A pure-Python neural network library
Project description
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.
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