README.md

Neural Networks Experiments From Scratch

This repository contains code for creating and training neural networks using only basic libraries such as NumPy for array manipulation and numerical calculations.

Usage example

from nnkit.core.neuronet import DenseLayer, DenseNetwork
from nnkit.core.activations import Softmax, ReLU, Sigmoid, Tanh
from nnkit.core.losses import CrossEntropySoftmax
from nnkit.datasets import mnist
from nnkit.datasets.utils import DataLabelSet, one_hot
from nnkit.training.neurotrain import NetworkTrainer
from nnkit.training.update_rules import SGD, RPropPlus, IRPropPlus, RPropMinus, IRPropMinus
from nnkit.training.stopping import GLStoppingCriterion
from nnkit.training.metrics import Accuracy, MetricsEvaluator

if __name__ == '__main__':
    # Build Network
    net = DenseNetwork(
        DenseLayer(num_inputs=784, num_neurons=256, activation_function=Tanh()),
        DenseLayer(num_neurons=10, activation_function=Softmax())
    )

    # Load data / Data pre-processing
    (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
    train_images = train_images.reshape((60000, 28 * 28))
    train_images = (train_images.astype('float32') / 255)
    train_labels = one_hot(train_labels)
    test_images = test_images.reshape((10000, 28 * 28))
    test_images = test_images.astype('float32') / 255
    test_labels = one_hot(test_labels)

    # Training data / Validation data
    training_set = DataLabelSet(train_images, train_labels, batch_size=len(train_images), name='training')
    training_set, validation_set = training_set.split(
        split_factor=0.2,
        split_set_batch_size=len(train_images),
        split_set_name='validation'
    )

    # Train the network
    trainer = NetworkTrainer(
        net=net,
        update_rule=IRPropPlus(),
        loss_function=CrossEntropySoftmax(),
        metrics=[Accuracy()]
    )

    history = trainer.train_network(training_set, validation_set, epochs=30)

    # Test the network
    test_set = DataLabelSet(test_images, test_labels, batch_size=len(test_images), name='test')
    evaluator = MetricsEvaluator(net, metrics=[Accuracy()], loss_function=CrossEntropySoftmax())
    metrics = evaluator.compute_metrics(test_set)
    print(metrics)