mirror of
https://github.com/wassname/keras-js.git
synced 2026-09-10 12:15:12 +08:00
split out tests of core layers
This commit is contained in:
Leon Chen
+16
-2
@@ -24,8 +24,22 @@
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<script src="/test/advanced_activations/data_advanced_activations.js"></script>
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<script src="/test/advanced_activations/advanced_activations.js"></script>
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<script src="/test/core/data_core.js"></script>
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<script src="/test/core/core.js"></script>
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<script src="/test/core/data_Dense.js"></script>
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<script src="/test/core/Dense.js"></script>
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<script src="/test/core/data_Activation.js"></script>
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<script src="/test/core/Activation.js"></script>
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<script src="/test/core/data_Dropout.js"></script>
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<script src="/test/core/Dropout.js"></script>
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<script src="/test/core/data_Flatten.js"></script>
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<script src="/test/core/Flatten.js"></script>
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<script src="/test/core/data_Reshape.js"></script>
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<script src="/test/core/Reshape.js"></script>
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<script src="/test/core/data_Permute.js"></script>
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<script src="/test/core/Permute.js"></script>
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<script src="/test/core/data_RepeatVector.js"></script>
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<script src="/test/core/RepeatVector.js"></script>
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<script src="/test/core/data_Merge.js"></script>
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<script src="/test/core/Merge.js"></script>
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<script src="/test/convolutional/data_Convolution2D.js"></script>
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<script src="/test/convolutional/Convolution2D.js"></script>
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@@ -1,6 +1,6 @@
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/* eslint-env browser, mocha */
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describe('Layers: Advanced Activations', function () {
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describe('advanced activation layers', 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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@@ -9,7 +9,7 @@ describe('Layers: Advanced Activations', function () {
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const layers = KerasJS.layers
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before(function () {
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console.log('\n%cLayers: Advanced Activations', styles.h1)
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console.log('\n%cadvanced activation layers', styles.h1)
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})
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/*********************************************************
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@@ -0,0 +1,54 @@
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/* eslint-env browser, mocha */
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describe('core layer: Activation', 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: Activation', styles.h1)
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})
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it('[core.Activation.0] should produce expected values for tanh activation following Dense layer', function () {
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const key = 'core.Activation.0'
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console.log(`\n%c[${key}] test 1 (tanh)`, styles.h3)
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let testLayer1 = new layers.Dense(2)
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testLayer1.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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t = testLayer1.call(t)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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let testLayer2 = new layers.Activation('tanh')
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const startTime = performance.now()
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t = testLayer2.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[core.Activation.1] should produce expected values for hardSigmoid activation following Dense layer', function () {
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const key = 'core.Activation.1'
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console.log(`\n%c[${key}] test 2 (hardSigmoid)`, styles.h3)
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let testLayer1 = new layers.Dense(2)
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testLayer1.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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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(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 +1,142 @@
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/* eslint-env browser, mocha */
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describe('core layer: Dense', 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: Dense', styles.h1)
