mirror of
https://github.com/wassname/keras-js.git
synced 2026-10-04 12:40:46 +08:00
832 lines
37 KiB
JavaScript
832 lines
37 KiB
JavaScript
/* eslint-env browser, mocha */
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describe('Layers: Core', function () {
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const assert = chai.assert
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const styles = testGlobals.styles
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const logTime = testGlobals.logTime
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const stringifyCondensed = testGlobals.stringifyCondensed
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const approxEquals = KerasJS.testUtils.approxEquals
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const layers = KerasJS.layers
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before(function () {
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console.log('\n%Layers: Core', styles.h1)
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})
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/*********************************************************
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* Dense
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*********************************************************/
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describe('Dense', function () {
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before(function () {
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console.log('\n%cDense', styles.h2)
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})
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it('[CPU] should produce expected values', function () {
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console.log('\n%c[CPU] test 1', styles.h3)
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let testLayer = new layers.Dense(2)
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testLayer.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
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new KerasJS.Tensor([0.5, 0.7], [2])
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])
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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, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([7.3, -0.21])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[CPU] should produce expected values, with sigmoid activation function', function () {
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console.log('\n%c[CPU] test 2 (with sigmoid activation)', styles.h3)
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let testLayer = new layers.Dense(2, { activation: 'sigmoid' })
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testLayer.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
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new KerasJS.Tensor([0.5, 0.7], [2])
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])
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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, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0.999325, 0.447692])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[CPU] should produce expected values, with softplus activation function and no bias', function () {
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console.log('\n%c[CPU] test 3 (with softplus activation and no bias)', styles.h3)
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let testLayer = new layers.Dense(2, { activation: 'softplus', bias: false })
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testLayer.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2])
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])
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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, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([6.801113, 0.338274])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[GPU] should produce expected values', function () {
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console.log('\n%c[GPU] test 1', styles.h3)
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let testLayer = new layers.Dense(2)
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testLayer.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
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new KerasJS.Tensor([0.5, 0.7], [2])
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])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6], { useWeblas: true })
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([7.3, -0.21])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[GPU] should produce expected values, with sigmoid activation function', function () {
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console.log('\n%c[GPU] test 2 (with sigmoid activation)', styles.h3)
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let testLayer = new layers.Dense(2, { activation: 'sigmoid' })
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testLayer.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
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new KerasJS.Tensor([0.5, 0.7], [2])
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])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6], { useWeblas: true })
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0.999325, 0.447692])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[GPU] should produce expected values, with softplus activation function and no bias', function () {
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console.log('\n%c[GPU] test 3 (with softplus activation and no bias)', styles.h3)
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let testLayer = new layers.Dense(2, { activation: 'softplus', bias: false })
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testLayer.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2])
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])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6], { useWeblas: true })
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([6.801113, 0.338274])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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})
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/*********************************************************
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* Activation
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*********************************************************/
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describe('Activation', function () {
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before(function () {
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console.log('\n%cActivation', styles.h2)
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})
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it('should produce expected values for tanh activation following Dense layer', function () {
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console.log('\n%ctest 1 (tanh)', styles.h3)
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let testLayer1 = new layers.Dense(2)
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testLayer1.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
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new KerasJS.Tensor([0.5, 0.7], [2])
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])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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t = testLayer1.call(t)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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let testLayer2 = new layers.Activation('tanh')
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const startTime = performance.now()
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t = testLayer2.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0.999999, -0.206966])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('should produce expected values for hardSigmoid activation following Dense layer', function () {
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console.log('\n%ctest 2 (hardSigmoid)', styles.h3)
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let testLayer1 = new layers.Dense(2)
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testLayer1.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
