neuralnetworks-shine7 0.0.2

Feed Forward Neural Networks

Feed Forward Neural Networks using NumPy

This library is a modification of my previous one. Click Here to check my previous library.

Installation

$ [sudo] pip3 install neuralnetworks-shine7

Development Installation

$ git clone https://github.com/Subhash3/Neural_Net_Using_NumPy.git

Usage

>>> from Model import NeuralNetwork

Create a Neural Network

inputs = 2
outputs = 1
network = NeuralNetwork(inputs, outputs)

# Add 2 hidden layers with 16 neurons each and activation function 'tanh'
network.addLayer(16, activation_function="tanh") 
network.addLayer(16, activation_function="tanh")

# Finish the neural network by adding the output layer with sigmoid activation function.
network.compile(activation_function="sigmoid")

Building a dataset

The package contains a Dataset class to create a dataset.

>>> from Dataset import Dataset

Make sure you have inputs and target values in seperate files in csv format.

input_file = "inputs.csv"
target_file = "targets.csv"

# Create a dataset object with the same inputs and outputs defined for the network.
datasetCreator = Dataset(inputs, outputs)
datasetCreator.makeDataset(input_file, target_file)
data, size = datasetCreator.getRawData()

Training The network

The library provides a Train function which accepts the dataset, dataset size, and two optional parameters epochs, and logging.

def Train(dataset, size, epochs=5000, logging=True) :
	....
	....

For Eg: If you want to train your network for 1000 epochs.

>>> network.Train(data, size, epochs=1000, graph=True)

Notice that I didn't change the value of log_outputs as I want the output to printed for each epoch.

Debugging

Plot a nice epoch vs error graph

>>> network.epoch_vs_error()

Know how well the model performed.

>>> network.evaluate()