Metadata-Version: 2.1
Name: mynn
Version: 0.9.3
Summary: A pure-Python neural network library
Home-page: https://github.com/davidmascharka/MyNN
Author: David Mascharka
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Requires-Dist: numpy (>=1.13)
Requires-Dist: mygrad (>=1.0)


MyNN is a simple NumPy-centric neural network library that builds on top of MyGrad. It provides
convenient wrappers for such functionality as

- Convenient neural network layers (e.g. convolutional, dense, batch normalization, dropout)
- Weight initialization functions (e.g. Glorot, He, uniform, normal)
- Neural network activation functions (e.g. elu, glu, tanh, sigmoid)
- Common loss functions (e.g. cross-entropy, KL-divergence, Huber loss)
- Optimization algorithms (e.g. sgd, adadelta, adam, rmsprop)

MyNN comes complete with several examples to ramp you up to being a fluent user of the library.
It was written as an extension to MyGrad for rapid prototyping of neural networks with minimal dependencies,
a clean codebase with excellent documentation, and as a learning tool.