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new KerasJS.Tensor([0.5, 0.7], [2])
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])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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t = testLayer1.call(t)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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let testLayer2 = new layers.Activation('hardSigmoid')
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const startTime = performance.now()
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t = testLayer2.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([1.0, 0.458])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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})
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/*********************************************************
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* Dropout
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*********************************************************/
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describe('Dropout', function () {
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before(function () {
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console.log('\n%cDropout', styles.h2)
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})
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it('should just pass through tensor during test time', function () {
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console.log('\n%cshould pass through', styles.h3)
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let testLayer1 = new layers.Dense(2)
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testLayer1.setWeights([
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new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
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new KerasJS.Tensor([0.5, 0.7], [2])
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])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
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t = testLayer1.call(t)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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let testLayer2 = new layers.Dropout(0.5)
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const startTime = performance.now()
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t = testLayer2.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([7.3, -0.21])
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const shapeExpected = [2]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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})
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/*********************************************************
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* Flatten
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*********************************************************/
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describe('Flatten', function () {
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before(function () {
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console.log('\n%cFlatten', styles.h2)
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})
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it('should do nothing for 1D', function () {
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console.log('\n%c1D', styles.h3)
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let testLayer = new layers.Flatten()
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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, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0, 0.2, 0.5, -0.1, 1, 2])
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const shapeExpected = [6]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('should flatten 2D', function () {
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console.log('\n%c2D', styles.h3)
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let testLayer = new layers.Flatten()
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [3, 2])
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0, 0.2, 0.5, -0.1, 1, 2])
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const shapeExpected = [6]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('should flatten 3D', function () {
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console.log('\n%c3D', styles.h3)
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let testLayer = new layers.Flatten()
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2], [3, 2, 2])
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0])
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const shapeExpected = [12]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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})
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/*********************************************************
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* Reshape
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*********************************************************/
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describe('Reshape', function () {
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before(function () {
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console.log('\n%cReshape', styles.h2)
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})
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it('should be able to go from shape [6] -> [2, 3]', function () {
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console.log('\n%cshape [6] -> [2, 3]', styles.h3)
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let testLayer = new layers.Reshape([2, 3])
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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, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0, 0.2, 0.5, -0.1, 1, 2])
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const shapeExpected = [2, 3]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('should be able to go from shape [3, 2] -> [6]', function () {
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console.log('\n%cshape [3, 2] -> [6]', styles.h3)
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let testLayer = new layers.Reshape([6])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [3, 2])
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0, 0.2, 0.5, -0.1, 1, 2])
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const shapeExpected = [6]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('should be able to go from shape [3, 2, 2] -> [4, 3]', function () {
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console.log('\n%cshape [3, 2, 2] -> [4, 3]', styles.h3)
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let testLayer = new layers.Reshape([4, 3])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2], [3, 2, 2])
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0])
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const shapeExpected = [4, 3]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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})
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/*********************************************************
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* Permute
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*********************************************************/
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describe('Permute', function () {
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before(function () {
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console.log('\n%cPermute', styles.h2)
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})
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it('should be able to go from shape [3, 2] -> [2, 3]', function () {
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console.log('\n%cshape [3, 2] -> [2, 3]', styles.h3)
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let testLayer = new layers.Permute([2, 1])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [3, 2])
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0.0, 0.5, 1.0, 0.2, -0.1, 2.0])
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const shapeExpected = [2, 3]
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('should be able to go from shape [2, 3, 4] -> [4, 3, 2]', function () {
