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Neural Network in Python

An implementation of a Multi-Layer Perceptron, with forward propagation, back propagation using Gradient Descent, training usng Batch or Stochastic Gradient Descent

Use: myNN = MyPyNN(nOfInputDims, nOfHiddenLayers, sizesOfHiddenLayers, nOfOutputDims, alpha, regLambda) Here, alpha = learning rate of gradient descent, regLambda = regularization parameter

Example 1

from myPyNN import *
X = [0, 0.5, 1]
y = [0, 0.5, 1]
myNN = MyPyNN([1, 1, 1]]

Input Layer : 1-dimensional (Eg: X)

1 Hidden Layer : 1-dimensional

Output Layer : 1-dimensional (Eg. y)

Learning Rate : 0.05 (default)

print myNN.predict(0.2)

Example 2

X = [[0,0], [1,1]]
y = [0, 1]
myNN = MyPyNN([2, 3, 1])

Input Layer : 2-dimensional (Eg: X)

1 Hidden Layer : 3-dimensional

Output Layer : 1-dimensional (Eg. y)

Learning rate : 0.8

print myNN.predict(X)
#myNN.trainUsingGD(X, y, 899)
myNN.trainUsingSGD(X, y, 1000)
print myNN.predict(X)

Example 3

X = [[2,2,2], [3,3,3], [4,4,4], [5,5,5], [6,6,6], [7,7,7], [8,8,8], [9,9,9], [10,10,10], [11,11,11]]
y = [.2, .3, .4, .5, .6, .7, .8, .9, 0, .1]
myNN = MyPyNN([3, 10, 10, 5, 1])

Input Layer : 3-dimensional (Eg: X)

3 Hidden Layers: 10-dimensional, 10-dimensional, 5-dimensional

Output Layer : 1-dimensional (Eg. y)

Learning rate : 0.9

Regularization parameter : 0.5

print myNN.predict(X)
#myNN.trainUsingGD(X, y, 899)
myNN.trainUsingSGD(X, y, 1000)
print myNN.predict(X)

Requirements for interactive tutorial (myPyNN.ipynb)

I ran this in OS X, after installing brew for command-line use, and pip for python-related stuff.

Python

I designed the tutorial on Python 2.7, can be run on Python 3 as well.

Packages

  • numpy
  • matplotlib
  • ipywidgets

Jupyter

The tutorial is an iPython notebook. It is designed and meant to run in Jupyter. To install Jupyter, one can install Anaconda which would install Python, Jupyter, along with a lot of other stuff. Or, one can install only Jupyter using:

pip install jupyter

ipywidgets

ipywidgets comes pre-installed with Jupyter. However, widgets might need to be actived using:

jupyter nbextension enable --py widgetsnbextension
jupyter nbextension enable --py --sys-prefix widgetsnbextension

References