如何在 nodejs (tensorflow.js) 中训练模型? [英] How to train a model in nodejs (tensorflow.js)?

问题描述

我想做一个图像分类器，但我不会python.Tensorflow.js 使用我熟悉的 javascript.可以用它训练模型吗?这样做的步骤是什么?坦率地说，我不知道从哪里开始.

I want to make a image classifier, but I don't know python. Tensorflow.js works with javascript, which I am familiar with. Can models be trained with it and what would be the steps to do so? Frankly I have no clue where to start.

我唯一想到的是如何加载mobilenet"，它显然是一组预先训练好的模型，并用它对图像进行分类:

The only thing I figured out is how to load "mobilenet", which apparently is a set of pre-trained models, and classify images with it:

const tf = require('@tensorflow/tfjs'),
      mobilenet = require('@tensorflow-models/mobilenet'),
      tfnode = require('@tensorflow/tfjs-node'),
      fs = require('fs-extra');

const imageBuffer = await fs.readFile(......),
      tfimage = tfnode.node.decodeImage(imageBuffer),
      mobilenetModel = await mobilenet.load();  

const results = await mobilenetModel.classify(tfimage);

可行，但对我来说没有用，因为我想使用带有我创建的标签的图像来训练我自己的模型.

which works, but it's no use to me because I want to train my own model using my images with labels that I create.

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假设我有一堆图片和标签.我如何使用它们来训练模型?

Say I have a bunch of images and labels. How do I use them to train a model?

const myData = JSON.parse(await fs.readFile('files.json'));

for(const data of myData){
  const image = await fs.readFile(data.imagePath),
        labels = data.labels;

  // how to train, where to pass image and labels ?

}

推荐答案

首先，需要将图像转换为张量.第一种方法是创建一个包含所有特征的张量(分别是一个包含所有标签的张量).只有当数据集包含很少的图像时，这才应该是可行的方法.

First of all, the images needs to be converted to tensors. The first approach would be to create a tensor containing all the features (respectively a tensor containing all the labels). This should the way to go only if the dataset contains few images.

  const imageBuffer = await fs.readFile(feature_file);
  tensorFeature = tfnode.node.decodeImage(imageBuffer) // create a tensor for the image

  // create an array of all the features
  // by iterating over all the images
  tensorFeatures = tf.stack([tensorFeature, tensorFeature2, tensorFeature3])

标签将是一个数组，指示每个图像的类型

The labels would be an array indicating the type of each image

 labelArray = [0, 1, 2] // maybe 0 for dog, 1 for cat and 2 for birds

现在需要创建标签的热编码

One needs now to create a hot encoding of the labels

 tensorLabels = tf.oneHot(tf.tensor1d(labelArray, 'int32'), 3);

一旦有了张量，就需要创建用于训练的模型.这是一个简单的模型.

Once there is the tensors, one would need to create the model for training. Here is a simple model.

const model = tf.sequential();
model.add(tf.layers.conv2d({
  inputShape: [height, width, numberOfChannels], // numberOfChannels = 3 for colorful images and one otherwise
  filters: 32,
  kernelSize: 3,
  activation: 'relu',
}));
model.add(tf.layers.flatten());
model.add(tf.layers.dense({units: 3, activation: 'softmax'}));

然后可以训练模型

model.fit(tensorFeatures, tensorLabels)

如果数据集包含大量图像，则需要创建一个 tfDataset.这个 answer 讨论了原因.

If the dataset contains a lot of images, one would need to create a tfDataset instead. This answer discusses why.

const genFeatureTensor = image => {
      const imageBuffer = await fs.readFile(feature_file);
      return tfnode.node.decodeImage(imageBuffer)
}

const labelArray = indice => Array.from({length: numberOfClasses}, (_, k) => k === indice ? 1 : 0)

function* dataGenerator() {
  const numElements = numberOfImages;
  let index = 0;
  while (index < numFeatures) {
    const feature = genFeatureTensor(imagePath) ;
    const label = tf.tensor1d(labelArray(classImageIndex))
    index++;
    yield {xs: feature, ys: label};
  }
}

const ds = tf.data.generator(dataGenerator);

并使用model.fitDataset(ds)训练模型

以上是在nodejs中训练的.要在浏览器中做这样的处理，genFeatureTensor 可以写成如下:

The above is for training in nodejs. To do such a processing in the browser, genFeatureTensor can be written as follow:

function load(url){
  return new Promise((resolve, reject) => {
    const im = new Image()
        im.crossOrigin = 'anonymous'
        im.src = 'url'
        im.onload = () => {
          resolve(im)
        }
   })
}

genFeatureTensor = image => {
  const img = await loadImage(image);
  return tf.browser.fromPixels(image);
}

需要注意的一点是，进行繁重的处理可能会阻塞浏览器中的主线程.这就是网络工作者发挥作用的地方.

One word of caution is that doing heavy processing might block the main thread in the browser. This is where web workers come into play.

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