||
console.log('\n%c3D', styles.h3)
|
||||
let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
|
||||
console.log('%cin', styles.h4, t)
|
||||
const startTime = performance.now()
|
||||
activations.linear(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, t)
|
||||
logTime(startTime, endTime)
|
||||
const dataOut = t.tensor.data
|
||||
const shapeOut = t.tensor.shape
|
||||
const dataExpected = new Float32Array([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1])
|
||||
const shapeExpected = [2, 2, 3]
|
||||
assert.deepEqual(shapeOut, shapeExpected)
|
||||
assert.isTrue(approxEquals(dataOut, dataExpected))
|
||||
})
|
||||
})
|
||||
})
|
||||
|
||||
Reference in New Issue
Block a user