mirror of
https://github.com/wassname/keras-js.git
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442 lines
22 KiB
JavaScript
442 lines
22 KiB
JavaScript
/* eslint-env browser, mocha */
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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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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: Merge', styles.h1)
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})
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/*********************************************************
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* sum
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*********************************************************/
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describe('sum', function () {
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before(function () {
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console.log('\n%csum', styles.h2)
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})
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it('[core.Merge.0] should produce expected values in sum mode', function () {
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const key = 'core.Merge.0'
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console.log(`\n%c[${key}] mode: sum`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2 = new layers.Merge({ mode: 'sum' })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t1a = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let t1b = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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t1a = testLayer1a.call(t1a)
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t1b = testLayer1b.call(t1b)
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console.log('%cin', styles.h4, stringifyCondensed(t1a.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(t1b.tensor))
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const startTime = performance.now()
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let t2 = testLayer2.call([t1a, t1b])
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t2.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(t2.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t2.tensor, dataExpected))
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})
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})
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/*********************************************************
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* mul
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*********************************************************/
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describe('mul', function () {
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before(function () {
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console.log('\n%cmul', styles.h2)
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})
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it('[core.Merge.1] should produce expected values in mul mode', function () {
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const key = 'core.Merge.1'
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console.log(`\n%c[${key}] mode: mul`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2 = new layers.Merge({ mode: 'mul' })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t1a = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let t1b = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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t1a = testLayer1a.call(t1a)
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t1b = testLayer1b.call(t1b)
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console.log('%cin', styles.h4, stringifyCondensed(t1a.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(t1b.tensor))
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const startTime = performance.now()
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let t2 = testLayer2.call([t1a, t1b])
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t2.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(t2.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t2.tensor, dataExpected))
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})
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})
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/*********************************************************
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* ave
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*********************************************************/
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describe('ave', function () {
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before(function () {
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console.log('\n%cave', styles.h2)
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})
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it('[core.Merge.2] should produce expected values in ave mode', function () {
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const key = 'core.Merge.2'
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console.log(`\n%c[${key}] mode: ave`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2 = new layers.Merge({ mode: 'ave' })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t1a = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let t1b = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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t1a = testLayer1a.call(t1a)
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t1b = testLayer1b.call(t1b)
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console.log('%cin', styles.h4, stringifyCondensed(t1a.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(t1b.tensor))
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const startTime = performance.now()
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let t2 = testLayer2.call([t1a, t1b])
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t2.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(t2.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t2.tensor, dataExpected))
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})
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})
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/*********************************************************
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* max
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*********************************************************/
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describe('max', function () {
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before(function () {
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console.log('\n%cmax', styles.h2)
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})
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it('[core.Merge.3] should produce expected values in max mode', function () {
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const key = 'core.Merge.3'
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console.log(`\n%c[${key}] mode: max`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2 = new layers.Merge({ mode: 'max' })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t1a = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let t1b = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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t1a = testLayer1a.call(t1a)
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t1b = testLayer1b.call(t1b)
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console.log('%cin', styles.h4, stringifyCondensed(t1a.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(t1b.tensor))
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const startTime = performance.now()
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let t2 = testLayer2.call([t1a, t1b])
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t2.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(t2.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t2.tensor, dataExpected))
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})
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})
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/*********************************************************
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* concat
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*********************************************************/
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describe('concat', function () {
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before(function () {
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console.log('\n%cconcat', styles.h2)
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})
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it('[core.Merge.4] should produce expected values in concat mode (1D)', function () {
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const key = 'core.Merge.4'
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console.log(`\n%c[${key}] mode: concat (1D)`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2 = new layers.Merge({ mode: 'concat', concatAxis: -1 })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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ta = testLayer1a.call(ta)
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tb = testLayer1b.call(tb)
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console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
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const startTime = performance.now()
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let t = testLayer2.call([ta, tb])
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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.Merge.5] should produce expected values in concat mode (2D, concatAxis=-1)', function () {
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const key = 'core.Merge.5'
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console.log(`\n%c[${key}] mode: concat (2D, concatAxis=-1)`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer2a = new layers.RepeatVector({ n: 3 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2b = new layers.RepeatVector({ n: 3 })
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let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: -1 })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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ta = testLayer1a.call(ta)
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ta = testLayer2a.call(ta)
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tb = testLayer1b.call(tb)
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tb = testLayer2b.call(tb)
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console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