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})
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/*********************************************************
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* CPU
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*********************************************************/
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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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})
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it('[core.Dense.0] [CPU] should produce expected values', function () {
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const key = 'core.Dense.0'
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console.log(`\n%c[${key}] [CPU] test 1`, styles.h3)
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let testLayer = new layers.Dense(2)
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[core.Dense.1] [CPU] should produce expected values, with sigmoid activation function', function () {
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const key = 'core.Dense.1'
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console.log(`\n%c[${key}] [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(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.Dense.2] [CPU] should produce expected values, with softplus activation function and no bias', function () {
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const key = 'core.Dense.2'
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console.log(`\n%c[${key}] [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(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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/*********************************************************
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* CPU
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*********************************************************/
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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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})
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it('[core.Dense.3] [GPU] should produce expected values', function () {
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const key = 'core.Dense.3'
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console.log(`\n%c[${key}] [GPU] test 1`, styles.h3)
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let testLayer = new layers.Dense(2)
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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, { 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(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.Dense.4] [GPU] should produce expected values, with sigmoid activation function', function () {
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const key = 'core.Dense.4'
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console.log(`\n%c[${key}] [GPU] test 2 (with sigmoid activation)`, styles.h3)
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let testLayer = new layers.Dense(2, { activation: 'sigmoid' })
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape, { useWeblas: true })
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[core.Dense.5] [GPU] should produce expected values, with softplus activation function and no bias', function () {
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const key = 'core.Dense.5'
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console.log(`\n%c[${key}] [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(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, { 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(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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})
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@@ -0,0 +1,34 @@
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/* eslint-env browser, mocha */
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describe('core layer: Dropout', 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: Dropout', styles.h1)
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})
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it('[core.Dropout.0] should just pass through tensor during test time', function () {
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const key = 'core.Dropout.0'
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console.log(`\n%c[${key}] should pass through`, styles.h3)
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let testLayer1 = new layers.Dense(2)
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testLayer1.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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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(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 +1,65 @@
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/* eslint-env browser, mocha */
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describe('core layer: Flatten', 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: Flatten', styles.h1)
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})
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it('[core.Flatten.0] should do nothing for 1D', function () {
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const key = 'core.Flatten.0'
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console.log(`\n%c[${key}] 1D`, styles.h3)
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let testLayer = new layers.Flatten()
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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.Flatten.1] should flatten 2D', function () {