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console.log('\n%cshape [2, 3, 4] -> [4, 3, 2]', styles.h3)
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let testLayer = new layers.Permute([3, 2, 1])
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let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2], [2, 3, 4])
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array([0.0, 0.0, 1.0, 1.0, 0.5, 0.5, 0.2, 0.2, 2.0, 2.0, -0.1, -0.1, 0.5, 0.5, 0.0, 0.0, 1.0, 1.0, -0.1, -0.1, 0.2, 0.2, 2.0, 2.0])
|
|
const shapeExpected = [4, 3, 2]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
})
|
|
|
|
/*********************************************************
|
|
* RepeatVector
|
|
*********************************************************/
|
|
|
|
describe('RepeatVector', function () {
|
|
before(function () {
|
|
console.log('\n%cRepeatVector', styles.h2)
|
|
})
|
|
|
|
it('should be able to go from shape [6] -> [7, 6]', function () {
|
|
console.log('\n%crepeat vector, shape [6] -> [7, 6]', styles.h3)
|
|
let testLayer = new layers.RepeatVector(7)
|
|
let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
|
const startTime = performance.now()
|
|
t = testLayer.call(t)
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0])
|
|
const shapeExpected = [7, 6]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
})
|
|
|
|
/*********************************************************
|
|
* Merge
|
|
*********************************************************/
|
|
|
|
describe('Merge', function () {
|
|
before(function () {
|
|
console.log('\n%cMerge', styles.h2)
|
|
})
|
|
|
|
it('should produce expected values in sum mode', function () {
|
|
console.log('\n%cmode: sum', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2 = new layers.Merge({ mode: 'sum' })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let t1a = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let t1b = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
t1a = testLayer1a.call(t1a)
|
|
t1b = testLayer1b.call(t1b)
|
|
console.log('%cin', styles.h4, stringifyCondensed([t1a.tensor, t1b.tensor]))
|
|
const startTime = performance.now()
|
|
let t2 = testLayer2.call([t1a, t1b])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t2.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([4.85, 4.27])
|
|
const shapeExpected = [2]
|
|
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in mul mode', function () {
|
|
console.log('\n%cmode: mul', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2 = new layers.Merge({ mode: 'mul' })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let t1a = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let t1b = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
t1a = testLayer1a.call(t1a)
|
|
t1b = testLayer1b.call(t1b)
|
|
console.log('%cin', styles.h4, stringifyCondensed([t1a.tensor, t1b.tensor]))
|
|
const startTime = performance.now()
|
|
let t2 = testLayer2.call([t1a, t1b])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t2.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([-17.885, -0.9408])
|
|
const shapeExpected = [2]
|
|
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in ave mode', function () {
|
|
console.log('\n%cmode: ave', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2 = new layers.Merge({ mode: 'ave' })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let t1a = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let t1b = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
t1a = testLayer1a.call(t1a)
|
|
t1b = testLayer1b.call(t1b)
|
|
console.log('%cin', styles.h4, stringifyCondensed([t1a.tensor, t1b.tensor]))
|
|
const startTime = performance.now()
|
|
let t2 = testLayer2.call([t1a, t1b])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t2.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([2.425, 2.135])
|
|
const shapeExpected = [2]
|
|
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in max mode', function () {
|
|
console.log('\n%cmode: max', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2 = new layers.Merge({ mode: 'max' })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let t1a = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let t1b = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
t1a = testLayer1a.call(t1a)
|
|
t1b = testLayer1b.call(t1b)
|
|
console.log('%cin', styles.h4, stringifyCondensed([t1a.tensor, t1b.tensor]))
|
|
const startTime = performance.now()
|
|
let t2 = testLayer2.call([t1a, t1b])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t2.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([7.3, 4.48])
|
|
const shapeExpected = [2]
|
|
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in concat mode (1D)', function () {
|
|
console.log('\n%cmode: concat (1D)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2 = new layers.Merge({ mode: 'concat', concatAxis: -1 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer2.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([7.3, -0.21, -2.45, 4.48])
|
|
const shapeExpected = [4]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in concat mode (2D, concatAxis=-1)', function () {
|
|
console.log('\n%cmode: concat (2D, concatAxis=-1)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: -1 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48])
|
|
const shapeExpected = [3, 4]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in concat mode (2D, concatAxis=-2)', function () {
|
|
console.log('\n%cmode: concat (2D, concatAxis=-2)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: -2 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48])
|
|
const shapeExpected = [6, 2]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in concat mode (2D, concatAxis=1)', function () {
|
|
console.log('\n%cmode: concat (2D, concatAxis=1)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: 1 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48])
|
|
const shapeExpected = [6, 2]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in concat mode (2D, concatAxis=2)', function () {
|
|
console.log('\n%cmode: concat (2D, concatAxis=2)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: 2 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48])
|
|
const shapeExpected = [3, 4]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in dot mode (2D x 2D, dotAxes=1)', function () {
|
|
console.log('\n%cmode: dot (2D x 2D, dotAxes=1)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'dot', dotAxes: 1 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([-53.655003, 98.112007, 1.5435, -2.8224])
|
|
const shapeExpected = [2, 2]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in dot mode (2D x 2D, dotAxes=2)', function () {
|
|
console.log('\n%cmode: dot (2D x 2D, dotAxes=2)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'dot', dotAxes: 2 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([-18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258])
|
|
const shapeExpected = [3, 3]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in cos mode (2D x 2D, dotAxes=1)', function () {
|
|
console.log('\n%cmode: cos (2D x 2D, dotAxes=1)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'cos', dotAxes: 1 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([-1.0, 7.972744, 0.125427, -1.0])
|
|
const shapeExpected = [1, 2, 2]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
|
|
it('should produce expected values in cos mode (2D x 2D, dotAxes=2)', function () {
|
|
console.log('\n%cmode: cos (2D x 2D, dotAxes=2)', styles.h3)
|
|
let testLayer1a = new layers.Dense(2)
|
|
let testLayer2a = new layers.RepeatVector(3)
|
|
let testLayer1b = new layers.Dense(2)
|
|
let testLayer2b = new layers.RepeatVector(3)
|
|
let testLayer3 = new layers.Merge({ mode: 'cos', dotAxes: 2 })
|
|
testLayer1a.setWeights([
|
|
new KerasJS.Tensor([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], [6, 2]),
|
|
new KerasJS.Tensor([0.5, 0.7], [2])
|
|
])
|
|
testLayer1b.setWeights([
|
|
new KerasJS.Tensor([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3], [6, 2]),
|
|
new KerasJS.Tensor([0.1, -0.2], [2])
|
|
])
|
|
let ta = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
let tb = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed([ta.tensor, tb.tensor]))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array([-0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843])
|
|
const shapeExpected = [1, 3, 3]
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
})
|
|
})
|