week1 ~ week4 review VS TensorFlow.js

July 12, 2019 13 minute read

Tags: coursera-machine-learning, linear-regression, neural-networks, tensorflow

超爽~~~ 很多細節要留著下禮拜上完後我再一一補上!!! YA!!

HAHA 厲害啦! 複習 + TednsorFlow.js YA!

用出來超爽的兒~~
- 這練習的教學影片: Linear Regression with TensorFlow.js
- 畫圖的JS: p5.js
- TendorFlow.js

Linear Regression

在圖紙上點。 tensorflow 自動幫你找最棒棒的 regression line

先來速度看code:

加入相關的libraries

 <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
 <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.9.0/p5.min.js" integrity="sha256-WVsM3xrcqyuFNF3W1qtIKbHFsD0977nDQA8DCMp1zCw=" crossorigin="anonymous"></script>   

javascript code

 	const x_vals = [];
 	const y_vals = [];
   
 	let m, b;
   
 	const learningRate = 0.2;
 	const optimizer = tf.train.sgd(learningRate);
   
 	function setup() {
        const canvas = createCanvas(400, 400);
          
        // scalar = real number
        m = tf.variable(tf.scalar(random(1)));
        b = tf.variable(tf.scalar(random(1)));
        canvas.parent('tf-linear-regression-container')
 	}
   
 	function loss(pred, labels) {
 	  // J( θ0, θ1 ) = 1/2m * ∑ ( hθ * ( x^(i) ) - y^(i) )^2 
       return pred.sub(labels).square().mean();
 	}
   
 	function predict(x) {
 	 const xs = tf.tensor1d(x);
 	 // y = mx + b;
 	 const ys = xs.mul(m).add(b);
      return ys;
 	}
   
 	function mousePressed() {
 		const x = map(mouseX, 0, width, 0, 1);
 		const y = map(mouseY, 0, height, 1, 0);
 		x_vals.push(x);
 		y_vals.push(y);
 	}
   
 	function draw() {
   
       // memory control
       tf.tidy(() => {
         if(x_vals.length > 0) {
           const ys = tf.tensor1d(y_vals);
           // https://js.tensorflow.org/api/latest/#train.sgd
 	      optimizer.minimize(() => loss(predict(x_vals), ys));
         }           	
       });
   
       background('#7FB4BE');
   
       stroke(255);
       strokeWeight(8);
       for (let i = 0; i < x_vals.length; i ++) {
       	let px = map(x_vals[i], 0, 1, 0 , width);
       	let py = map(y_vals[i], 0, 1, height, 0);
       	point(px, py);
       }
   
       const LineX = [0, 1];
       const ys = tf.tidy(() => predict(LineX));
       let lineY = ys.dataSync();
       ys.dispose();
   
       let x1 = map(LineX[0], 0, 1, 0, width);
       let x2 = map(LineX[1], 0, 1, 0, width); 
           
       let y1 = map(lineY[0], 0, 1, height, 0);
       let y2 = map(lineY[1], 0, 1, height, 0);
       strokeWeight(2)
       line(x1,y1,x2,y2);
     }

Neural Network

學習影片
- Build a neural network with TensorFlow

用最經典的範例!

Iris DataSet

用美美的花花來學習 AI 真的是再幸福不過的事情了~

速度看 code
- optimizer的地方，要到 week5 大神才會介紹，這次就當課前預習吧 :)
- 有要用到 json 資料所以會很醜可以練習 compile 一下 XDDD