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const key = 'core.Flatten.1'
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console.log(`\n%c[${key}] 2D`, styles.h3)
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let testLayer = new layers.Flatten()
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it('[core.Flatten.2] should flatten 3D', function () {
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const key = 'core.Flatten.2'
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console.log(`\n%c[${key}] 3D`, styles.h3)
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let testLayer = new layers.Flatten()
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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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@@ -1,6 +1,6 @@
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/* eslint-env browser, mocha */
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describe('Layers: Core', function () {
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describe('core layer: Merge', 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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@@ -9,407 +9,16 @@ describe('Layers: Core', function () {
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const layers = KerasJS.layers
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before(function () {
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console.log('\n%cLayers: Core', styles.h1)
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console.log('\n%ccore layer: Merge', styles.h1)
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})
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/*********************************************************
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* Dense
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* sum
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*********************************************************/
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describe('Dense', function () {
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describe('sum', 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('[core.Dense.0] [CPU] should produce expected values', function () {
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const key = 'core.Dense.0'
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console.log(`\n%c[${key}] [CPU] test 1`, styles.h3)
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let testLayer = new layers.Dense(2)
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Dense.1] [CPU] should produce expected values, with sigmoid activation function', function () {
|
||||
const key = 'core.Dense.1'
|
||||
console.log(`\n%c[${key}] [CPU] test 2 (with sigmoid activation)`, styles.h3)
|
||||
let testLayer = new layers.Dense(2, { activation: 'sigmoid' })
|
||||
testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Dense.2] [CPU] should produce expected values, with softplus activation function and no bias', function () {
|
||||
const key = 'core.Dense.2'
|
||||
console.log(`\n%c[${key}] [CPU] test 3 (with softplus activation and no bias)`, styles.h3)
|
||||
let testLayer = new layers.Dense(2, { activation: 'softplus', bias: false })
|
||||
testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Dense.3] [GPU] should produce expected values', function () {
|
||||
const key = 'core.Dense.3'
|
||||
console.log(`\n%c[${key}] [GPU] test 1`, styles.h3)
|
||||
let testLayer = new layers.Dense(2)
|
||||
testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape, { useWeblas: true })
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Dense.4] [GPU] should produce expected values, with sigmoid activation function', function () {
|
||||
const key = 'core.Dense.4'
|
||||
console.log(`\n%c[${key}] [GPU] test 2 (with sigmoid activation)`, styles.h3)
|
||||
let testLayer = new layers.Dense(2, { activation: 'sigmoid' })
|
||||
testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape, { useWeblas: true })
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Dense.5] [GPU] should produce expected values, with softplus activation function and no bias', function () {
|
||||
const key = 'core.Dense.5'
|
||||
console.log(`\n%c[${key}] [GPU] test 3 (with softplus activation and no bias)`, styles.h3)
|
||||
let testLayer = new layers.Dense(2, { activation: 'softplus', bias: false })
|
||||
testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape, { useWeblas: true })
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* Activation
|
||||
*********************************************************/
|
||||
|
||||
describe('Activation', function () {
|
||||
before(function () {
|
||||
console.log('\n%cActivation', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Activation.0] should produce expected values for tanh activation following Dense layer', function () {
|
||||
const key = 'core.Activation.0'
|
||||
console.log(`\n%c[${key}] test 1 (tanh)`, styles.h3)
|
||||
let testLayer1 = new layers.Dense(2)
|
||||
testLayer1.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
t = testLayer1.call(t)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
let testLayer2 = new layers.Activation('tanh')
|
||||
const startTime = performance.now()
|
||||
t = testLayer2.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Activation.1] should produce expected values for hardSigmoid activation following Dense layer', function () {
|
||||
const key = 'core.Activation.1'
|
||||
console.log(`\n%c[${key}] test 2 (hardSigmoid)`, styles.h3)
|
||||
let testLayer1 = new layers.Dense(2)
|
||||
testLayer1.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
t = testLayer1.call(t)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
let testLayer2 = new layers.Activation('hardSigmoid')
|
||||
