Attention

1. Choose your version: This project uses GPU for training by default. You can use the CPU version by replacing tensorflow-gpu==1.10.0 in the requirements.txt file with tensorflow==1.10.0

Ready to work

If you want to use the GPU for training, you must first install CUDA and cuDNN: https://www.tensorflow.org/install/install_sources#tested_source_configurations Please open the above link to view the version of CUDA and cuDNN corresponding to the current TensorFlow version.

Start

1. Install the python 3.6 environment (with pip)
2. Install virtualenv pip3 install virtualenv
3. Create a separate virtual environment for the project:

   virtualenv -p /usr/bin/python3 venv # venv is the name of the virtual environment.
   cd venv/ # venv is the name of the virtual environment.
   source bin/activate # to activate the current virtual environment.
   cd captcha_platform # captcha_platform is the project path.

4. pip install -r requirements.txt

Configuration

1. config.yaml - System Config

   # Device: [gpu:0, cpu:0] The default device is GPU.
   # - requirement.txt  -  GPU: tensorflow-gpu, CPU: tensorflow
   # - If you use the GPU version, you need to install some additional applications.
   # TrainRegex and TestRegex: Default matching apple_20181010121212.jpg file.
   # TrainsPath and TestPath: The local path of your training and testing set.
   System:
     NeuralNet: 'CNNNet'
     Device: 'gpu:0'
     DeviceUsage: 0.7
     TrainsPath: 'E:\Task\Trains\YourModelName'
     TrainRegex: '.*?(?=_.*\.)'
     TestPath: 'E:\Task\TestGroup\YourModelName'
     TestRegex: '.*?(?=_.*\.)'
   
   # SavedStep: A Session.run() execution is called a Step,
   # - Used to save training progress, Default value is 100.
   # TestNum: The number of samples for each test batch.
   # - A test for every saved steps.
   # CompileAcc: When the accuracy reaches the set threshold,
   # - the model will be compiled together each time it is archived.
   # - Available for specific usage scenarios.
   # EndAcc: Finish the training when the accuracy reaches [EndAcc*100]%.
   # EndStep: Finish the training when the step is greater than the [-1: Off, EndStep >0: On] step.
   # LearningRate: Find the fastest relationship between the loss decline and the learning rate.
   Trains:
     SavedStep: 100
     TestNum: 500
     CompileAcc: 0.8
     EndAcc: 0.95
     EndStep: -1
     LearningRate: 0.001

   There are several common examples of TrainRegex: i. apple_20181010121212.jpg

   .*?(?=_.*\.)
   

   ii apple.png

   .*?(?=\.)
   

2. model.yaml - Model Config

   # Convolution: The number of layers is at least 3.
   # - The number below corresponds to the size of each layer of convolution.
   # Provide flexible neural network construction,
   # Adjust the neural network structure that suits you best
   # [Convolution, Pool, Optimization: {Dropout}]
   CNNNet:
     Layer:
       - Convolution: 32
       - Pool: [1, 2, 2, 1]
       - Optimization: Dropout
       - Convolution: 64
       - Pool: [1, 2, 2, 1]
       - Optimization: Dropout
       - Convolution: 64
       - Pool: [1, 2, 2, 1]
       - Optimization: Dropout
     ConvCoreSize: 3
     FullConnect: 1024
   
   # ModelName: Corresponding to the model file in the model directory,
   # - such as YourModelName.pb, fill in YourModelName here.
   # CharSet: Provides a default optional built-in solution:
   # - [ALPHANUMERIC, ALPHANUMERIC_LOWER, ALPHANUMERIC_UPPER,
   # -- NUMERIC, ALPHABET_LOWER, ALPHABET_UPPER, ALPHABET]
   # - Or you can use your own customized character set like: ['a', '1', '2'].
   # ImageChannel: [1 - Gray Scale, 3 - RGB].
   # CharLength: Captcha Length.
   # CharExclude: CharExclude should be a list, like: ['a', '1', '2']
   # - which is convenient for users to freely combine character sets.
   # - If you don't want to manually define the character set manually,
   # - you can choose a built-in character set
   # - and set the characters to be excluded by CharExclude parameter.
   Model:
     ModelName: YourModelName
     ImageChannel: 1
     CharLength: 4
     CharSet: ALPHANUMERIC_LOWER
     CharExclude: []
   
   # Magnification: [ x2 -> from size(50, 50) to size(100,100)].
   # OriginalColor: [false - Gray Scale, true - RGB].
   # Binaryzation: [-1: Off, >0 and < 255: On].
   # Smoothing: [-1: Off, >0: On].
   # Blur: [-1: Off, >0: On].
   # Resize: [WIDTH, HEIGHT].
   Pretreatment:
     Magnification: 0
     OriginalColor: false
     Binaryzation: 240
     Smoothing: 3
     Invert: false
     Blur: 5
   #  Resize: [160, 60]

Tools

1. Pretreatment Previewer python -m tools.preview
2. Navigator (Currently only supports character set recommendations) python -m tools.navigator
3. Quantize python -m tools.quantize --input=***.pb --output=***.pb
4. PyInstaller Package

   pip install pyinstaller
   python -m tools.package
   

Run

1. python trains.py

License

This project use SATA License (Star And Thank Author License), so you have to star this project before using. Read the license carefully.

Introduction

https://www.jianshu.com/p/b1a5427db6e2