当将keras模型加载到tensorjs中时，它变得完全不准确 [英] When keras model is loaded into tensorjs, it becomes completely inaccurate

问题描述

我正在尝试为我在kaggle上找到的keras模型构建图像识别Web应用程序.我是这个东西的完整入门者.这是我第一次从事ML项目.该模型在keras/tensorflow中工作得很好(如果我的术语不准确，请原谅我)，但是当我通过tensorjs将模型加载到我的web应用程序中并进行预测时，即使使用训练数据，它也很不准确.我不知道到底发生了什么，但我有一种直觉，那就是它涉及到如何在Web应用程序中处理我的图像.我只是不知道我到底要改变什么.

I am trying to build an image recognition web app for a keras model that i found on kaggle. I'm a complete beginner to this stuff. This is my first time working on a ML project. The model works fairly well in keras/tensorflow (forgive me if my terminologies are inaccurate), but when i load the model into my webapp via tensorjs and make predictions, its hilariously inaccurate, even with training data. I dont know exactly what's going on, but I have a hunch that it involves how my image is being processed in the web app. I just dont know exactly what i have to change.

这是我的processImage代码

This is my processImage code

function processImage(image)
{

    let tensor = tf.browser.fromPixels(image)

    const resized = tf.image.resizeBilinear(tensor, [256, 256]).toFloat()

    const offset = tf.scalar(255.0);
    const normalized = tf.scalar(1.0).sub(resized.div(offset));

    const batched = normalized.expandDims(0);
    return batched;



} 


async function start()
{


    model=await tf.loadLayersModel('http://localhost:8013/pokemonClassifier/model/model.json');
    console.log(classNames.length)

    console.log($('#custom-text').text());

    if(model==undefined)
    {
        alert('No model present');
    }

    if($.trim($('#custom-text').text())=='No file chosen, yet.')
    {
        alert('please load an image before starting model');


    }
        let image=document.getElementById("preview");
        console.log(image);
        let tensor=processImage(image);

        let predictions= await model.predict(tensor).data();
        console.log(predictions);
        let results = Array.from(predictions)
        .map(function (p, i) {
          return {
            probability: p,
            className: classNames[i]
          };
        }).sort(function (a, b) {
          return b.probability - a.probability;
        }).slice(0, 5);

        alert(results[0].className);
        console.log(results);


}

最后，我用来在python中加载测试图像的代码.这是为我的模型格式化图像的方式.

and finally, the code I use to load test images in python. This is how images are formatted for my model.

def load_image(img_path, show=False):

    img = image.load_img(img_path, target_size=(256, 256))
    img_tensor = image.img_to_array(img)                    # (height, width, channels)
    img_tensor = np.expand_dims(img_tensor, axis=0)         # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)
    img_tensor /= 255.                                      # imshow expects values in the range [0, 1]

    if show:
        plt.imshow(img_tensor[0])                           
        plt.axis('off')
        plt.show()

    return img_tensor

我真的只需要告诉我我用于模型的load_image格式与我在javascript中使用的processImage代码之间的差异.我需要在javascript代码中添加或删除哪些内容，以便我的图片得到正确处理?

I really just need someone to tell me the discrepancies between the load_image formatting which I use for my model and the processImage code I used in javascript. What do I need to add or remove from the javascript code so that my image will be processed properly?

推荐答案

所应用的预处理在js和python中是不同的.

The preprocessing applied is different in js and python.

在python中

normalized = data / 255

和js

normalized = 1 - (data / 255)

要在js中进行相同的规范化，规范化应为:

To have the same normalization in js, the normalization should be:

const normalized = resized.div(offset)

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