This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.

Visit the project page or Read the documentation.

The code has been tested.

• Vanilla Backpropagation
• Backpropagation with classical momentum
• Backpropagation with Nesterov momentum
• RMSprop
• Adagrad
• Adam
• Resilient Backpropagation
• Scaled Conjugate Gradient
• SciPy’s Optimize

pip install nimblenet

• Python
• NumPy
• Optionally: SciPy

This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.

• Implemented with matrix operations to ensure high performance.
• Dropout regularization is available to reduce overfitting. Implemented as desribed here.
• Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
• PYPY friendly (requires pypy-numpy).
• Features a selection of cost functions (error functions) and activation functions

 from nimblenet.activation_functions import sigmoid_function
 from nimblenet.cost_functions import cross_entropy_cost
 from nimblenet.learning_algorithms import RMSprop
 from nimblenet.data_structures import Instance
 from nimblenet.neuralnet import NeuralNet
 dataset = [
 Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
 ]
 settings = {
 "n_inputs" : 2,
 "layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
 }
 network = NeuralNet( settings )
 training_set = dataset
 test_set = dataset
 cost_function = cross_entropy_cost
 RMSprop(
 network, # the network to train
 training_set, # specify the training set
 test_set, # specify the test set
 cost_function, # specify the cost function to calculate error
 )