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An educational module meant to serve as a prelude to talking about automatic differentiation in deep learning frameworks (such as in the Autograd module of PyTorch)

Project description

Consult the module API page at

https://engineering.purdue.edu/kak/distCGP/ComputationalGraphPrimer-1.0.5.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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ComputationalGraphPrimer-1.0.5.tar.gz (60.2 kB view hashes)

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