├── .gitignore ├── ImageClassify ├── cat.jpg ├── index.html ├── model │ ├── group1-shard1of5 │ ├── group1-shard2of5 │ ├── group1-shard3of5 │ ├── group1-shard4of5 │ ├── group1-shard5of5 │ └── model.json ├── package-lock.json └── package.json ├── README.md ├── TensorFlow.js.png ├── TensorFlow.js.xd ├── code ├── app.js ├── index.html ├── package-lock.json └── package.json └── ppt ├── tensorflowjs课程介绍.pptx └── 理解张量.pptx /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .idea 3 | node_modules 4 | -------------------------------------------------------------------------------- /ImageClassify/cat.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/plter/tfjs_quick_start_course_2020/7cc4afb4a29a87c8bc9addef703127d3785a7a13/ImageClassify/cat.jpg -------------------------------------------------------------------------------- /ImageClassify/index.html: 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"^2.6.11" 10 | } 11 | } 12 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # tfjs_quick_start_course_2020 2 | tfjs极简入门视频教程 3 | 4 | # 视频教程 5 | 6 | [https://yunp.top/p/v/1515](https://yunp.top/p/v/1515) 7 | 8 | # 课程目录 9 | 10 | | 课程 | 课时 | 11 | | --- | --- | 12 | | 第一章 效果演示 | | 13 | | 第二章 课程介绍 | | 14 | | 第三章 手写识别实现 | | 15 | | 第四章 使用tfhub的模型 | | 16 | -------------------------------------------------------------------------------- /TensorFlow.js.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/plter/tfjs_quick_start_course_2020/7cc4afb4a29a87c8bc9addef703127d3785a7a13/TensorFlow.js.png -------------------------------------------------------------------------------- /TensorFlow.js.xd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/plter/tfjs_quick_start_course_2020/7cc4afb4a29a87c8bc9addef703127d3785a7a13/TensorFlow.js.xd -------------------------------------------------------------------------------- /code/app.js: -------------------------------------------------------------------------------- 1 | (function () { 2 | new Vue({ 3 | el: "#vueapp", 4 | 5 | data: { 6 | targetNum: 0, 7 | trainStatus: "", 8 | result: "" 9 | }, 10 | 11 | mounted() { 12 | let c2d = this.drawCanvasContext2d = this.$refs.drawCanvas.getContext("2d"); 13 | c2d.lineWidth = 20; 14 | c2d.lineCap = "round"; 15 | c2d.lineJoin = "round"; 16 | 17 | this.previewCanvasContext2d = this.$refs.previewCanvas.getContext("2d"); 18 | 19 | this.loadOrCreateModel(); 20 | }, 21 | 22 | 23 | methods: { 24 | 25 | async loadOrCreateModel() { 26 | try { 27 | this.model = await tf.loadLayersModel("localstorage://mymodel"); 28 | } catch (e) { 29 | console.warn("Can not load model from LocalStorage, so we create a new model"); 30 | this.model = tf.sequential({ 31 | layers: [ 32 | tf.layers.inputLayer({inputShape: [784]}), 33 | tf.layers.dense({units: 10}), 34 | tf.layers.softmax() 35 | ] 36 | }); 37 | } 38 | 39 | this.model.compile({ 40 | optimizer: 'sgd', 41 | loss: 'categoricalCrossentropy', 42 | metrics: ['accuracy'] 43 | }); 44 | }, 45 | 46 | canvasMouseDownHandler(e) { 47 | this.drawing = true; 48 | this.drawCanvasContext2d.beginPath(); 49 | this.drawCanvasContext2d.moveTo(e.offsetX, e.offsetY); 50 | }, 51 | 52 | canvasMouseMoveHandler(e) { 53 | if (this.drawing) { 54 | this.drawCanvasContext2d.lineTo(e.offsetX, e.offsetY); 55 | this.drawCanvasContext2d.stroke(); 56 | } 57 | }, 58 | 59 | canvasMouseUpHandler(e) { 60 | this.drawing = false; 61 | 62 | this.previewCanvasContext2d.fillStyle = "white"; 63 | this.previewCanvasContext2d.fillRect(0, 0, 28, 28); 64 | this.previewCanvasContext2d.drawImage(this.