mirror of
https://github.com/wassname/keras-js.git
synced 2026-09-09 11:25:25 +08:00
add MaxoutDense and Highway layers, with tests
This commit is contained in:
Leon Chen
+2
-2
@@ -18,7 +18,7 @@ describe('core layer: Dense', function () {
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describe('CPU', function () {
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before(function () {
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console.log('\n%cDense', styles.h2)
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console.log('\n%cCPU', styles.h2)
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})
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it('[core.Dense.0] [CPU] should produce expected values', function () {
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@@ -82,7 +82,7 @@ describe('core layer: Dense', function () {
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describe('CPU', function () {
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before(function () {
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console.log('\n%cDense', styles.h2)
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console.log('\n%cGPU', styles.h2)
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})
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it('[core.Dense.3] [GPU] should produce expected values', function () {
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@@ -0,0 +1,68 @@
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/* eslint-env browser, mocha */
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describe('core layer: Highway', 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%ccore layer: Highway', styles.h1)
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})
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it('[core.Highway.0] should produce expected values, transformBias=-2, activation=linear bias=true', function () {
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const key = 'core.Highway.0'
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console.log(`\n%c[${key}] transformBias=-2, activation=linear bias=true`, styles.h3)
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let testLayer = new layers.Highway({ transformBias: -2, activation: 'linear', bias: true })
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[core.Highway.1] should produce expected values, transformBias=-5, activation=tanh bias=true', function () {
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const key = 'core.Highway.1'
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console.log(`\n%c[${key}] transformBias=-5, activation=tanh bias=true`, styles.h3)
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let testLayer = new layers.Highway({ transformBias: -5, activation: 'tanh', bias: true })
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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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(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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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('[core.Highway.2] should produce expected values, transformBias=1, activation=hardSigmoid bias=false', function () {
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const key = 'core.Highway.2'
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console.log(`\n%c[${key}] transformBias=1, activation=hardSigmoid bias=false`, styles.h3)
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let testLayer = new layers.Highway({ transformBias: 1, activation: 'hardSigmoid', bias: false })
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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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(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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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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@@ -0,0 +1,50 @@
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/* eslint-env browser, mocha */
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describe('core layer: MaxoutDense', 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%ccore layer: MaxoutDense', styles.h1)
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})
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it('[core.MaxoutDense.0] should produce expected values, nbFeature=4, bias=true', function () {
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const key = 'core.MaxoutDense.0'
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console.log(`\n%c[${key}] nbFeature=4, bias=true`, styles.h3)
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let testLayer = new layers.MaxoutDense(3)
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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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(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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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('[core.MaxoutDense.1] should produce expected values, nbFeature=7, bias=false', function () {
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const key = 'core.MaxoutDense.1'
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console.log(`\n%c[${key}] nbFeature=7, bias=false`, styles.h3)
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let testLayer = new layers.MaxoutDense(3, { bias: false })
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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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(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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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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@@ -0,0 +1,86 @@
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// TEST DATA
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// Keyed by mocha test ID
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// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
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(function () {
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var DATA = {
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'core.Highway.0': {
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input: {
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data: [-0.665722, -0.215115, 0.236105, -0.17614, -0.99507, 0.768064],
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shape: [6]
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},
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weights: [
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{
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data: [0.176262, 0.795427, 0.783061, 0.631675, -0.928221, 0.383515, -0.242638, 0.037022, 0.315903, -0.6123, -0.455367, 0.437212, 0.566007, 0.700655, 0.55049, -0.926671, -0.766613, 0.502561, -0.521564, -0.490388, 0.715251, 0.899558, 0.123374, -0.642439, 0.540504, -0.015238, 0.262506, 0.678996, -0.077921, -0.00412, 0.358822, 0.301572, -0.46241, -0.865351, 0.54289, -0.038032],
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shape: [6, 6]
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},
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{
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data: [-0.90255, -0.421781, 0.441933, -0.956768, -0.588154, -0.898453],
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shape: [6]
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},
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{
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data: [-0.583079, -0.036638, -0.158924, 0.718364, -0.657677, -0.322272, -0.458934, 0.382083, -0.559191, 0.623902, -0.978946, 0.122407, 0.627452, 0.490201, -0.621777, -0.987718, 0.544088, 0.915664, 0.403876, -0.404843, 0.535985, 0.376437, -0.225633, 0.230412, -0.14489, 0.168579, 0.405271, -0.77621, 0.84654, 0.977773, 0.354822, 0.59033, -0.941847, -0.644482, 0.749855, 0.489864],
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shape: [6, 6]
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},
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{
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data: [0.034596, 0.893925, 0.53092, -0.435208, -0.557909, 0.372444],
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shape: [6]
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}
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],
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expected: {
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data: [-0.914347, -0.408456, -0.114281, -0.888056, -0.290505, -0.199544],
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shape: [6]
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}
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},
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'core.Highway.1': {
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input: {
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data: [-0.62992, -0.960677, 0.906504, 0.360902, -0.026824],
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shape: [5]
