Metadata-Version: 2.1
Name: mlgen
Version: 1.0.3
Summary: MLGen is a tool which helps you to generate machine learning code with ease.
Home-page: https://github.com/NebutechOpenSource/MLGen
Author: Nebutech
Author-email: mukundh.bhushan@nebutech.in
License: MIT
Description: # MLGen
        MlGen is a tool which helps you to generate machine learning code with ease.
        MLGen uses a ".mlm" file format which is a file with YML like syntax.
        This tool as of now supports keras and tensorflow2.0(not fully supported)
        
        `pip install mlgen`
        
        ## CLI commands--->
        To init files  
        `mlgen -i | --init <file name>`   
        To generate a specific template (optional)  
        `mlgen -g | --gen <neural network type> --backend | -be <lib to use> -t jupyter`  
        To generate the ml python file  
        `mlgen -r . `
        
        ## MLM file syntax --->
        
        **file**: name of the python file to be created
        
        **version**: version of python being used
        
        **backend**: which machine learning platform if to be used
        
        **gpu**: (bool) is gpu being used or not
        
        **data**: location of the dataset can be a URL/ folder location on machine
        
        **split**:(int) slipt in training and testing data. automatically converted to a decimal
        
        **coloumns_feature**: list of coloumns being used for the prediction
        
        **nill_data**: basic null data handling in non categorical datatypes. Available techiniques remove, mean, mode, median 
        
        **nill_data_categorical**: basic null data handling for categorical datatypes. Available techiniques remove, max, min
        
        
        **NeuralNetwork_type**: the type of neural network being used such as ANN, CNN, LSTM
        <pre>
        layer1:  
            number_neurons: (int) number of neurons  
            input_dim: input dimensions of the first layer  input 
            activation: activation function being used 
            dropout: (optional)  
               dropout: (int) dropout percentage  
               noise_shape: (int) noise shape (optional)  
               seed: (int) seed value (optional)  
        layer2:  
            number_neurons: (int) number of neurons  
            activation: activation function being used  
            dropout: (optional)  
                dropout: (int) dropout percentage  
                noise_shape: (int) noise shape (optional)  
                seed: (int) seed value (optional)  
        
        
        compile:  
            epochs: (int) number of epoch  
            batch_size: (int) batch size  
            verbose: (int) verbose value 0,1,2  
            optimizer:  optimizer being used  
            loss: loss type  
            metrics: (array)  
                - metrics type  
        
        
        checkpoint: (optional)  
            monitor: metrix type  
            verbose: (int) batch size  
            save_best_only: (bool)  
            mode: mode such as min max  
        
        
        save_model: (optional)  
            file: file name to save model in  
            save: save type. Available options weights and model
        </pre>
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