$refs.drawCanvas, 0, 0, 28, 28); 65 | }, 66 | 67 | btnClearCanvasClickedHandler(e) { 68 | this.drawCanvasContext2d.clearRect(0, 0, this.$refs.drawCanvas.width, this.$refs.drawCanvas.height); 69 | }, 70 | 71 | getImageData() { 72 | let imageData = this.previewCanvasContext2d.getImageData(0, 0, 28, 28); 73 | let pixelData = []; 74 | 75 | let color; 76 | for (let i = 0; i < imageData.data.length; i += 4) { 77 | color = (imageData.data[i] + imageData.data[i + 1] + imageData.data[i + 2]) / 3; 78 | pixelData.push(Math.round((255 - color) / 255)); 79 | } 80 | return pixelData; 81 | }, 82 | 83 | 84 | async btnTrainClickedHandler(e) { 85 | let data = this.getImageData(); 86 | console.log(this.targetNum); 87 | // [1,0,0,0,0,0,0,0,0,0] 88 | // [0,1,0,0,0,0,0,0,0,0] 89 | let targetTensor = tf.oneHot(parseInt(this.targetNum), 10); 90 | 91 | let self = this; 92 | console.log("Start training"); 93 | await this.model.fit(tf.tensor([data]), tf.tensor([targetTensor.arraySync()]), { 94 | epochs: 30, 95 | callbacks: { 96 | onEpochEnd(epoch, logs) { 97 | console.log(epoch, logs); 98 | self.trainStatus = `
Step: ${epoch}
Loss: ${logs.loss}
`; 99 | } 100 | } 101 | }); 102 | self.trainStatus = `
训练完成
`; 103 | console.log("Completed"); 104 | 105 | await this.model.save("localstorage://mymodel"); 106 | }, 107 | 108 | async btnPredictClickedHandler(e) { 109 | let data = this.getImageData(); 110 | let predictions = await this.model.predict(tf.tensor([data])); 111 | this.result = predictions.argMax(1).arraySync()[0]; 112 | } 113 | } 114 | }); 115 | })(); -------------------------------------------------------------------------------- /code/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | Title 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
15 |
16 |
17 | 此处写数字 18 |
19 |
20 | 24 |
25 | 27 |
28 |
29 |
图像数据预览
30 |
31 | 33 |
34 |
35 |
36 |
训练
37 |
38 | 关联数字： 39 | 40 | 41 | 42 |
43 |
44 |
45 |
46 |
识别
47 |
48 | 49 |
{{result}}
50 |
51 |
52 |
53 | 54 | 55 | 56 | -------------------------------------------------------------------------------- /code/package-lock.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "code", 3 | "version": "1.0.0", 4 | "lockfileVersion": 1, 5 | "requires": true, 6 | "dependencies": { 7 | "@tensorflow/tfjs": { 8 | "version": "1.4.0", 9 | "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-1.4.0.tgz", 10 | "integrity": "sha512-pvc7arrWZ0NNOpEFVL4e99Bj933ty3tmUR0xLnjKao3psts9xr7w0sfJpuSn98HKjz+dj87UEXZIi0fwsAZdwQ==", 11 | "requires": { 12 | "@tensorflow/tfjs-converter": "1.4.0", 13 | "@tensorflow/tfjs-core": "1.4.0", 14 | "@tensorflow/tfjs-data": "1.4.0", 15 | "@tensorflow/tfjs-layers": "1.4.0" 16 | } 17 | }, 18 | "@tensorflow/tfjs-converter": { 19 | "version": "1.4.0", 20 | "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-1.4.0.tgz", 21 | "integrity": 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