neupy 0.5.2

NeuPy is a Python library for Artificial Neural Networks and Deep Learning.

NeuPy v0.5.2

NeuPy is a Python library for Artificial Neural Networks. NeuPy supports many different types of Neural Networks from a simple perceptron to deep learning models.

Installation

$ pip install neupy

User Guide

• Install NeuPy

• Check the tutorials

• Learn more about NeuPy in the documentation

• Explore lots of different neural network algorithms.

• Read articles and learn more about Neural Networks.

Links

• Tutorials

• Documentation

• Articles

• Cheat sheet

• Open Issues

Articles

• Hyperparameter optimization for Neural Networks

• Visualize Backpropagation Algorithms

• MNIST classification

• Predict prices for houses in the area of Boston

• Password recovery

• Discrete Hopfield Network

Examples

Convolutional Neural Networks

• MNIST CNN

• CIFAR10 CNN

• Pre-trained AlexNet CNN

• Pre-trained VGG16 CNN

• Pre-trained VGG19 CNN

• Pre-trained SqueezeNet

• GoogleNet

• Inception v3

• ResNet 50

Reinforcement Learning

• Neural Network plays CartPole game

• Value Iteration Network (VIN)

Recurrent Neural Networks

• Reber Grammar Classification, sequence input and fixed-size output with GRU

• Generate Shakespeare text, sequence input and sequence output with LSTM

Autoencoders

• MNIST, Denoising Autoencoder

• MNIST, Convolutional Autoencoder

• MNIST, Stacked Convolutional Autoencoders

• MNIST, Variational Autoencoder

Boltzmann Machine

• Feature Learning from the MNIST Images, Restricted Boltzmann Machine (RBM)

• Gibbs sampling using face images, Restricted Boltzmann Machine (RBM)

MLP Neural Networks

• MNIST, Multilayer perceptron

• Rectangle images, Multilayer perceptron

• Boston House Price prediction, Hessian algorithm

• Learning Algorithms Visualization, Gradient Descent, Momentum, RPROP and Conjugate Gradient

• IMDB review classification using CBOW and RPROP MLP

• MLP with categorical and numerical features

Competitive Neural Networks

• Simple SOFM example

• Learning half-circle topology with SOFM

• Compare feature grid types for SOFM

• Compare weight initialization methods for SOFM

• Visualize digit images in 2D space with SOFM

• Embedding 30-dimensional dataset into 2D and building heatmap visualization for SOFM

• Reduce number of training samples in iris dataset with LVQ3

Neural Networks with Radial Basis Functions

• Classify iris dataset, Probabilistic Neural Network (PNN)

• Regression using Diabetes dataset, Generilized Neural Nerwork (GRNN)

• Music-Speech audio classification, Probabilistic Neural Network (PNN)

Memory based Neural Networks

• Sinus function approximation, CMAC

• Visualize Discrete Hopfield Neural Network energy function

• Password recovery, Discrete Hopfield Neural Network

Dependencies

• Python 2.7, 3.4, 3.5, 3.6

• Theano == 0.9.0

• NumPy >= 1.9.0

• SciPy >= 0.19.0

• Matplotlib >= 1.4.0

• graphviz == 0.5.1