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-1.1.2.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()
