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Merged
mubaidr merged 3 commits into from
May 13, 2020
Merged

merge master #549

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6bfb207
fix: All recurrent end to end tests
robertleeplummerjr May 5, 2020
2b61239
Merge branch 'rnn-cleanup'
robertleeplummerjr May 5, 2020
7b73a89
feat: Support raw numbers in DataFormatter, fixes #532
robertleeplummerjr May 6, 2020
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4 changes: 2 additions & 2 deletions __tests__/layer/recurrent.js → __tests__/layer/rnn-cell.js
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
const { recurrent } = require('../../src/layer/recurrent');
const { rnnCell } = require('../../src/layer/rnn-cell');

describe('Recurrent Layer', () => {
test('properly sets width and height', () => {
Expand All @@ -12,7 +12,7 @@ describe('Recurrent Layer', () => {
},
};

const layer = recurrent(settings, input, recurrentInput);
const layer = rnnCell(settings, input, recurrentInput);

expect(layer.width).toEqual(1);
expect(layer.height).toEqual(settings.height);
Expand Down
52 changes: 24 additions & 28 deletions __tests__/recurrent/end-to-end.js
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
const { GPU } = require('gpu.js');
const { add, input, multiply, output, random, recurrent, lstm } = require('../../src/layer');
const { add, input, multiply, output, random, rnnCell, lstmCell } = require('../../src/layer');
const { setup, teardown } = require('../../src/utilities/kernel');

const { Recurrent } = require('../../src/recurrent');
Expand Down Expand Up @@ -35,7 +35,7 @@ describe('Recurrent Class: End to End', () => {
inputLayer: () => input({ height: 1 }),
hiddenLayers: [
(inputLayer, recurrentInput) =>
recurrent({ width: 1, height: 3 }, inputLayer, recurrentInput),
rnnCell({ width: 1, height: 3 }, inputLayer, recurrentInput),
],
outputLayer: inputLayer => output({ height: 1 }, inputLayer),
});
Expand Down Expand Up @@ -440,7 +440,7 @@ describe('Recurrent Class: End to End', () => {
inputLayer: () => input({ width: 1 }),
hiddenLayers: [
(inputLayer, recurrentInput) =>
recurrent({ width: 1, height: 1 }, inputLayer, recurrentInput),
rnnCell({ width: 1, height: 1 }, inputLayer, recurrentInput),
],
outputLayer: inputLayer => output({ width: 1, height: 1 }, inputLayer),
});
Expand All @@ -463,9 +463,9 @@ describe('Recurrent Class: End to End', () => {
inputLayer: () => input({ width: 1 }),
hiddenLayers: [
(inputLayer, recurrentInput) =>
recurrent({ height: 3, width: 1 }, inputLayer, recurrentInput),
rnnCell({ height: 3, width: 1 }, inputLayer, recurrentInput),
(inputLayer, recurrentInput) =>
recurrent({ height: 1, width: 1 }, inputLayer, recurrentInput),
rnnCell({ height: 1, width: 1 }, inputLayer, recurrentInput),
],
outputLayer: inputLayer => output({ height: 1 }, inputLayer),
});
Expand All @@ -481,7 +481,7 @@ describe('Recurrent Class: End to End', () => {
inputLayer: () => input({ height: 1 }),
hiddenLayers: [
(inputLayer, recurrentInput) =>
recurrent({ height: 3 }, inputLayer, recurrentInput),
rnnCell({ height: 3 }, inputLayer, recurrentInput),
],
outputLayer: inputLayer => output({ height: 1 }, inputLayer),
});
Expand All @@ -500,38 +500,34 @@ describe('Recurrent Class: End to End', () => {

it('can learn xor', () => {
const net = new Recurrent({
praxisOpts: {
regularizationStrength: 0.000001,
learningRate: 0.01,
},
inputLayer: () => input({ height: 1 }),
hiddenLayers: [
(input, recurrentInput) => recurrent({ height: 3 }, input, recurrentInput)
(input, recurrentInput) => lstmCell({ height: 20 }, input, recurrentInput)
],
outputLayer: input => output({ height: 1 }, input)
});
net.initialize();
net.initializeDeep();
expect(net._model.length).toBe(5);
expect(net._layerSets[0].length).toBe(15);
expect(net._layerSets[1].length).toBe(15);
let error;
for (let i = 0; i < 100; i++) {
let sum = 0;
sum = net._trainPattern([0, 0, 0], true)[0];
sum += net._trainPattern([0, 1, 1], true)[0];
sum += net._trainPattern([1, 0, 1], true)[0];
sum += net._trainPattern([1, 1, 0], true)[0];
error = sum / 4;
}
console.log(net.runInput([0, 0]));
console.log(net.runInput([0, 1]));
console.log(net.runInput([1, 0]));
console.log(net.runInput([1, 1]));
expect(error / 4).toBe(0.005);
let xorNetValues = [
[0.001, 0.001, 0.001],
[0.001, 1, 1],
[1, 0.001, 1],
[1, 1, 0.001]
];
net.train(xorNetValues, { iterations: 300 });
expect(net.run([0.001, 0.001])[0][0] < 0.1).toBeTruthy();
expect(net.run([0.001, 1])[0][0] > 0.9).toBeTruthy();
expect(net.run([1, 0.001])[0][0] > 0.9).toBeTruthy();
expect(net.run([1, 1])[0][0] < 0.1).toBeTruthy();
});
test('can learn 1,2,3', () => {
const net = new Recurrent({
inputLayer: () => input({ height: 1 }),
hiddenLayers: [
(inputLayer, recurrentInput) =>
lstm({ height: 10 }, inputLayer, recurrentInput),
lstmCell({ height: 10 }, inputLayer, recurrentInput),
],
outputLayer: inputLayer => output({ height: 1 }, inputLayer),
});
Expand All @@ -554,7 +550,7 @@ describe('Recurrent Class: End to End', () => {
inputLayer: () => input({ height: 1 }),
hiddenLayers: [
(inputLayer, recurrentInput) =>
recurrent({ height: 3 }, inputLayer, recurrentInput),
rnnCell({ height: 3 }, inputLayer, recurrentInput),
],
outputLayer: inputLayer => output({ height: 1 }, inputLayer),
});
Expand Down
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