iris = [
    {"sepal_length": "5.1", "sepal_width": "3.5", "petal_length": "1.4", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "4.9", "sepal_width": "3.0", "petal_length": "1.4", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "4.7", "sepal_width": "3.2", "petal_length": "1.3", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "4.6", "sepal_width": "3.1", "petal_length": "1.5", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "5.0", "sepal_width": "3.6", "petal_length": "1.4", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "5.4", "sepal_width": "3.9", "petal_length": "1.7", "petal_width": "0.4", "species": "setosa"},
    {"sepal_length": "4.6", "sepal_width": "3.4", "petal_length": "1.4", "petal_width": "0.3", "species": "setosa"},
    {"sepal_length": "5.0", "sepal_width": "3.4", "petal_length": "1.5", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "4.4", "sepal_width": "2.9", "petal_length": "1.4", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "4.9", "sepal_width": "3.1", "petal_length": "1.5", "petal_width": "0.1", "species": "setosa"},
    {"sepal_length": "5.4", "sepal_width": "3.7", "petal_length": "1.5", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "4.8", "sepal_width": "3.4", "petal_length": "1.6", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "4.8", "sepal_width": "3.0", "petal_length": "1.4", "petal_width": "0.1", "species": "setosa"},
    {"sepal_length": "4.3", "sepal_width": "3.0", "petal_length": "1.1", "petal_width": "0.1", "species": "setosa"},
    {"sepal_length": "5.8", "sepal_width": "4.0", "petal_length": "1.2", "petal_width": "0.2", "species": "setosa"},
    {"sepal_length": "5.7", "sepal_width": "4.4", "petal_length": "1.5", "petal_width": "0.4", "species": "setosa"},
    {"sepal_length": "5.4", "sepal_width": "3.9", "petal_length": "1.3", "petal_width": "0.4", "species": "setosa"},
    {"sepal_length": "5.1", "sepal_width": "3.5", "petal_length": "1.4", "petal_width": "0.3", "species": "setosa"},
    {"sepal_length": "5.7", "sepal_width": "3.8", "petal_length": "1.7", "petal_width": "0.3", "species": "setosa"},
    {"sepal_length": "5.1", "sepal_width": "3.8", "petal_length": "1.5", "petal_width": "0.3", "species": "setosa"},
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    {"sepal_length": "6.7", "sepal_width": "3.3", "petal_length": "5.7", "petal_width": "2.1", "species": "virginica"},
    {"sepal_length": "7.2", "sepal_width": "3.2", "petal_length": "6.0", "petal_width": "1.8", "species": "virginica"},
    {"sepal_length": "6.2", "sepal_width": "2.8", "petal_length": "4.8", "petal_width": "1.8", "species": "virginica"},
    {"sepal_length": "6.1", "sepal_width": "3.0", "petal_length": "4.9", "petal_width": "1.8", "species": "virginica"},
    {"sepal_length": "6.4", "sepal_width": "2.8", "petal_length": "5.6", "petal_width": "2.1", "species": "virginica"},
    {"sepal_length": "7.2", "sepal_width": "3.0", "petal_length": "5.8", "petal_width": "1.6", "species": "virginica"},
    {"sepal_length": "7.4", "sepal_width": "2.8", "petal_length": "6.1", "petal_width": "1.9", "species": "virginica"},
    {"sepal_length": "7.9", "sepal_width": "3.8", "petal_length": "6.4", "petal_width": "2.0", "species": "virginica"},
    {"sepal_length": "6.4", "sepal_width": "2.8", "petal_length": "5.6", "petal_width": "2.2", "species": "virginica"},
    {"sepal_length": "6.3", "sepal_width": "2.8", "petal_length": "5.1", "petal_width": "1.5", "species": "virginica"},
    {"sepal_length": "6.1", "sepal_width": "2.6", "petal_length": "5.6", "petal_width": "1.4", "species": "virginica"},
    {"sepal_length": "7.7", "sepal_width": "3.0", "petal_length": "6.1", "petal_width": "2.3", "species": "virginica"},
    {"sepal_length": "6.3", "sepal_width": "3.4", "petal_length": "5.6", "petal_width": "2.4", "species": "virginica"},
    {"sepal_length": "6.4", "sepal_width": "3.1", "petal_length": "5.5", "petal_width": "1.8", "species": "virginica"},
    {"sepal_length": "6.0", "sepal_width": "3.0", "petal_length": "4.8", "petal_width": "1.8", "species": "virginica"},
    {"sepal_length": "6.9", "sepal_width": "3.1", "petal_length": "5.4", "petal_width": "2.1", "species": "virginica"},
    {"sepal_length": "6.7", "sepal_width": "3.1", "petal_length": "5.6", "petal_width": "2.4", "species": "virginica"},
    {"sepal_length": "6.9", "sepal_width": "3.1", "petal_length": "5.1", "petal_width": "2.3", "species": "virginica"},
    {"sepal_length": "5.8", "sepal_width": "2.7", "petal_length": "5.1", "petal_width": "1.9", "species": "virginica"},
    {"sepal_length": "6.8", "sepal_width": "3.2", "petal_length": "5.9", "petal_width": "2.3", "species": "virginica"},
    {"sepal_length": "6.7", "sepal_width": "3.3", "petal_length": "5.7", "petal_width": "2.5", "species": "virginica"},
    {"sepal_length": "6.7", "sepal_width": "3.0", "petal_length": "5.2", "petal_width": "2.3", "species": "virginica"},
    {"sepal_length": "6.3", "sepal_width": "2.5", "petal_length": "5.0", "petal_width": "1.9", "species": "virginica"},
    {"sepal_length": "6.5", "sepal_width": "3.0", "petal_length": "5.2", "petal_width": "2.0", "species": "virginica"},
    {"sepal_length": "6.2", "sepal_width": "3.4", "petal_length": "5.4", "petal_width": "2.3", "species": "virginica"},
    {"sepal_length": "5.9", "sepal_width": "3.0", "petal_length": "5.1", "petal_width": "1.8", "species": "virginica"}
]

