pileoffeather 0.3.1

Lightweight and easy to use ml library for small projects, create a neural network in minutes.

POF - pile of feather

This library is not an alternative to big ml library like pytorch or tensorflow, it lacks features and optimization, such as gpu support. The goal is to create a lightweight library of about 100 lines of code that is easy to use and quick to implement for creating small projects or experiments.

Documentation | Examples

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Install module

pip install pileoffeather

Examples

examples/simple_image_classifier - Handwritten digit image classifier, dataset is made out of 500 images of ones and 500 images of zeros taken from the mnist dataset. The first code snippet is defining the neural network model, uploading the dataset and than training the model

from pileoffeather import pof, pod
import numpy as np

#Define neural network model
model = pof.neuralNetwork(layers = [[400,"input"],[30,"relu"],[10,"relu"],[1,"sigmoid"]], name = "test1")

#Load the images for the dataset, 500 ones images and 500 zeros images
ones = pod.load(data_type = "image", color = "grayscale", folder = "ones", resize = (20,20))
zeros = pod.load(data_type = "image", color = "grayscale", folder = "zeros", resize = (20,20))
input = pod.merge(ones, zeros)

#Generate expected output, first 500 images should output 1, the other 500 0
output = np.concatenate((np.ones(500), np.zeros(500)))

#Train the neural network using backpropagation
pof.train(model, input, output, batch_size = 16, epoch_number = 100, rate = 0.6)

The second code snippet is testing the neural network on some examples

from pileoffeather import pof, pod

#Define neural network model
model = pof.neuralNetwork(load = "test1")

#Run model
input = pod.loadImage("example_image_one.png", (20,20), "grayscale")
output = model.run(input)

#Print result
print(round(output[0],3))

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examples/mnist - Training script for the mnist dataset, 2 epochs -> 20~ seconds on 12600k -> 96%+ accuracy on 10k-test dataset

from pileoffeather import pof, pod

#Define neural network model
model = pof.neuralNetwork(layers = [[784,""],[128,"relu"],[10,"sigmoid"]], name = "mnist")

#Upload mnist dataset
input_dataset = pod.load(data_type = "gz", path = "train-images-idx3-ubyte.gz", start_index = 16, input_number = 784, divide = 255)
output_dataset = pod.load(data_type = "gz", path = "train-labels-idx1-ubyte.gz", start_index = 8, one_hot = 10)

#Train the neural network using backpropagation
pof.train(model, input_dataset, output_dataset, batch_size = 12, epoch_number = 2, rate = 1)

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Documentation

The library is structured in 2 files, pof.py (pile of feather) is used to create and train neural networks, pod.py (pile of data) is used import your own data to generate a training dataset.

pof.py - neural network module

Install module

pip install pileoffeather

Import module

from pileoffeather import pof

Define neural network model, the available activation functions are "sigmoid","relu","leakyRelu"

model = pof.neuralNetwork(layers = [[400,""],[50,"relu"],[10,"relu"],[1,"sigmoid"]], name = "test1")

Save the model

model.save()

Load an exsisting model

model = pof.neuralNetwork(load = "test1")

Use the neural network

output = model.run(input)

Compute backpropagation for a single batch, model.computeBatch(batch_input, batch_output, batch_size, learning_rate)

model.computeBatch(batch_input, batch_output, 16, 0.3)

Method that contains model.computeBatch() in a loop so that it iterates over all the dataset for n epochs

pof.train(model, input_matrix, output_matrix, batch_size = 16, epoch_number = 100, rate = 0.03)

pod.py - data load module

Import module

from pileoffeather import pod

Load dataset of images from local folder

dataset = pod.load(data_type = "image", color = "grayscale", folder = "folder_name_containing_all_images", resize = (20,20))

Load dataset from gz file like mnist

#Load training input data of mnist, normalize input from 0 to 1 using divide = 255
dataset = pod.load(data_type = "gz", path = "train-images-idx3-ubyte.gz", start_index = 16, input_number = 784, divide = 255)
#Load training output data of mnist, use one_hot encoding to convert a decimal number to an array (pass total number of classes as parameter)
#4 -> [0,0,0,0,1,0,0,0,0,0] 0 -> [1,0,0,0,0,0,0,0,0,0]
dataset = pod.load(data_type = "gz", path = "train-labels-idx1-ubyte.gz", start_index = 8, one_hot = 10)

Load a single image to feed the neural network loadImage(name, resize, color)

input_vector = pod.loadImage("example_image.png", (20,20), "grayscale")

Convert neural network output to image and save, saveImage(neural_network_output, image_path, resize, color)

pod.saveImage(neural_network_output, "image_path_and_name", (20,20), "grayscale")