PyZapo 🚀

PyZapo is a lightweight, object-oriented Deep Learning framework written completely from scratch using pure NumPy (without PyTorch) [ML / AI Engineering: PyTorch].

It features an in-built adaptive Adam optimizer right inside the layers, making neural network training fast, clean, and highly efficient without any extra boilerplate code [ML / AI Engineering: PyTorch].

Features ✨

• PyTorch-Style Syntax: Built-in __call__ allows you to invoke models and layers like functions (model(X)) [ML / AI Engineering: PyTorch].
• OOP Architecture: Inherit from Module to build complex custom neural networks [ML / AI Engineering: PyTorch].
• Built-in Adam Optimizer: Adaptive learning rate for each weight out of the box.
• Modern Activations: High-performance ReLU and Sigmoid layers.
• Loss Functions: MSELoss and binary cross-entropy (BCELoss) with clipping safety.
• Weights Management: Save and load your trained models instantly with .npz binary files.

Installation 📦

pip install pyzapo

Quick Start 💻

import numpy as np
import pyzapo as pz

# 1. Define your custom architecture
class MyCoolAi(pz.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = pz.Linear(2, 8)
        self.relu = pz.ReLU()
        self.fc2 = pz.Linear(8, 1)
        self.sigmoid = pz.Sigmoid()

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.sigmoid(out)
        return out

    def backward(self, loss_gradient):
        delta = self.sigmoid.backward(loss_gradient)
        delta = self.fc2.backward(delta)
        delta = self.relu.backward(delta)
        delta = self.fc1.backward(delta)
        return delta

# 2. Train and Save
X = np.array([[5.0, 1.0], [1.0, 50.0]])
y = np.array([[1.0], [0.0]])

model = MyCoolAi()
criterion = pz.BCELoss()

for epoch in range(1000):
    pred = model(X)
    loss = criterion(pred, y)
    
    loss_grad = criterion.backward()
    model.backward(loss_grad)
    model.step(lr=0.01)

model.save_weights("my_model.npz")