implement MaxPooling1D/AveragePooling1D, with tests

test/pooling/AveragePooling1D.js

@@ -0,0 +1,75 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: AveragePooling1D', 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 = [
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: null, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 1, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 3, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: null, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 1, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 3, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 3, stride: null, borderMode: 'valid' }
+    },
+    {
+      inputShape: [7, 7],
+      attrs: { poolLength: 3, stride: 1, borderMode: 'same' }
+    },
+    {
+      inputShape: [7, 7],
+      attrs: { poolLength: 3, stride: 3, borderMode: 'same' }
+    }
+  ]
+
+  before(function () {
+    console.log('\n%cconvolutional layer: AveragePooling1D', styles.h1)
+  })
+
+  testParams.forEach(({ inputShape, attrs }, i) => {
+    const key = `convolutional.AveragePooling1D.${i}`
+    const [inputLength, inputFeatures] = inputShape
+    const title = `[${key}] test: ${inputLength}x${inputFeatures} input, poolLength='${attrs.poolLength}', stride=${attrs.stride}, borderMode=${attrs.borderMode}`
+
+    it(title, function () {
+      console.log(`\n%c${title}`, styles.h3)
+      let testLayer = new layers.AveragePooling1D(attrs)
+      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))
+    })
+  })
+})