const startTime = performance.now()
|
||||
t = testLayer2.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* Dropout
|
||||
*********************************************************/
|
||||
|
||||
describe('Dropout', function () {
|
||||
before(function () {
|
||||
console.log('\n%cDropout', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Dropout.0] should just pass through tensor during test time', function () {
|
||||
const key = 'core.Dropout.0'
|
||||
console.log(`\n%c[${key}] should pass through`, styles.h3)
|
||||
let testLayer1 = new layers.Dense(2)
|
||||
testLayer1.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
t = testLayer1.call(t)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
let testLayer2 = new layers.Dropout(0.5)
|
||||
const startTime = performance.now()
|
||||
t = testLayer2.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* Flatten
|
||||
*********************************************************/
|
||||
|
||||
describe('Flatten', function () {
|
||||
before(function () {
|
||||
console.log('\n%cFlatten', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Flatten.0] should do nothing for 1D', function () {
|
||||
const key = 'core.Flatten.0'
|
||||
console.log(`\n%c[${key}] 1D`, styles.h3)
|
||||
let testLayer = new layers.Flatten()
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Flatten.1] should flatten 2D', function () {
|
||||
const key = 'core.Flatten.1'
|
||||
console.log(`\n%c[${key}] 2D`, styles.h3)
|
||||
let testLayer = new layers.Flatten()
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Flatten.2] should flatten 3D', function () {
|
||||
const key = 'core.Flatten.2'
|
||||
console.log(`\n%c[${key}] 3D`, styles.h3)
|
||||
let testLayer = new layers.Flatten()
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* Reshape
|
||||
*********************************************************/
|
||||
|
||||
describe('Reshape', function () {
|
||||
before(function () {
|
||||
console.log('\n%cReshape', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Reshape.0] should be able to go from shape [6] -> [2, 3]', function () {
|
||||
const key = 'core.Reshape.0'
|
||||
console.log(`\n%c[${key}] shape [6] -> [2, 3]`, styles.h3)
|
||||
let testLayer = new layers.Reshape([2, 3])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Reshape.1] should be able to go from shape [3, 2] -> [6]', function () {
|
||||
const key = 'core.Reshape.1'
|
||||
console.log(`\n%c[${key}] shape [3, 2] -> [6]`, styles.h3)
|
||||
let testLayer = new layers.Reshape([6])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Reshape.2] should be able to go from shape [3, 2, 2] -> [4, 3]', function () {
|
||||
const key = 'core.Reshape.2'
|
||||
console.log(`\n%c[${key}] shape [3, 2, 2] -> [4, 3]`, styles.h3)
|
||||
let testLayer = new layers.Reshape([4, 3])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* Permute
|
||||
*********************************************************/
|
||||
|
||||
describe('Permute', function () {
|
||||
before(function () {
|
||||
console.log('\n%cPermute', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Permute.0] should be able to go from shape [3, 2] -> [2, 3]', function () {
|
||||
const key = 'core.Permute.0'
|
||||
console.log(`\n%c[${key}] shape [3, 2] -> [2, 3]`, styles.h3)
|
||||
let testLayer = new layers.Permute([2, 1])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Permute.1] should be able to go from shape [2, 3, 4] -> [4, 3, 2]', function () {
|
||||
const key = 'core.Permute.1'
|
||||
console.log(`\n%c[${key}] shape [2, 3, 4] -> [4, 3, 2]`, styles.h3)
|
||||
let testLayer = new layers.Permute([3, 2, 1])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* RepeatVector
|
||||
*********************************************************/
|
||||
|
||||
describe('RepeatVector', function () {
|
||||
before(function () {
|
||||
console.log('\n%cRepeatVector', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.RepeatVector.0] should be able to go from shape [6] -> [7, 6]', function () {
|
||||
const key = 'core.RepeatVector.0'
|
||||
console.log(`\n%c[${key}] repeat vector, shape [6] -> [7, 6]`, styles.h3)
|
||||
let testLayer = new layers.RepeatVector(7)
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* Merge
|
||||
*********************************************************/
|
||||
|
||||
describe('Merge', function () {
|
||||
before(function () {
|
||||
console.log('\n%cMerge', styles.h2)
|
||||
console.log('\n%csum', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Merge.0] should produce expected values in sum mode', function () {
|
||||
@@ -435,6 +44,16 @@ describe('Layers: Core', function () {
|
||||
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* mul
|
||||
*********************************************************/
|
||||
|
||||
describe('mul', function () {
|
||||
before(function () {
|
||||
console.log('\n%cmul', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Merge.1] should produce expected values in mul mode', function () {
|
||||
const key = 'core.Merge.1'
|
||||
@@ -459,6 +78,16 @@ describe('Layers: Core', function () {
|
||||
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* ave
|
||||
*********************************************************/
|
||||
|
||||
describe('ave', function () {
|
||||
before(function () {
|
||||
console.log('\n%cave', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Merge.2] should produce expected values in ave mode', function () {
|
||||
const key = 'core.Merge.2'
|
||||
@@ -483,6 +112,16 @@ describe('Layers: Core', function () {
|
||||
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* max
|
||||
*********************************************************/
|
||||
|
||||
describe('max', function () {
|
||||
before(function () {
|
||||
console.log('\n%cmax', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Merge.3] should produce expected values in max mode', function () {
