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)
Project description
Consult the module API page at
https://engineering.purdue.edu/kak/distCGP/ComputationalGraphPrimer-1.1.3.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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