mlgen 1.0.5

MLGen is a tool which helps you to generate machine learning code with ease.

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

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