Skip to main content

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

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',
               output_vars = ['xw'],
               dataset_size = 10000,
               learning_rate = 1e-6,
               grad_delta    = 1e-4,
               display_vals_how_often = 1000,

cgp.gen_gt_dataset(vals_for_learnable_params = {'ab':1.0, 'bc':2.0, 'cd':3.0, 'ac':4.0})

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for ComputationalGraphPrimer, version 1.0.6
Filename, size File type Python version Upload date Hashes
Filename, size ComputationalGraphPrimer-1.0.6.tar.gz (66.3 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page