SJNET 1.0.0

A neural network Framework

SJNET

This repository includes SJNET.py, which contains the Network and Layer classes.

These can be used to create and train custom neural network

Import SJNET

from SJNET import Network ,Layer

A new Network can be initialized by

# provide the dataset X and Y
# each element is Y must be an array [] , Y = [[y1..y1n],[y2..y2n]...[yN..yNn]]

network = Network(X=X,Y=Y,learningRate=0.0002,epoch=1000,errorThresh=3)

Declare or initialize layers with its coresponding arguments

• neuronCount : number of neurons required in the layer

• position : position of layer in network

  • Note : (1 for input layer and -1 for output layer)

• activation : required activation function , avilable("linear","relu")

#Note: position must be (1 for input layer and -1 for output layer)
inputLayer = Layer(neuronCount=2,position=1)
hidden = Layer(neuronCount=10,position=2,activation="linear")
hidden2 = Layer(neuronCount=4,position=3,activation="linear")
output = Layer(neuronCount=1,position=-1,activation="linear")

Add declared layers into the network

#add them in order (inputLayer->first , outputLayer->last)
network.add(layer=inputLayer)
network.add(layer=hidden)
network.add(layer=hidden2)
network.add(layer=output)

Compile or initilise the network setup

network.compile()

Train the model

network.Train()

Save the model

# saved as json
network.save(name="testModel")

Load the model

Model = {}
with open('./savedmodels/testModel.json', 'r') as file:
    Model = json.load(file)

#Loads the  network topology weights and biases 
network.loadNetwork(network=Model)

predict with model

pred_val = network.predict(inputvals=[3.1, 2.5])

How to train and save a model -> Training_and_Saving_Model.py

How to load and use the model -> Loading_Pre_Trained_Model.py

It uses stochastic gradient descent, just in case you are curious.