implement Convolution1D layer, with tests

test/convolutional/Convolution1D.js

@@ -0,0 +1,105 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: Convolution1D', 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: [5, 2],
+      kernelShape: [4, 3],
+      attrs: { activation: 'linear', borderMode: 'valid', subsampleLength: 1, bias: true }
+    },
+    {
+      inputShape: [6, 3],
+      kernelShape: [4, 3],
+      attrs: { activation: 'linear', borderMode: 'valid', subsampleLength: 1, bias: false }
+    },
+    {
+      inputShape: [4, 6],
+      kernelShape: [2, 3],
+      attrs: { activation: 'sigmoid', borderMode: 'same', subsampleLength: 2, bias: true }
+    },
+    {
+      inputShape: [8, 3],
+      kernelShape: [2, 7],
+      attrs: { activation: 'tanh', borderMode: 'same', subsampleLength: 1, bias: true }
+    }
+  ]
+
+  before(function () {
+    console.log('\n%cconvolutional layer: Convolution1D', styles.h1)
+  })
+
+  /*********************************************************
+  * CPU
+  *********************************************************/
+
+  describe('CPU', function () {
+    before(function () {
+      console.log('\n%cCPU', styles.h2)
+    })
+
+    testParams.forEach(({ inputShape, kernelShape, attrs }, i) => {
+      const key = `convolutional.Convolution1D.${i}`
+      const [inputLength, inputFeatures] = inputShape
+      const [nbFilter, filterLength] = kernelShape
+      const title = `[${key}] [CPU] test: ${nbFilter} length ${filterLength} filters on ${inputLength}x${inputFeatures} input, activation='${attrs.activation}', border_mode='${attrs.borderMode}', subsampleLength=${attrs.subsampleLength}, bias=${attrs.bias}`
+
+      it(title, function () {
+        console.log(`\n%c${title}`, styles.h3)
+        let testLayer = new layers.Convolution1D(nbFilter, filterLength, attrs)
+        testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
+        let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+        console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+        const startTime = performance.now()
+        t = testLayer.call(t)
+        const endTime = performance.now()
+        console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+        logTime(startTime, endTime)
+        const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+        const shapeExpected = TEST_DATA[key].expected.shape
+        assert.deepEqual(t.tensor.shape, shapeExpected)
+        assert.isTrue(approxEquals(t.tensor, dataExpected))
+      })
+    })
+  })
+
+  /*********************************************************
+  * GPU
+  *********************************************************/
+
+  describe('GPU', function () {
+    before(function () {
+      console.log('\n%cGPU', styles.h2)
+    })
+
+    testParams.forEach(({ inputShape, kernelShape, attrs }, i) => {
+      const key = `convolutional.Convolution1D.${i}`
+      const [inputLength, inputFeatures] = inputShape
+      const [nbFilter, filterLength] = kernelShape
+      const title = `[${key}] [GPU] test: ${nbFilter} length ${filterLength} filters on ${inputLength}x${inputFeatures} input, activation='${attrs.activation}', border_mode='${attrs.borderMode}', subsampleLength=${attrs.subsampleLength}, bias=${attrs.bias}`
+
+      it(title, function () {
+        console.log(`\n%c${title}`, styles.h3)
+        let testLayer = new layers.Convolution1D(nbFilter, filterLength, 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, { 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))
+      })
+    })
+  })
+})