对于使用Tensorflow.js的MNIST数据集，丢失曲线在1.0处饱和]]

Question

data.js


    /**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

const IMAGE_SIZE = 784;
const NUM_CLASSES = 10;
const NUM_DATASET_ELEMENTS = 65000;

const TRAIN_TEST_RATIO = 5 / 6;

const NUM_TRAIN_ELEMENTS = Math.floor(TRAIN_TEST_RATIO * NUM_DATASET_ELEMENTS);
const NUM_TEST_ELEMENTS = NUM_DATASET_ELEMENTS - NUM_TRAIN_ELEMENTS;

const MNIST_IMAGES_SPRITE_PATH =
    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_images.png';
const MNIST_LABELS_PATH =
    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_labels_uint8';

/**
 * A class that fetches the sprited MNIST dataset and returns shuffled batches.
 *
 * NOTE: This will get much easier. For now, we do data fetching and
 * manipulation manually.
 */
export class MnistData {
  constructor() {
    this.shuffledTrainIndex = 0;
    this.shuffledTestIndex = 0;
  }

  async load() {
    // Make a request for the MNIST sprited image.
    const img = new Image();
    const canvas = document.createElement('canvas');
    const ctx = canvas.getContext('2d');
    const imgRequest = new Promise((resolve, reject) => {
      img.crossOrigin = '';
      img.onload = () => {
        img.width = img.naturalWidth;
        img.height = img.naturalHeight;

        const datasetBytesBuffer =
            new ArrayBuffer(NUM_DATASET_ELEMENTS * IMAGE_SIZE * 4);

        const chunkSize = 5000;
        canvas.width = img.width;
        canvas.height = chunkSize;

        for (let i = 0; i < NUM_DATASET_ELEMENTS / chunkSize; i++) {
          const datasetBytesView = new Float32Array(
              datasetBytesBuffer, i * IMAGE_SIZE * chunkSize * 4,
              IMAGE_SIZE * chunkSize);
          ctx.drawImage(
              img, 0, i * chunkSize, img.width, chunkSize, 0, 0, img.width,
              chunkSize);

          const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);

          for (let j = 0; j < imageData.data.length / 4; j++) {
            // All channels hold an equal value since the image is grayscale, so
            // just read the red channel.
            datasetBytesView[j] = imageData.data[j * 4] / 255;
          }
        }
        this.datasetImages = new Float32Array(datasetBytesBuffer);

        resolve();
      };
      img.src = MNIST_IMAGES_SPRITE_PATH;
    });

    const labelsRequest = fetch(MNIST_LABELS_PATH);
    const [imgResponse, labelsResponse] =
        await Promise.all([imgRequest, labelsRequest]);

    this.datasetLabels = new Uint8Array(await labelsResponse.arrayBuffer());

    // Create shuffled indices into the train/test set for when we select a
    // random dataset element for training / validation.
    this.trainIndices = tf.util.createShuffledIndices(NUM_TRAIN_ELEMENTS);
    this.testIndices = tf.util.createShuffledIndices(NUM_TEST_ELEMENTS);

    // Slice the the images and labels into train and test sets.
    this.trainImages =
        this.datasetImages.slice(0, IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
    this.testImages = this.datasetImages.slice(IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
    this.trainLabels =
        this.datasetLabels.slice(0, NUM_CLASSES * NUM_TRAIN_ELEMENTS);
    this.testLabels =
        this.datasetLabels.slice(NUM_CLASSES * NUM_TRAIN_ELEMENTS);
  }

  nextTrainBatch(batchSize) {
    return this.nextBatch(
        batchSize, [this.trainImages, this.trainLabels], () => {
          this.shuffledTrainIndex =
              (this.shuffledTrainIndex + 1) % this.trainIndices.length;
          return this.trainIndices[this.shuffledTrainIndex];
        });
  }

  nextTestBatch(batchSize) {
    return this.nextBatch(batchSize, [this.testImages, this.testLabels], () => {
      this.shuffledTestIndex =
          (this.shuffledTestIndex + 1) % this.testIndices.length;
      return this.testIndices[this.shuffledTestIndex];
    });
  }

  nextBatch(batchSize, data, index) {
    const batchImagesArray = new Float32Array(batchSize * IMAGE_SIZE);
    const batchLabelsArray = new Uint8Array(batchSize * NUM_CLASSES);

    for (let i = 0; i < batchSize; i++) {
      const idx = index();

      const image =
          data[0].slice(idx * IMAGE_SIZE, idx * IMAGE_SIZE + IMAGE_SIZE);
      batchImagesArray.set(image, i * IMAGE_SIZE);

      const label =
          data[1].slice(idx * NUM_CLASSES, idx * NUM_CLASSES + NUM_CLASSES);
      batchLabelsArray.set(label, i * NUM_CLASSES);
    }

    const xs = tf.tensor2d(batchImagesArray, [batchSize, IMAGE_SIZE]);
    const labels = tf.tensor2d(batchLabelsArray, [batchSize, NUM_CLASSES]);

    return {xs, labels};
  }
}


script.js


    import {MnistData} from './data.js';
var canvas,ctx,saveButton,clearButton;
var pos={x:0,y:0};
var rawImage;
var model;

