keras-js/test/normalization/BatchNormalization.js

/* eslint-env browser, mocha */

describe('normalization layer: BatchNormalization', 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

  const testParams = [
    { attrs: { epsilon: 1e-5, mode: 0, axis: -1 } },
    { attrs: { epsilon: 1e-2, mode: 0, axis: -1 } },
    { attrs: { epsilon: 1e-5, mode: 0, axis: 1 } },
    { attrs: { epsilon: 1e-5, mode: 0, axis: 2 } },
    { attrs: { epsilon: 1e-5, mode: 0, axis: 3 } },
    { attrs: { epsilon: 1e-5, mode: 1, axis: -1 } },
    { attrs: { epsilon: 1e-5, mode: 2, axis: -1 } }
  ]

  before(function () {
    console.log('\n%cnormalization layer: BatchNormalization', styles.h1)
  })

  testParams.forEach(({ attrs }, i) => {
    const key = `normalization.BatchNormalization.${i}`
    const title = `[${key}] test: epsilon='${attrs.epsilon}', mode=${attrs.mode}, axis=${attrs.axis}`

    it(title, function () {
      console.log(`\n%c${title}`, styles.h3)
      let testLayer = new layers.BatchNormalization(attrs)
      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))
    })
  })
})