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const startTime = performance.now()
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let t = testLayer3.call([ta, tb])
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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.Merge.6] should produce expected values in concat mode (2D, concatAxis=-2)', function () {
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const key = 'core.Merge.6'
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console.log(`\n%c[${key}] mode: concat (2D, concatAxis=-2)`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer2a = new layers.RepeatVector({ n: 3 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2b = new layers.RepeatVector({ n: 3 })
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let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: -2 })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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ta = testLayer1a.call(ta)
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ta = testLayer2a.call(ta)
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tb = testLayer1b.call(tb)
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tb = testLayer2b.call(tb)
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console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
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const startTime = performance.now()
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let t = testLayer3.call([ta, tb])
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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.Merge.7] should produce expected values in concat mode (2D, concatAxis=1)', function () {
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const key = 'core.Merge.7'
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console.log(`\n%c[${key}] mode: concat (2D, concatAxis=1)`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer2a = new layers.RepeatVector({ n: 3 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2b = new layers.RepeatVector({ n: 3 })
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let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: 1 })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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ta = testLayer1a.call(ta)
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ta = testLayer2a.call(ta)
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tb = testLayer1b.call(tb)
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tb = testLayer2b.call(tb)
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console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
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const startTime = performance.now()
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let t = testLayer3.call([ta, tb])
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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.Merge.8] should produce expected values in concat mode (2D, concatAxis=2)', function () {
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const key = 'core.Merge.8'
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console.log(`\n%c[${key}] mode: concat (2D, concatAxis=2)`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer2a = new layers.RepeatVector({ n: 3 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2b = new layers.RepeatVector({ n: 3 })
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let testLayer3 = new layers.Merge({ mode: 'concat', concatAxis: 2 })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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ta = testLayer1a.call(ta)
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ta = testLayer2a.call(ta)
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tb = testLayer1b.call(tb)
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tb = testLayer2b.call(tb)
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console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
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const startTime = performance.now()
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let t = testLayer3.call([ta, tb])
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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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* dot
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*********************************************************/
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describe('dot', function () {
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before(function () {
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console.log('\n%cdot', styles.h2)
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})
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it('[core.Merge.9] should produce expected values in dot mode (2D x 2D, dotAxes=1)', function () {
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const key = 'core.Merge.9'
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console.log(`\n%c[${key}] mode: dot (2D x 2D, dotAxes=1)`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer2a = new layers.RepeatVector({ n: 3 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2b = new layers.RepeatVector({ n: 3 })
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let testLayer3 = new layers.Merge({ mode: 'dot', dotAxes: 1 })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
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let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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ta = testLayer1a.call(ta)
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ta = testLayer2a.call(ta)
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tb = testLayer1b.call(tb)
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tb = testLayer2b.call(tb)
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console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
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console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
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const startTime = performance.now()
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let t = testLayer3.call([ta, tb])
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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.Merge.10] should produce expected values in dot mode (2D x 2D, dotAxes=2)', function () {
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const key = 'core.Merge.10'
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console.log(`\n%c[${key}] mode: dot (2D x 2D, dotAxes=2)`, styles.h3)
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let testLayer1a = new layers.Dense({ outputDim: 2 })
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let testLayer2a = new layers.RepeatVector({ n: 3 })
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let testLayer1b = new layers.Dense({ outputDim: 2 })
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let testLayer2b = new layers.RepeatVector({ n: 3 })
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let testLayer3 = new layers.Merge({ mode: 'dot', dotAxes: 2 })
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testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
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|
testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
|
|
let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
|
let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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|
ta = testLayer1a.call(ta)
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|
ta = testLayer2a.call(ta)
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|
tb = testLayer1b.call(tb)
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|
tb = testLayer2b.call(tb)
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|
console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
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|
console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
|
|
const startTime = performance.now()
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|
let t = testLayer3.call([ta, tb])
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|
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))
|
|
})
|
|
})
|
|
|
|
/*********************************************************
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|
* cos
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|
*********************************************************/
|
|
|
|
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'
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|
console.log(`\n%c[${key}] mode: cos (2D x 2D, dotAxes=1)`, styles.h3)
|
|
let testLayer1a = new layers.Dense({ outputDim: 2 })
|
|
let testLayer2a = new layers.RepeatVector({ n: 3 })
|
|
let testLayer1b = new layers.Dense({ outputDim: 2 })
|
|
let testLayer2b = new layers.RepeatVector({ n: 3 })
|
|
let testLayer3 = new layers.Merge({ mode: 'cos', dotAxes: 1 })
|
|
testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
|
|
testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
|
|
let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
|
let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
|
|
console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array(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.Merge.12] should produce expected values in cos mode (2D x 2D, dotAxes=2)', function () {
|
|
const key = 'core.Merge.12'
|
|
console.log(`\n%c[${key}] mode: cos (2D x 2D, dotAxes=2)`, styles.h3)
|
|
let testLayer1a = new layers.Dense({ outputDim: 2 })
|
|
let testLayer2a = new layers.RepeatVector({ n: 3 })
|
|
let testLayer1b = new layers.Dense({ outputDim: 2 })
|
|
let testLayer2b = new layers.RepeatVector({ n: 3 })
|
|
let testLayer3 = new layers.Merge({ mode: 'cos', dotAxes: 2 })
|
|
testLayer1a.setWeights(TEST_DATA[key].weights.slice(0, 2).map(w => new KerasJS.Tensor(w.data, w.shape)))
|
|
testLayer1b.setWeights(TEST_DATA[key].weights.slice(2, 4).map(w => new KerasJS.Tensor(w.data, w.shape)))
|
|
let ta = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
|
let tb = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
|
ta = testLayer1a.call(ta)
|
|
ta = testLayer2a.call(ta)
|
|
tb = testLayer1b.call(tb)
|
|
tb = testLayer2b.call(tb)
|
|
console.log('%cin', styles.h4, stringifyCondensed(ta.tensor))
|
|
console.log('%cin', styles.h4, stringifyCondensed(tb.tensor))
|
|
const startTime = performance.now()
|
|
let t = testLayer3.call([ta, tb])
|
|
const endTime = performance.now()
|
|
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
|
logTime(startTime, endTime)
|
|
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
|
const shapeExpected = TEST_DATA[key].expected.shape
|
|
assert.deepEqual(t.tensor.shape, shapeExpected)
|
|
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
|
})
|
|
})
|
|
})
|