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        "author": "Nebutech",
        "author_email": "mukundh.bhushan@nebutech.in",
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            "Development Status :: 3 - Alpha",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Build Tools"
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        "description": "# MLGen\nMlGen is a tool which helps you to generate machine learning code with ease.\nMLGen uses a \".mlm\" file format which is a file with YML like syntax.\nThis tool as of now supports keras and tensorflow2.0(not fully supported)\n\n`pip install mlgen`\n\n## CLI commands--->\nTo init files  \n`mlgen -i | --init <file name>`   \nTo generate a specific template (optional)  \n`mlgen -g | --gen <neural network type> --backend | -be <lib to use> -t jupyter`  \nTo generate the ml python file  \n`mlgen -r . `\n\n## MLM file syntax --->\n\n**file**: name of the python file to be created\n\n**version**: version of python being used\n\n**backend**: which machine learning platform if to be used\n\n**gpu**: (bool) is gpu being used or not\n\n**data**: location of the dataset can be a URL/ folder location on machine\n\n**split**:(int) slipt in training and testing data. automatically converted to a decimal\n\n**coloumns_feature**: list of coloumns being used for the prediction\n\n**nill_data**: basic null data handling in non categorical datatypes. Available techiniques remove, mean, mode, median \n\n**nill_data_categorical**: basic null data handling for categorical datatypes. Available techiniques remove, max, min\n\n\n**NeuralNetwork_type**: the type of neural network being used such as ANN, CNN, LSTM\n<pre>\nlayer1:  \n    number_neurons: (int) number of neurons  \n    input_dim: input dimensions of the first layer  input \n    activation: activation function being used \n    dropout: (optional)  \n       dropout: (int) dropout percentage  \n       noise_shape: (int) noise shape (optional)  \n       seed: (int) seed value (optional)  \nlayer2:  \n    number_neurons: (int) number of neurons  \n    activation: activation function being used  \n    dropout: (optional)  \n        dropout: (int) dropout percentage  \n        noise_shape: (int) noise shape (optional)  \n        seed: (int) seed value (optional)  \n\n\ncompile:  \n    epochs: (int) number of epoch  \n    batch_size: (int) batch size  \n    verbose: (int) verbose value 0,1,2  \n    optimizer:  optimizer being used  \n    loss: loss type  \n    metrics: (array)  \n        - metrics type  \n\n\ncheckpoint: (optional)  \n    monitor: metrix type  \n    verbose: (int) batch size  \n    save_best_only: (bool)  \n    mode: mode such as min max  \n\n\nsave_model: (optional)  \n    file: file name to save model in  \n    save: save type. Available options weights and model\n</pre>\n\n",
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