irisTesting = [
    {"sepal_length": "5.4", "sepal_width": "3.9", "petal_length":"", "petal_width":"", "species":"",},
    {"sepal_length": "5.9", "sepal_width": "3", "petal_length":"", "petal_width":"", "species":"",},
    {"sepal_length": "5.6", "sepal_width": "", "petal_length":"", "petal_width":"", "species":"",}
]

// convert/setup our data
const trainingData = tf.tensor2d(iris.map(item => [
  parseFloat(item.sepal_length), parseFloat(item.sepal_width), parseFloat(item.petal_length), parseFloat(item.petal_width),
]))
const outputData = tf.tensor2d(iris.map(item => [
  item.species === "setosa" ? 1 : 0,
  item.species === "virginica" ? 1 : 0,
  item.species === "versicolor" ? 1 : 0,
]))
const testingData = tf.tensor2d(irisTesting.map(item => [
  parseFloat(item.sepal_length), parseFloat(item.sepal_width), parseFloat(item.petal_length), parseFloat(item.petal_width),
]))

// build neural network
const model = tf.sequential()

model.add(tf.layers.dense({
  inputShape: [4],
  activation: "sigmoid",
  units: 5,
}))
model.add(tf.layers.dense({
  inputShape: [5],
  activation: "sigmoid",
  units: 3,
}))
model.add(tf.layers.dense({
  activation: "sigmoid",
  units: 3,
}))
model.compile({
  loss: "meanSquaredError",
  optimizer: tf.train.adam(.06),
})
// train/fit our network
const startTime = Date.now()
model.fit(trainingData, outputData, {epochs: 100})
  .then((history) => {
    // console.log(history)
    model.predict(testingData).print()
  })
// test network

如果真的認真看到這裡的話請按 f12 看看 console 有沒有一個長得很像:

// 哈哈哈 用你電腦 train 了一個小 model 然後又順便測試了一下 這 model OK 不 OK~~ YA!
Tensor
     [[0.9712404, 0.001853 , 0.088761 ],
     [0.0020284, 0.9328192, 0.1161195],
     [0.0264381, 0.0329727, 0.8904994]]

跟我們 Testing 比較:

上面 Tensor 第一個row 的結果大約趨近於 [1, 0 ,0]
上面 Tensor 第二個row 的結果大約趨近於 [0, 1 ,0]

上面 Tensor 第三個row 的結果大約趨近於 [0, 0 ,1]

irisTesting = [
  {"sepal_length": "5.4", "sepal_width": "3.9", "petal_length":"1.7", "petal_width":"0.4", "species":"setosa",},
  {"sepal_length": "5.9", "sepal_width": "3", "petal_length":"5.1", "petal_width":"1.8", "species":"virginica",},
  {"sepal_length": "5.6", "sepal_width": "2.9", "petal_length":"4.2", "petal_width":"1.3", "species":"versicolor",}
]

恩~~ 很棒棒測試資料都正確:)

資料很重要! 好的、乾淨的、整理過的資料，對訓練 model 就跟呼吸一樣平常且重要! 重要! 重要!

