Skip to main content

Lib

Project description

This contains Feedforward and Recurrent neural networks

Features

  • Feedforward and Recurrent neural nets

  • Contains logistic, tanh, ReLU, softmax neurons

  • Standard loss functions (squared error, cross entropy, sparsity constraints), and support for custom loss functions

  • Different methods to initialize weights

  • Customizable connectivity between layers

  • Inputs can be generated dynamically by some other system (like a simulation)

Quick start

Install the package via:

pip install gaunn

Requirements

  • python 2.7 or 3.5

  • numpy 1.9.2

  • matplotlib 1.3.1

Project details


Download files

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

Source Distribution

gaunn-1.0.0.tar.gz (32.2 kB view details)

Uploaded Source

Built Distribution

gaunn-1.0.0-py2-none-any.whl (37.4 kB view details)

Uploaded Python 2

File details

Details for the file gaunn-1.0.0.tar.gz.

File metadata

  • Download URL: gaunn-1.0.0.tar.gz
  • Upload date:
  • Size: 32.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for gaunn-1.0.0.tar.gz
Algorithm Hash digest
SHA256 30d0320301bbdafbd24647f4eadea737a53341ed8d03b8420bb09dac9460d3d8
MD5 f2f93caa3869cb86ba71d83508c0471b
BLAKE2b-256 0ec8d5a040c7a94ea4654eea7f169248a67c2d380788410a8f87767c5853df16

See more details on using hashes here.

File details

Details for the file gaunn-1.0.0-py2-none-any.whl.

File metadata

File hashes

Hashes for gaunn-1.0.0-py2-none-any.whl
Algorithm Hash digest
SHA256 1de6b484362b25962fd5f3d7e9276a54237b2794a7bcd208ad9603821c0c9e71
MD5 3fc861f382306db802c519eb203ab240
BLAKE2b-256 fd64b17bb8123619f50069d8c51384a4a086fc7b93f7e5aaefdd0c8f56eb80fd

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page