Skip to content
A feed-forward neural network library that uses the computational graph approach to compute the gradients
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
datasets
doc
LICENSE
README.md
basic_driver.py
driver.py
layers.py
loss.py
network.py
optimizers.py
solver.py
util.py

README.md

Espalier

A feed-forward neural network library that uses the computational graph approach to compute the gradients. This library supports ANNs of arbitrary size as defined by the user.

Espalier is the horticultural and ancient agricultural practice of controlling woody plant growth for the production of fruit, by pruning and tying branches to a frame - Wikipedia

Computational Graph

Getting Started:

Prerequisites:

This implementation makes use of just Python and Numpy. Matplotlib was used for testing the network and plotting graphs to observe it's learning.

Activation functions:

  • Sigmoid
  • ReLU
  • Softmax
  • Linear (No activation, only linear transform)

Loss functions:

  • L1 Loss
  • L2 Loss
  • Cross Entropy
  • SVM Loss

Optimizers:

  • SGD
  • Momentum

Computational Graph

Contributing:

When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change. Ensure any install or build dependencies are removed before the end of the layer when doing a build. Update the README.md with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.

License:

This project is licensed under the MIT License - see the LICENSE.md file for details

(Computational graph image source: https://colah.github.io/)

You can’t perform that action at this time.