reorganize test folder

test/convolutional/convolutional.js

@@ -0,0 +1,44 @@
+/* eslint-env browser, mocha */
+
+describe('Layers: Convolutional', 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
+
+  before(function () {
+    console.log('\n%cLayers: Convolutional', styles.h1)
+  })
+
+  /*********************************************************
+  * Convolution2D
+  *********************************************************/
+
+  describe('Convolution2D', function () {
+    before(function () {
+      console.log('\n%cConvolution2D', styles.h2)
+    })
+
+    it('[convolutional.Convolution2D.0] [CPU] should produce expected values for activation=linear, borderMode=valid, subsample=[1,1], dimOrdering=tf, biase=true', function () {
+      const key = 'convolutional.Convolution2D.0'
+      const [nbRow, nbCol, nbFilter] = TEST_DATA[key].expected.shape
+      const attrs = { activation: 'linear', borderMode: 'valid', subsample: [1, 1], dimOrdering: 'tf', bias: true }
+      console.log(`\n%c[${key}] [CPU] test 1: ${nbFilter} ${nbRow}x${nbCol} filters on 5x5x2 input, activation='${attrs.activation}', border_mode='${attrs.borderMode}', subsample=${attrs.subsample}, dim_ordering='${attrs.dimOrdering}', bias=${attrs.bias}`, styles.h3)
+      let testLayer = new layers.Convolution2D(nbFilter, nbRow, nbCol, 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))
+    })
+  })
+})