所以多看一下IRIS 資料的 TABLE 吧 XDD

sepal.length	sepal.width	petal.length	petal.width	variety
5.1	3.5	1.4	.2	“Setosa”
4.9	3	1.4	.2	“Setosa”
4.7	3.2	1.3	.2	“Setosa”
4.6	3.1	1.5	.2	“Setosa”
5	3.6	1.4	.2	“Setosa”
5.4	3.9	1.7	.4	“Setosa”
4.6	3.4	1.4	.3	“Setosa”
5	3.4	1.5	.2	“Setosa”
4.4	2.9	1.4	.2	“Setosa”
4.9	3.1	1.5	.1	“Setosa”
5.4	3.7	1.5	.2	“Setosa”
4.8	3.4	1.6	.2	“Setosa”
4.8	3	1.4	.1	“Setosa”
4.3	3	1.1	.1	“Setosa”
5.8	4	1.2	.2	“Setosa”
5.7	4.4	1.5	.4	“Setosa”
5.4	3.9	1.3	.4	“Setosa”
5.1	3.5	1.4	.3	“Setosa”
5.7	3.8	1.7	.3	“Setosa”
5.1	3.8	1.5	.3	“Setosa”
5.4	3.4	1.7	.2	“Setosa”
5.1	3.7	1.5	.4	“Setosa”
4.6	3.6	1	.2	“Setosa”
5.1	3.3	1.7	.5	“Setosa”
4.8	3.4	1.9	.2	“Setosa”
5	3	1.6	.2	“Setosa”
5	3.4	1.6	.4	“Setosa”
5.2	3.5	1.5	.2	“Setosa”
5.2	3.4	1.4	.2	“Setosa”
4.7	3.2	1.6	.2	“Setosa”
4.8	3.1	1.6	.2	“Setosa”
5.4	3.4	1.5	.4	“Setosa”
5.2	4.1	1.5	.1	“Setosa”
5.5	4.2	1.4	.2	“Setosa”
4.9	3.1	1.5	.2	“Setosa”
5	3.2	1.2	.2	“Setosa”
5.5	3.5	1.3	.2	“Setosa”
4.9	3.6	1.4	.1	“Setosa”
4.4	3	1.3	.2	“Setosa”
5.1	3.4	1.5	.2	“Setosa”
5	3.5	1.3	.3	“Setosa”
4.5	2.3	1.3	.3	“Setosa”
4.4	3.2	1.3	.2	“Setosa”
5	3.5	1.6	.6	“Setosa”
5.1	3.8	1.9	.4	“Setosa”
4.8	3	1.4	.3	“Setosa”
5.1	3.8	1.6	.2	“Setosa”
4.6	3.2	1.4	.2	“Setosa”
5.3	3.7	1.5	.2	“Setosa”
5	3.3	1.4	.2	“Setosa”
7	3.2	4.7	1.4	“Versicolor”
6.4	3.2	4.5	1.5	“Versicolor”
6.9	3.1	4.9	1.5	“Versicolor”
5.5	2.3	4	1.3	“Versicolor”
6.5	2.8	4.6	1.5	“Versicolor”
5.7	2.8	4.5	1.3	“Versicolor”
6.3	3.3	4.7	1.6	“Versicolor”
4.9	2.4	3.3	1	“Versicolor”
6.6	2.9	4.6	1.3	“Versicolor”
5.2	2.7	3.9	1.4	“Versicolor”
5	2	3.5	1	“Versicolor”
5.9	3	4.2	1.5	“Versicolor”
6	2.2	4	1	“Versicolor”
6.1	2.9	4.7	1.4	“Versicolor”
5.6	2.9	3.6	1.3	“Versicolor”
6.7	3.1	4.4	1.4	“Versicolor”
5.6	3	4.5	1.5	“Versicolor”
5.8	2.7	4.1	1	“Versicolor”
6.2	2.2	4.5	1.5	“Versicolor”
5.6	2.5	3.9	1.1	“Versicolor”
5.9	3.2	4.8	1.8	“Versicolor”
6.1	2.8	4	1.3	“Versicolor”
6.3	2.5	4.9	1.5	“Versicolor”
6.1	2.8	4.7	1.2	“Versicolor”
6.4	2.9	4.3	1.3	“Versicolor”
6.6	3	4.4	1.4	“Versicolor”
6.8	2.8	4.8	1.4	“Versicolor”
6.7	3	5	1.7	“Versicolor”
6	2.9	4.5	1.5	“Versicolor”
5.7	2.6	3.5	1	“Versicolor”
5.5	2.4	3.8	1.1	“Versicolor”
5.5	2.4	3.7	1	“Versicolor”
5.8	2.7	3.9	1.2	“Versicolor”
6	2.7	5.1	1.6	“Versicolor”
5.4	3	4.5	1.5	“Versicolor”
6	3.4	4.5	1.6	“Versicolor”
6.7	3.1	4.7	1.5	“Versicolor”
6.3	2.3	4.4	1.3	“Versicolor”
5.6	3	4.1	1.3	“Versicolor”