|
||||
const key = 'core.Merge.3'
|
||||
@@ -507,6 +146,16 @@ describe('Layers: Core', function () {
|
||||
assert.deepEqual(t2.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t2.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* concat
|
||||
*********************************************************/
|
||||
|
||||
describe('concat', function () {
|
||||
before(function () {
|
||||
console.log('\n%cconcat', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Merge.4] should produce expected values in concat mode (1D)', function () {
|
||||
const key = 'core.Merge.4'
|
||||
@@ -643,6 +292,16 @@ describe('Layers: Core', function () {
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* dot
|
||||
*********************************************************/
|
||||
|
||||
describe('dot', function () {
|
||||
before(function () {
|
||||
console.log('\n%cdot', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Merge.9] should produce expected values in dot mode (2D x 2D, dotAxes=1)', function () {
|
||||
const key = 'core.Merge.9'
|
||||
@@ -699,6 +358,16 @@ describe('Layers: Core', function () {
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
|
||||
/*********************************************************
|
||||
* cos
|
||||
*********************************************************/
|
||||
|
||||
describe('cos', function () {
|
||||
before(function () {
|
||||
console.log('\n%ccos', styles.h2)
|
||||
})
|
||||
|
||||
it('[core.Merge.11] should produce expected values in cos mode (2D x 2D, dotAxes=1)', function () {
|
||||
const key = 'core.Merge.11'
|
||||
@@ -0,0 +1,48 @@
|
||||
/* eslint-env browser, mocha */
|
||||
|
||||
describe('core layer: Permute', function () {
|
||||
const assert = chai.assert
|
||||
const styles = testGlobals.styles
|
||||
const logTime = testGlobals.logTime
|
||||
const stringifyCondensed = testGlobals.stringifyCondensed
|
||||
const approxEquals = KerasJS.testUtils.approxEquals
|
||||
const layers = KerasJS.layers
|
||||
|
||||
before(function () {
|
||||
console.log('\n%ccore layer: Permute', styles.h1)
|
||||
})
|
||||
|
||||
it('[core.Permute.0] should be able to go from shape [3, 2] -> [2, 3]', function () {
|
||||
const key = 'core.Permute.0'
|
||||
console.log(`\n%c[${key}] shape [3, 2] -> [2, 3]`, styles.h3)
|
||||
let testLayer = new layers.Permute([2, 1])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Permute.1] should be able to go from shape [2, 3, 4] -> [4, 3, 2]', function () {
|
||||
const key = 'core.Permute.1'
|
||||
console.log(`\n%c[${key}] shape [2, 3, 4] -> [4, 3, 2]`, styles.h3)
|
||||
let testLayer = new layers.Permute([3, 2, 1])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,31 @@
|
||||
/* eslint-env browser, mocha */
|
||||
|
||||
describe('core layer: RepeatVector', function () {
|
||||
const assert = chai.assert
|
||||
const styles = testGlobals.styles
|
||||
const logTime = testGlobals.logTime
|
||||
const stringifyCondensed = testGlobals.stringifyCondensed
|
||||
const approxEquals = KerasJS.testUtils.approxEquals
|
||||
const layers = KerasJS.layers
|
||||
|
||||
before(function () {
|
||||
console.log('\n%ccore layer: RepeatVector', styles.h1)
|
||||
})
|
||||
|
||||
it('[core.RepeatVector.0] should be able to go from shape [6] -> [7, 6]', function () {
|
||||
const key = 'core.RepeatVector.0'
|
||||
console.log(`\n%c[${key}] repeat vector, shape [6] -> [7, 6]`, styles.h3)
|
||||
let testLayer = new layers.RepeatVector(7)
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,65 @@
|
||||
/* eslint-env browser, mocha */
|
||||
|
||||
describe('core layer: Reshape', function () {
|
||||
const assert = chai.assert
|
||||
const styles = testGlobals.styles
|
||||
const logTime = testGlobals.logTime
|
||||
const stringifyCondensed = testGlobals.stringifyCondensed
|
||||
const approxEquals = KerasJS.testUtils.approxEquals
|
||||
const layers = KerasJS.layers
|
||||
|
||||
before(function () {
|
||||
console.log('\n%ccore layer: Reshape', styles.h1)
|
||||
})
|
||||
|
||||
it('[core.Reshape.0] should be able to go from shape [6] -> [2, 3]', function () {
|
||||
const key = 'core.Reshape.0'
|
||||
console.log(`\n%c[${key}] shape [6] -> [2, 3]`, styles.h3)
|
||||
let testLayer = new layers.Reshape([2, 3])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Reshape.1] should be able to go from shape [3, 2] -> [6]', function () {
|
||||
const key = 'core.Reshape.1'
|
||||
console.log(`\n%c[${key}] shape [3, 2] -> [6]`, styles.h3)
|
||||
let testLayer = new layers.Reshape([6])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it('[core.Reshape.2] should be able to go from shape [3, 2, 2] -> [4, 3]', function () {
|
||||
const key = 'core.Reshape.2'
|
||||
console.log(`\n%c[${key}] shape [3, 2, 2] -> [4, 3]`, styles.h3)
|
||||
let testLayer = new layers.Reshape([4, 3])
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
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(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,26 @@
|
||||
// TEST DATA
|
||||
// Keyed by mocha test ID
|
||||
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.Activation.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [0.999999, -0.206966], shape: [2] }
|
||||
},
|
||||
'core.Activation.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [1.0, 0.458], shape: [2] }
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
@@ -0,0 +1,56 @@
|
||||
// TEST DATA
|
||||
// Keyed by mocha test ID
|
||||
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.Dense.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [7.3, -0.21], shape: [2] }
|
||||
},
|
||||
'core.Dense.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [0.999325, 0.447692], shape: [2] }
|
||||
},
|
||||
'core.Dense.2': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] }
