README.md

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

Features

• Activation Functions

  • Linear

  • Sigmoid

  • Relu

  • Softmax

• Optimizer

  • Adam

• Loss Functions

  • Cross Entropy

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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

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Installation

• Python3.6+
• numpy

Clone

• Clone this repo to your local machine using

git clone https://github.com/SuyashMore/tinyNeuralNet

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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

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Reference

• Andrew NG's Neural Networks and Deep Learning Course

• 3blue1brown's Neural Network Series

License

• MIT license
• Copyright 2020 © SuyashMore.