PyAiNetwork 0.1.6

A simple Python library for creating neural networks

PyAiNetwork

PyAiNetwork is an open-source Python library for creating and training neural networks with simple and beginner-friendly code.

Install:

pip install PyAiNetwork

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About

PyAiNetwork is designed to make neural network development simple.

Instead of writing hundreds of lines of code for neurons, layers, weights, and training, you can create a neural network with just a few commands.

The library is suitable for:

• Learning how neural networks work
• Creating simple AI projects
• Experimenting with custom network architectures
• Building your own AI systems

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Developers

Eyes Studio

Eyes Studio is an independent software developer focused on AI tools and open-source projects.

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Features

• ✅ Simple neural network API
• ✅ Multiple hidden layers
• ✅ GELU activation
• ✅ ReLU activation
• ✅ Sigmoid activation
• ✅ Built-in training
• ✅ Lightweight implementation
• ✅ Pure Python
• ✅ Open Source

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Installation

Install from PyPI:

pip install PyAiNetwork

Import:

from PyAiNetwork import Network

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Quick Start

Create your first neural network.

from PyAiNetwork import Network

net = Network(
    2,  # input neurons
    2,  # hidden layers
    4,  # neurons in every hidden layer
    1   # output neurons
)

result = net.forward([0.5, 1.0])

print(result)

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Training

Example:

from PyAiNetwork import Network

net = Network(2,1,4,1)

for i in range(100):
    net.train(
        [1,0],
        [1]
    )

print(net.forward([1,0]))

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Activation Functions

PyAiNetwork currently supports:

activition="gelu"

activition="relu"

activition="sigmoid"

Example:

net = Network(
    2,
    2,
    8,
    1,
    activition="relu"
)

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Network

Simple neural network.

Constructor:

Network(
    input_neorons,
    layers,
    neorons_on_layer,
    output_neorons,
    activition="gelu"
)

Parameters:

Parameter	Description
input_neorons	Number of input neurons
layers	Number of hidden layers
neorons_on_layer	Neurons in every hidden layer
output_neorons	Number of output neurons
activition	Activation function

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ProfNetwork

Advanced neural network.

Unlike Network, every hidden layer can have a different number of neurons.

Example:

from PyAiNetwork import ProfNetwork

net = ProfNetwork(
    2,
    [8,16,8],
    1
)

Architecture:

2 → 8 → 16 → 8 → 1

Constructor:

ProfNetwork(
    input_neorons,
    layers_neorons,
    output_neorons,
    activition="gelu"
)

Example:

net = ProfNetwork(
    3,
    [32,64,64,32],
    5
)

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Roadmap

Future versions may include:

• Adam optimizer
• Model saving/loading
• Batch training
• More activation functions
• Loss functions
• Better performance
• More neural network types

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License

MIT License

Copyright (c) 2026 Eyes Studio