如何基于Serverless云函数 SCF+Kaggle端到端验证码识别从训练到部署

如何基于Serverless云函数 SCF+Kaggle端到端验证码识别从训练到部署

随着互联网的快速发展，验证码识别技术在安全防护、身份验证等领域得到了广泛应用，本文将介绍如何基于Serverless云函数(SCF)和Kaggle数据集，实现一个端到端的验证码识别系统，从训练到部署。

环境准备

1、安装Node.js和npm

首先需要安装Node.js和npm,可以从官网下载并安装：https://nodejs.org/zh-cn/

2、安装AWS CLI

在命令行中输入以下命令安装AWS CLI:

npm install -g aws-cli 

3、配置AWS CLI

按照提示输入您的AWS Access Key ID和Secret Access Key。

数据集准备

1、下载Kaggle数据集

在Kaggle网站上搜索“验证码识别”，找到相关竞赛项目，下载数据集，数据集通常包括图片和对应的标签。

2、转换数据格式

使用Python的PIL库将图片转换为灰度格式，并将像素值归一化到0-1之间，将标签转换为整数编码。

模型训练

1、安装TensorFlow.js和Keras.js

在项目根目录下运行以下命令安装TensorFlow.js和Keras.js:

npm install @tensorflow/tfjs @tensorflow-models/mobilenet --save 

2、创建模型文件

在项目根目录下创建一个名为model.json的文件，内容如下：

{
  "model": {
    "inputs": [
      { "name": "input" }
    ],
    "outputs": [
      { "name": "output", "kernelName": "conv_1/Relu" },
      { "name": "output", "kernelName": "pool_3/MaxPool" },
      { "name": "output", "kernelName": "conv_5/Relu" },
      { "name": "output", "kernelName": "pool_7/MaxPool" }
    ],
    "variableReplacementParameters": {},
    "errorCorrectionVariables": [],
    "supportedBatchSizes": [1, 2, 4, 8],
    "inferenceEndpoints": ["input"],
    "platform": "serverless-cpu",
    "backendConfig": {},
    "compilationOptions": {},
    "modelTopology": [
      { "layer": 0, "type": "InputLayer", "name": null, "dtype": null, "shape": [ undefined, undefined, undefined, undefined] },
      { "layer": 1, "type": "Conv2D", "name": "conv_1", "kernelSize": [3,3], "strides": [1,1], "padding": 'valid', "dilationRates": [1,1], "channels": [32], "activation":"relu","useBiasTerm": true, "biasWeightInitializer":"zeros"},
      { "layer": 2, "type": "MaxPooling2D", "name": "pool_3", "poolSize": [2,2], "strides": [2,2],"padding":"valid"},
      { "layer": 3, "type": "Conv2D", "name": "conv_5", "kernelSize": [3,3], "strides": [1,1], "padding": 'valid', "dilationRates": [1,1], "channels": [64], "activation":"relu","useBiasTerm": true, "biasWeightInitializer":"zeros"},
      { "layer": 4, "type": "MaxPooling2D", "name": "pool_7", "poolSize": [2,2], "strides": [2,2],"padding":"valid"},
      { "layer": 5, "type": "Flatten", "name": null,"dtype":"float32","shape":[0][0][0][0]},
      { "layer": 6, "type": "Dense", "name": null,"units":[1024],"kernelInitializer":"glorotUniform","biasInitializer":"zeros","activation":"relu","useBiasTerm": true},
      { "layer": 7, "type:"Dense", name:null}, {"layer":"softmax","name":"output"}    ]    }    }    }    }    }    }         ```
3、将训练好的模型导出为TensorFlow.js格式： 
import * as tf from '@tensorflow/tfjs'; // Import the tensorflow library in node.js script running on serverless platform. This will be used to load the pre-trained model and make predictions on it. We are using mobilenet model here which is a lightweight version of the model trained by Google on their dataset of over a million images for mobile and web applications. It has been optimized for inference on devices with limited computational resources like mobile phones. The model is saved in json format in the same directory where this file is located. The model can be loaded using the following code snippet: const model = await tf.loadLayersModel('file://path/to/model.json'); const result = await model.predict(image); ```