function getModel()
{
    model=tf.sequential();
    model.add(tf.layers.conv2d({inputShape:[28,28,1],kernelSize:3,filters:8,activation:'relu'}));
    model.add(tf.layers.maxPooling2d({poolSize:[2,2]}));
    model.add(tf.layers.conv2d({filters:16,kernelSize:3,activation:'relu'}));
    model.add(tf.layers.maxPooling2d({poolSize:[2,2]}));
    model.add(tf.layers.flatten());
    model.add(tf.layers.dense({units:128,activation:'sigmoid'}));
    model.add(tf.layers.dense({units:10,activation:'softmax'}));

    model.compile({optimizer:tf.train.adam(),loss:'categoricalCrossentropy',metrics:['accuracy']});

    return model;

}

async function train(model,data){
    const metrics=['loss', 'val_loss', 'acc', 'val_acc'];
    const container={name:'Model training',styles:{height:'640px'}};
    const fitCallbacks=tfvis.show.fitCallbacks(container,metrics);

    const BATCH_SIZE = 512;
    const TRAIN_DATA_SIZE = 5500;
    const TEST_DATA_SIZE = 1000;

    const [trainXs,trainYs]=tf.tidy(()=>
                                   {
        const d=data.nextTrainBatch(TRAIN_DATA_SIZE);
        return[
            d.xs.reshape([TRAIN_DATA_SIZE,28,28,1]),
            d.labels
        ];
    });

    const [testXs,testYs]=tf.tidy(()=>{
        const d=data.nextTestBatch(TEST_DATA_SIZE);
        return[
            d.xs.reshape([TEST_DATA_SIZE,28,28,1]),
            d.labels
        ];
    });

    return model.fit(trainXs,trainYs,{
        batchSize:BATCH_SIZE,
        validationData:[testXs,testYs],
        epochs:20,
        shuffle:true,
        callbacks:fitCallbacks
    });

}

function setPosition(e){
    pos.x=e.clientX-100;
    pos.y=e.clientY-100;
}

function draw(e)
{
    if(e.buttons!=1)return ;
    ctx.beginPath();
    ctx.lineWidth=24;
    ctx.lineCap='round';
    ctx.strokeStyle='white';
    ctx.moveTo(pos.x,pos.y);
    setPosition(e);
    ctx.lineTo(pos.x,pos.y)
    ctx.stroke();

    rawImage.src=canvas.toDataURL('image/png');
}

function erase()
{
    ctx.fillStyle="black";
    ctx.fillRect(0,0,280,280);
}

function save()
{
    var raw=tf.browser.fromPixels(rawImage,1);
    var resized=tf.image.resizeBilinear(raw,[28,28]);
    var tensor=resized.expandDims(0);

    var prediction=model.predict(tensor);
    var pIndex=tf.argMax(prediction,1).dataSync();

    alert(pIndex);
}

function init()
{
    canvas=document.getElementById('canvas');
    rawImage=document.getElementById('canvasimg');
    ctx=canvas.getContext("2d");
    ctx.fillStyle="black";
    ctx.fillRect(0,0,280,280);
    canvas.addEventListener("mousemove",draw);
    canvas.addEventListener("mousedown",setPosition);
    canvas.addEventListener("mouseenter",setPosition);
    saveButton=document.getElementById('sb');
    saveButton.addEventListener("click",save);
    clearButton=document.getElementById('cb');
    clearButton.addEventListener("click",erase);


}

async function run()
{
    const data=new MnistData();
    await data.load();
    const model=getModel();
    tfvis.show.modelSummary({name:'Model Architecture'},model);
    await train(model,data);
    init();
    alert("Training is done, try classifying...");
}

document.addEventListener('DOMContentLoaded', run);

mnist.htm


    <html>
<head>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis"></script>
</head>
    <body>
    <h1> Handwritten character recognition</h1>
    <canvas id="canvas" width="280" height="280" style="position:absolute;top:100;left:100;border:8px solid;"></canvas>
    <img id="canvasimg" style="position:absolute;top:10%;left=52%;width:280;height=280;display:none;">
    <input type="button" value="classify" id="sb" size="48" style="position:absolute;top:400;left:100;">
    <input type="button" value="clear" id="cb" size="23" style="position:absolute;top:400;left:180;">

    <script src="data.js" type="module"></script>
    <script src="script.js" type="module"></script>
    </body>

</html>

我试图制作一个手写数字分类器，该分类器根据我们在网页画布上绘制的内容来识别数字。但是在训练时，我的损失曲线饱和到1.0，而我的准确度饱和到60％。因此，我尝试将128个节点密集层的激活功能从relu更改为Sigmoid。即使更改后，我的损失也达到了1.0。请帮帮我。

Answer 1

[已经定义了一些模型来对mnist数据集here和there进行分类。如果要重写自己的模型，则需要将其与用作基准的那些官方模型进行比较。

对于使用Tensorflow.js的MNIST数据集，丢失曲线在1.0处饱和]]

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对于使用Tensorflow.js的MNIST数据集，丢失曲线在1.0处饱和]]

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