5.5	2.5	4	1.3	“Versicolor”
5.5	2.6	4.4	1.2	“Versicolor”
6.1	3	4.6	1.4	“Versicolor”
5.8	2.6	4	1.2	“Versicolor”
5	2.3	3.3	1	“Versicolor”
5.6	2.7	4.2	1.3	“Versicolor”
5.7	3	4.2	1.2	“Versicolor”
5.7	2.9	4.2	1.3	“Versicolor”
6.2	2.9	4.3	1.3	“Versicolor”
5.1	2.5	3	1.1	“Versicolor”
5.7	2.8	4.1	1.3	“Versicolor”
6.3	3.3	6	2.5	“Virginica”
5.8	2.7	5.1	1.9	“Virginica”
7.1	3	5.9	2.1	“Virginica”
6.3	2.9	5.6	1.8	“Virginica”
6.5	3	5.8	2.2	“Virginica”
7.6	3	6.6	2.1	“Virginica”
4.9	2.5	4.5	1.7	“Virginica”
7.3	2.9	6.3	1.8	“Virginica”
6.7	2.5	5.8	1.8	“Virginica”
7.2	3.6	6.1	2.5	“Virginica”
6.5	3.2	5.1	2	“Virginica”
6.4	2.7	5.3	1.9	“Virginica”
6.8	3	5.5	2.1	“Virginica”
5.7	2.5	5	2	“Virginica”
5.8	2.8	5.1	2.4	“Virginica”
6.4	3.2	5.3	2.3	“Virginica”
6.5	3	5.5	1.8	“Virginica”
7.7	3.8	6.7	2.2	“Virginica”
7.7	2.6	6.9	2.3	“Virginica”
6	2.2	5	1.5	“Virginica”
6.9	3.2	5.7	2.3	“Virginica”
5.6	2.8	4.9	2	“Virginica”
7.7	2.8	6.7	2	“Virginica”
6.3	2.7	4.9	1.8	“Virginica”
6.7	3.3	5.7	2.1	“Virginica”
7.2	3.2	6	1.8	“Virginica”
6.2	2.8	4.8	1.8	“Virginica”
6.1	3	4.9	1.8	“Virginica”
6.4	2.8	5.6	2.1	“Virginica”
7.2	3	5.8	1.6	“Virginica”
7.4	2.8	6.1	1.9	“Virginica”
7.9	3.8	6.4	2	“Virginica”
6.4	2.8	5.6	2.2	“Virginica”
6.3	2.8	5.1	1.5	“Virginica”
6.1	2.6	5.6	1.4	“Virginica”
7.7	3	6.1	2.3	“Virginica”
6.3	3.4	5.6	2.4	“Virginica”
6.4	3.1	5.5	1.8	“Virginica”
6	3	4.8	1.8	“Virginica”
6.9	3.1	5.4	2.1	“Virginica”
6.7	3.1	5.6	2.4	“Virginica”
6.9	3.1	5.1	2.3	“Virginica”
5.8	2.7	5.1	1.9	“Virginica”
6.8	3.2	5.9	2.3	“Virginica”
6.7	3.3	5.7	2.5	“Virginica”
6.7	3	5.2	2.3	“Virginica”
6.3	2.5	5	1.9	“Virginica”
6.5	3	5.2	2	“Virginica”
6.2	3.4	5.4	2.3	“Virginica”
5.9	3	5.1	1.8	“Virginica”

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阿葛廷

week1 ~ week4 review VS TensorFlow.js

Linear Regression

Neural Network

如果真的認真看到這裡的話請按 f12 看看 console 有沒有一個長得很像:

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當你覺得對方很Ｇ8討厭的時候，放面鏡子照照自己剛剛的行為先吧！

daily Programming: 寶哥出場品質保證 GitHub Copilot

兩洞兩三最後三週計畫！

工作筆記: 想要開發然後開不了結果亂開亂關不熟的 K8S XDDD

阿葛廷

Linear Regression

Neural Network

如果真的認真看到這裡的話 請按 f12 看看 console 有沒有一個長得很像:

You May Also Enjoy

當你覺得對方很Ｇ8討厭的時候，放面鏡子照照自己剛剛的行為先吧！

daily Programming: 寶哥出場 品質保證 GitHub Copilot

兩洞兩三最後三週計畫！

工作筆記: 想要開發然後開不了 結果亂開 亂關不熟的 K8S XDDD

如果真的認真看到這裡的話請按 f12 看看 console 有沒有一個長得很像:

daily Programming: 寶哥出場品質保證 GitHub Copilot

工作筆記: 想要開發然後開不了結果亂開亂關不熟的 K8S XDDD