setup test files

2026-09-12 12:33:40 +08:00 · 2016-08-21 21:48:05 -04:00
parent 0baa98b8b7
commit d4892e1ba5
3 changed files with 197 additions and 0 deletions
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+<html>
+<head>
+  <meta charset="utf-8">
+  <title>Keras-js | Mocha Tests</title>
+  <link href="https://cdn.rawgit.com/mochajs/mocha/v3.0.2/mocha.css" rel="stylesheet" />
+</head>
+<body>
+  <div id="mocha"></div>
+
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.1.0/jquery.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/mocha/3.0.2/mocha.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/chai/3.5.0/chai.min.js"></script>
+
+  <script src="/lib/gpu.min.js"></script>
+  <script src="/lib/weblas.js"></script>
+  <script src="/dist/keras.js"></script>
+
+  <script>mocha.setup('bdd')</script>
+  <script src="/test/test-utils.js"></script>
+  <script src="/test/activations.js"></script>
+  <script>
+    mocha.checkLeaks();
+    mocha.globals(['jQuery']);
+    mocha.run();
+  </script>
+</body>
+</html>
@@ -0,0 +1,136 @@
+/* eslint-env browser, mocha */
+
+const assert = chai.assert
+const activations = KerasJS.activations
+
+const styles = testUtils.styles
+const approxEquals = testUtils.approxEquals
+const logTime = testUtils.logTime
+
+describe('activations', function () {
+  describe('softmax', function () {
+    it('should work for 1D tensor', function () {
+      console.log('\n%cactivations', styles.h1)
+      console.log('\n%csoftmax', styles.h2)
+      console.log('\n%c1D', styles.h3)
+      let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
+      console.log('%cin', styles.h4, t)
+      const startTime = performance.now()
+      activations.softmax(t)
+      const endTime = performance.now()
+      console.log('%cout', styles.h4, t)
+      logTime(startTime, endTime)
+      const dataOut = t.tensor.data
+      const shapeOut = t.tensor.shape
+      const dataExpected = new Float32Array([0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965])
+      const shapeExpected = [6]
+      assert.deepEqual(shapeOut, shapeExpected)
+      assert.isTrue(approxEquals(dataOut, dataExpected))
+    })
+
+    it('should work for 2D tensor', function () {
+      console.log('\n%c2D', styles.h3)
+      let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
+      console.log('%cin', styles.h4, t)
+      const startTime = performance.now()
+      activations.softmax(t)
+      const endTime = performance.now()
+      console.log('%cout', styles.h4, t)
+      logTime(startTime, endTime)
+      const dataOut = t.tensor.data
+      const shapeOut = t.tensor.shape
+      const dataExpected = new Float32Array([0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965, 0.107768, 0.149902, 0.11105, 0.247147, 0.082268, 0.301865])
+      const shapeExpected = [2, 6]
+      assert.deepEqual(shapeOut, shapeExpected)
+      assert.isTrue(approxEquals(dataOut, dataExpected))
+    })
+  })
+
+  describe('relu', function () {
+    it('should work for 1D tensor', function () {
+      console.log('\n%crelu', styles.h2)
+      console.log('\n%c1D', styles.h3)
+      let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2], [6])
+      console.log('%cin', styles.h4, t)
+      const startTime = performance.now()
+      activations.relu(t)
+      const endTime = performance.now()
+      console.log('%cout', styles.h4, t)
+      logTime(startTime, endTime)
+      const dataOut = t.tensor.data
+      const shapeOut = t.tensor.shape
+      const dataExpected = new Float32Array([0.0, 0.2, 0.5, 0.0, 1.0, 2.0])
+      const shapeExpected = [6]
+      assert.deepEqual(shapeOut, shapeExpected)
+      assert.isTrue(approxEquals(dataOut, dataExpected))
+    })
+
+    it('should work for 2D tensor', function () {
+      console.log('\n%c2D', styles.h3)
