TINN

TINN acronym for Tiny Neural Network is a lightweight, neural network library,build over numpy.

Installation

You can download tinn using pip via pypi. $ pip install tinn

Getting Started

Creating a neural network

Lets start by creating a 3 layer neural network

First start with importing the required modules

    from tinn.neural import NeuralNet
    from tinn.layer import Layer

A neural network is composed of a series of layers of neurons, such that all the neurons in each layer connect to the neurons in the next layer.

Lets see how to make a layer using tinn.

A layer in tinn requires 2 parameters

• num_neurons : No of neurons in that layer
• activation : Activation function for that layer

Lets create a layer with 5 neurons and sigmoid activation function l1=Layer(5,'sigmoid')

Once the layer is created a neural network can be created by combining multiple layers using tinn.neural.NeuralNet class.

    model= NeuralNet() # Creates an empty neural network with 0 layers
    model.add(Layer(3,'sigmoid') # Hidden layer with 3 neurons
    model.add(Layer(5,'sigmoid') # Hidden layer with 5 neurons
    model.add(Layer(1,'sigmoid') # Outpput layer with1 neuron

Above code creates a 3 layered neural network with 2 hidden layers and 1 output layer.

Training the model

tinn.neural.NeuralNet.train() can be used to train the neural network on a given set of training data using stochastic gradient descent algorithm.

Here is the prototype of train method in NeuralNet class. def train(self,inputData,outputData,learning_rate=0.01,epocs=100,suffle=True)

• inputData : An array of all inputs of the training set.
• outputData : Array of corresponding outputs of the training set.
• learning_rate : Could be used to tweak the learning rate parameter
• epocs : Default epocs is 100, it denotes the number of traning iterations over the given dataset
• suffle : If set to false, dataset will not be shuffled between epocs.

Accuracy of the model

tinn.neural.NeuralNet.validate() is used to compute the accuracy of the model on given testing data. It returns a floating number between [0,1] inclusive where 1 represents 100 percent accuracy.

Prediction

Once the model is trained tinn.neural.NeuralNet.predict() can be used to get the predicted outputs from the trained neural network.

Saving the model

tinn.neural.NeuralNet.save() saves the model to a file.

    NeuralNet.save(self,filepath)

Saves the model along with weights and architecture ,in the specified file, uses pickle module of python.

Loading the model

Trained model can be loaded from the file using tinn.neural.NeuralNet.load() model=NeuralNet.load('handWrittenDigit.pkl') Once loaded the model can be use for prediction.