ComputationalGraphPrimer

Name	ComputationalGraphPrimer JSON
Version	1.1.2 JSON
	download
home_page	https://engineering.purdue.edu/kak/distCGP/ComputationalGraphPrimer-1.1.2.html
Summary	An educational module meant to serve as a prelude to talking about automatic differentiation in deep learning frameworks (for example, as provided by the Autograd module in PyTorch)
upload_time	2023-02-02 17:56:35
maintainer	None
docs_url	None
author	Avinash Kak
requires_python	None
license	Python Software Foundation License
keywords	computing in a graph
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            

Consult the module API page at

      https://engineering.purdue.edu/kak/distCGP/ComputationalGraphPrimer-1.1.2.html

for all information related to this module, including information related
to the latest changes to the code.  

::

        from ComputationalGraphPrimer import *
        
        cgp = ComputationalGraphPrimer(
                       expressions = ['xx=xa^2',
                                      'xy=ab*xx+ac*xa',
                                      'xz=bc*xx+xy',
                                      'xw=cd*xx+xz^3'],
                       output_vars = ['xw'],
                       dataset_size = 10000,
                       learning_rate = 1e-6,
                       grad_delta    = 1e-4,
                       display_vals_how_often = 1000,
              )
        
        cgp.parse_expressions()
        cgp.display_network1()                                                                    
        cgp.gen_gt_dataset(vals_for_learnable_params = {'ab':1.0, 'bc':2.0, 'cd':3.0, 'ac':4.0})
        cgp.train_on_all_data()
        cgp.plot_loss()

          
            

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