shineflow 0.0.1

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 shineflow

Development Installation

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

Usage

>>> from shineflow import NeuralNetwork

Creating 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 shineflow 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()

If you want to manually make a dataset, follow these rules:

• Dataset must be a list of data samples.
• A data sample is a list containing inputs and target values.
• Input and target values are column vector of size (inputs x 1) and (outputs x 1) respectively.

For eg, a typical XOR data set looks something like :

>>> XOR_data = [
    [
        np.array([[0], [0]]),
        np.array([[0]])
    ],
    [
        np.array([[0], [1]]),
        np.array([[1]])
    ],
    [
        np.array([[1], [0]]),
        np.array([[1]])
    ],
    [
        np.array([[1], [1]]),
        np.array([[0]])
    ]
]
>>> size = 4

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)

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()

To take a look at all the layers' info

>>> network.display()

Sometimes, learning rate might have to be altered for better convergence.

>>> network.setLearningRate(0.1)

Exporting Model

You can export a trained model to a json file which can be loaded and used for predictions in the future.

filename = "model.json"
network.export_model(filename)

Load Model

To load a model from an exported model (json) file. load_model is a static function, so you must not call this on a NeuralNetwork object!.

filename = "model.json"
network = NeuralNetwork.load_model(filename)

Todo

- [ ] Data scaling
    - [x] Min Max scaler
    - [ ] Data Standardization
- [ ] Implement Cross Entropy Loss
- [ ] Linear and Relu activation function
- [ ] Ability to perform regression
- [ ] Convolution Nets
- [ ] Recurrent Nets