/tinyNeuralNet

Simple Neural Network Library Built-in Python using Numpy

Primary LanguageJupyter NotebookMIT LicenseMIT

PR License

Tiny Neural Network

  • Simple and Modular Neural Network Library built in python built using just numpy.

  • Keras like Network Initialization.

  • Easy to understand and build upon.

Try the Network train and test on MNIST Dataset

Open In Colab

NN

Features

  • Activation Functions

    • Linear

    • Sigmoid

    • Relu

    • Softmax

  • Optimizer

    • Adam

  • Loss Functions

    • Cross Entropy

Network Initialization

import tinyNN as tnn
nn = tnn.NeuralNetwork()
nn.addLayer(2)                       #Input Layer (2 inputs)
nn.addLayer(6,tnn.activation_sigmoid)    #Hidden Dense Layer 
nn.addLayer(6,tnn.activation_sigmoid)    #Hidden Dense Layer 
nn.addLayer(6,tnn.activation_sigmoid)    #Hidden Dense Layer 
nn.addLayer(3,tnn.activation_softmax)    #Output Layer 
nn.compile(lr=1)

# To Train 
nn.fit(Xs,Ys,epochs=5)  #Train for 5 epochs

Installation

  • Python3.6+
  • numpy

Clone

  • Clone this repo to your local machine using
git clone https://github.com/SuyashMore/tinyNeuralNet

Implementation Details

  • Weights and biases are stored as numpy Matrices

Sample Activation Function Implementation

def activation_sigmoid(X,der=False):
    if not der:
        return np.divide(1, 1 + np.exp(-X) )
    else:
        #Return Derivative of the Activation 
        return np.multiply(X,(1-X))
  • der Flag Represents the derivative of the Activation Function used during BackProp

Reference

License