|
||||
],
|
||||
expected: { data: [6.801113, 0.338274], shape: [2] }
|
||||
},
|
||||
'core.Dense.3': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [7.3, -0.21], shape: [2] }
|
||||
},
|
||||
'core.Dense.4': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [0.999325, 0.447692], shape: [2] }
|
||||
},
|
||||
'core.Dense.5': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] }
|
||||
],
|
||||
expected: { data: [6.801113, 0.338274], shape: [2] }
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
@@ -0,0 +1,18 @@
|
||||
// TEST DATA
|
||||
// Keyed by mocha test ID
|
||||
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.Dropout.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [7.3, -0.21], shape: [2] }
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
@@ -0,0 +1,22 @@
|
||||
// TEST DATA
|
||||
// Keyed by mocha test ID
|
||||
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.Flatten.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] }
|
||||
},
|
||||
'core.Flatten.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] }
|
||||
},
|
||||
'core.Flatten.2': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2, 2] },
|
||||
expected: { data: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0], shape: [12] }
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
@@ -4,121 +4,6 @@
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.Dense.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [7.3, -0.21], shape: [2] }
|
||||
},
|
||||
'core.Dense.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [0.999325, 0.447692], shape: [2] }
|
||||
},
|
||||
'core.Dense.2': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] }
|
||||
],
|
||||
expected: { data: [6.801113, 0.338274], shape: [2] }
|
||||
},
|
||||
'core.Dense.3': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [7.3, -0.21], shape: [2] }
|
||||
},
|
||||
'core.Dense.4': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [0.999325, 0.447692], shape: [2] }
|
||||
},
|
||||
'core.Dense.5': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] }
|
||||
],
|
||||
expected: { data: [6.801113, 0.338274], shape: [2] }
|
||||
},
|
||||
'core.Activation.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [0.999999, -0.206966], shape: [2] }
|
||||
},
|
||||
'core.Activation.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [1.0, 0.458], shape: [2] }
|
||||
},
|
||||
'core.Dropout.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
{ data: [0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0], shape: [6, 2] },
|
||||
{ data: [0.5, 0.7], shape: [2] }
|
||||
],
|
||||
expected: { data: [7.3, -0.21], shape: [2] }
|
||||
},
|
||||
'core.Flatten.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] }
|
||||
},
|
||||
'core.Flatten.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] }
|
||||
},
|
||||
'core.Flatten.2': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2, 2] },
|
||||
expected: { data: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0], shape: [12] }
|
||||
},
|
||||
'core.Reshape.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [2, 3] }
|
||||
},
|
||||
'core.Reshape.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] }
|
||||
},
|
||||
'core.Reshape.2': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2, 2] },
|
||||
expected: { data: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0], shape: [4, 3] }
|
||||
},
|
||||
'core.Permute.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2] },
|
||||
expected: { data: [0.0, 0.5, 1.0, 0.2, -0.1, 2.0], shape: [2, 3] }
|
||||
},
|
||||
'core.Permute.1': {
|
||||
input: {
|
||||
data: [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],
|
||||
shape: [2, 3, 4]
|
||||
},
|
||||
expected: {
|
||||
data: [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],
|
||||
shape: [4, 3, 2]
|
||||
}
|
||||
},
|
||||
'core.RepeatVector.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
expected: {
|
||||
data: [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],
|
||||
shape: [7, 6]
|
||||
}
|
||||
},
|
||||
'core.Merge.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
weights: [
|
||||
@@ -0,0 +1,24 @@
|
||||
// TEST DATA
|
||||
// Keyed by mocha test ID
|
||||
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.Permute.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2] },
|
||||
expected: { data: [0.0, 0.5, 1.0, 0.2, -0.1, 2.0], shape: [2, 3] }
|
||||
},
|
||||
'core.Permute.1': {
|
||||
input: {
|
||||
data: [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],
|
||||
shape: [2, 3, 4]
|
||||
},
|
||||
expected: {
|
||||
data: [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],
|
||||
shape: [4, 3, 2]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
@@ -0,0 +1,17 @@
|
||||
// TEST DATA
|
||||
// Keyed by mocha test ID
|
||||
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.RepeatVector.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
expected: {
|
||||
data: [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],
|
||||
shape: [7, 6]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
@@ -0,0 +1,22 @@
|
||||
// TEST DATA
|
||||
// Keyed by mocha test ID
|
||||
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
|
||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'core.Reshape.0': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [2, 3] }
|
||||
},
|
||||
'core.Reshape.1': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2] },
|
||||
expected: { data: [0, 0.2, 0.5, -0.1, 1, 2], shape: [6] }
|
||||
},
|
||||
'core.Reshape.2': {
|
||||
input: { data: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2], shape: [3, 2, 2] },
|
||||
expected: { data: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0], shape: [4, 3] }
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
Reference in New Issue
Block a user