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},
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weights: [
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{
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data: [0.288287, -0.238503, 0.326096, -0.672699, 0.925216, -0.306676, 0.983502, -0.529884, 0.171389, -0.18662, -0.727531, 0.088273, 0.036353, 0.53371, 0.8677, -0.820593, -0.608457, 0.988387, -0.529639, -0.522027, 0.2582, 0.469905, 0.376689, -0.937739, 0.805028],
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shape: [5, 5]
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},
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{
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data: [-0.427892, 0.916211, 0.540626, 0.97374, -0.583669],
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shape: [5]
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},
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{
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data: [0.717779, -0.254578, 0.110258, 0.911313, 0.473339, 0.63241, -0.797827, 0.856976, 0.218218, 0.193107, -0.816432, -0.309628, 0.325505, -0.116573, 0.102976, 0.407425, 0.178802, -0.900134, 0.123584, 0.532717, 0.821817, -0.81418, 0.805043, -0.078079, -0.095963],
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shape: [5, 5]
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},
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{
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data: [-0.50298, -0.100049, -0.178118, -0.479401, 0.740791],
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shape: [5]
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}
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],
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expected: {
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data: [-0.652907, -0.353259, 0.890362, 0.477292, -0.256096],
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shape: [5]
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}
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},
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'core.Highway.2': {
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input: {
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data: [0.409965, -0.370646, 0.490565, -0.203574],
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shape: [4]
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},
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weights: [
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{
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data: [-0.184626, -0.889268, 0.57707, -0.42539, -0.099299, -0.392175, 0.052799, 0.247624, 0.553551, 0.372483, 0.961878, 0.201632, 0.627937, 0.41729, -0.944931, 0.808534],
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shape: [4, 4]
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},
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{
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data: [-0.498153, -0.907808, 0.353632, -0.913061, -0.767153, 0.207731, -0.618139, 0.337031, 0.834896, -0.16244, -0.33548, -0.433933, -0.627435, -0.365779, -0.037663, -0.860959],
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shape: [4, 4]
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}
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],
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expected: {
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data: [0.482075, -0.04199, 0.593448, 0.031503],
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shape: [4]
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}
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}
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}
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window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
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})()
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@@ -0,0 +1,46 @@
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// TEST DATA
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// Keyed by mocha test ID
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// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
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(function () {
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var DATA = {
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'core.MaxoutDense.0': {
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input: {
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data: [-0.992103, 0.024385, 0.625242, 0.225052, 0.443511, -0.416248],
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shape: [6]
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},
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weights: [
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{
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data: [0.542641, -0.958496, 0.267296, 0.497608, -0.002986, -0.550407, -0.603874, 0.521061, -0.661778, -0.82332, 0.37072, 0.906787, -0.992103, 0.024385, 0.625242, 0.225052, 0.443511, -0.416248, 0.835548, 0.429152, 0.085089, -0.71566, -0.253318, 0.348267, -0.116334, -0.131972, 0.235534, 0.026276, 0.300794, 0.202078, 0.610446, 0.043294, 0.817298, -0.361528, -0.819081, -0.3986, -0.772031, 0.657363, -0.906207, 0.252574, 0.095172, 0.638574, -0.602105, 0.713701, -0.296695, 0.509295, -0.408077, 0.767873, -0.348977, -0.669968, -0.214942, -0.813079, 0.642211, -0.697696, -0.231771, 0.888521, 0.975251, -0.087391, 0.652246, -0.497252, 0.194743, 0.805664, 0.069116, 0.180403, -0.921436, -0.285636, -0.840774, -0.38908, -0.338561, 0.547661, -0.920082, -0.141016],
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shape: [4, 6, 3]
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},
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{
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data: [0.542641, -0.958496, 0.267296, 0.497608, -0.002986, -0.550407, -0.603874, 0.521061, -0.661778, -0.82332, 0.37072, 0.906787],
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shape: [4, 3]
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}
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],
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expected: {
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data: [0.090044, 0.227783, 0.435236],
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shape: [3]
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}
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},
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'core.MaxoutDense.1': {
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input: {
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data: [-0.104458, 0.101279, 0.94235, 0.864827, 0.681371, -0.745903],
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shape: [6]
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},
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weights: [
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{
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data: [-0.639461, -0.96105, -0.073563, 0.449868, -0.159593, -0.029146, -0.974438, -0.025257, 0.883613, 0.70159, 0.459929, -0.782528, 0.787808, 0.714308, -0.669827, 0.264668, -0.959033, -0.766525, -0.367265, -0.684175, 0.517959, 0.636551, -0.310751, -0.362402, -0.776678, -0.832094, 0.425452, 0.199087, -0.888653, -0.040405, -0.196647, 0.695958, 0.435698, 0.204128, 0.104768, 0.898205, 0.973347, -0.323892, -0.520251, 0.592872, -0.872627, -0.270769, -0.859954, -0.361265, -0.859235, -0.419473, 0.580202, 0.810801, 0.585243, 0.123637, 0.232037, -0.277033, -0.662365, -0.127518, 0.465651, -0.874225, -0.958534, 0.541096, -0.400096, 0.402329, 0.469335, 0.865809, -0.199343, -0.283124, 0.613134, 0.528982, 0.305229, 0.621933, 0.28443, 0.914888, -0.332251, 0.476505, 0.899667, -0.331272, 0.223264, -0.26866, -0.076919, -0.849996, -0.961313, 0.519299, -0.094482, 0.245668, 0.479903, -0.162673, -0.264765, -0.661942, 0.587745, 0.666075, 0.468426, 0.750589, 0.296113, 0.392132, -0.613964, 0.535264, -0.331834, -0.124082, -0.36226, 0.136579, 0.317385, 0.151117, -0.363626, -0.563993, 0.689877, -0.395671, -0.123559, -0.817171, -0.398042, -0.82861, -0.312573, 0.418602, 0.947937, 0.25045, -0.461393, 0.313418, 0.253996, 0.651304, 0.067388, 0.822243, -0.168993, -0.427911, 0.038015, 0.8449, 0.447823, -0.012174, -0.005007, 0.297444],
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shape: [7, 6, 3]
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}
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],
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expected: {
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data: [1.043451, 2.068543, 0.396771],
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shape: [3]
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}
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}
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}
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window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
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})()
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