+      let t = new KerasJS.Tensor([0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1], [2, 6])
+      console.log('%cin', styles.h4, t)
+      const startTime = performance.now()
+      activations.relu(t)
+      const endTime = performance.now()
+      console.log('%cout', styles.h4, t)
+      logTime(startTime, endTime)
+      const dataOut = t.tensor.data
+      const shapeOut = t.tensor.shape
+      const dataExpected = new Float32Array([0.0, 0.2, 0.5, 0.0, 1.0, 2.0, 0.0, 0.3, 0.0, 0.8, 0.0, 1.0])
+      const shapeExpected = [2, 6]
+      assert.deepEqual(shapeOut, shapeExpected)
+      assert.isTrue(approxEquals(dataOut, dataExpected))
+    })
+
+    it('should work for 3D tensor', function () {
+      console.log('\n%c3D', styles.h3)
+      let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
+      console.log('%cin', styles.h4, t)
+      const startTime = performance.now()
+      activations.relu(t)
+      const endTime = performance.now()
+      console.log('%cout', styles.h4, t)
+      logTime(startTime, endTime)
+      const dataOut = t.tensor.data
+      const shapeOut = t.tensor.shape
+      const dataExpected = new Float32Array([0.0, 0.2, 0.0, 0.0, 1.0, 2.0, 0.0, 2.3, 0.0, 0.8, 0.0, 1.0])
+      const shapeExpected = [2, 2, 3]
+      assert.deepEqual(shapeOut, shapeExpected)
+      assert.isTrue(approxEquals(dataOut, dataExpected))
+    })
+
+    it('should work with maxValue', function () {
+      console.log('\n%c3D, maxValue=0.5', styles.h3)
+      let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
+      console.log('%cin', styles.h4, t)
+      const startTime = performance.now()
+      activations.relu(t, { maxValue: 0.5 })
+      const endTime = performance.now()
+      console.log('%cout', styles.h4, t)
+      logTime(startTime, endTime)
+      const dataOut = t.tensor.data
+      const shapeOut = t.tensor.shape
+      const dataExpected = new Float32Array([0.0, 0.2, 0.0, 0.0, 0.5, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5])
+      const shapeExpected = [2, 2, 3]
+      assert.deepEqual(shapeOut, shapeExpected)
+      assert.isTrue(approxEquals(dataOut, dataExpected))
+    })
+
+    it('should work with alpha (slope of negative portion)', function () {
+      console.log('\n%c3D, alpha=0.3', styles.h3)
+      let t = new KerasJS.Tensor([0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1], [2, 2, 3])
+      console.log('%cin', styles.h4, t)
+      const startTime = performance.now()
+      activations.relu(t, { alpha: 0.3 })
+      const endTime = performance.now()
+      console.log('%cout', styles.h4, t)
+      logTime(startTime, endTime)
+      const dataOut = t.tensor.data
+      const shapeOut = t.tensor.shape
+      const dataExpected = new Float32Array([0.0, 0.2, -0.15, -0.03, 1.0, 2.0, -0.009, 2.3, 0.0, 0.8, -0.09, 1.0])
+      const shapeExpected = [2, 2, 3]
+      assert.deepEqual(shapeOut, shapeExpected)
+      assert.isTrue(approxEquals(dataOut, dataExpected))
+    })
+  })
+})
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+(function () {
+  'use strict'
+
+  const styles = {
+    h1: 'color:#001f3f;font-weight:bold;font-size:160%;',
+    h2: 'color:#0074D9;font-weight:bold;font-size:130%;',
+    h3: 'color:#FF4136;font-weight:bold;font-size:110%;',
+    h4: 'color:#AAAAAA;font-size:100%;',
+    time: 'color:#2ECC40;font-weight:bold;font-size:100%;'
+  }
+
+  function approxEquals (a, b, tol = 1e-6) {
+    if (a.length !== b.length) return false
+    for (let i = 0; i < a.length; i++) {
+      if (
+        a[i] < (b[i] - tol) ||
+        a[i] > (b[i] + tol)
+      ) {
+        return false
+      }
+    }
+    return true
+  }
+
+  function logTime (startTime, endTime) {
+    console.log(`%c>>>> exec: ${Math.round(100 * (endTime - startTime)) / 100} ms`, styles.time)
+  }
+
+  window.testUtils = {
+    styles,
+    approxEquals,
+    logTime
+  }
+})()