nnetflow 1.0.6

A minimal neural network framework with autodiff and NumPy

nnetflow

A minimal neural network framework with autodiff, inspired by micrograd and pytorch.

Installation

pip install nnetflow

import numpy as np
from nnetflow.nn import Conv2D, MaxPool2D, Linear, MLP, CrossEntropyLoss, Module
from nnetflow.engine import Tensor
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import time

# ----------- DataLoader -----------
def numpy_dataloader(batch_size=32, train=True):
    tf = transforms.Compose([
        transforms.ToTensor(),  # (0,1)
        transforms.Lambda(lambda x: x.numpy()),
    ])
    cifar = datasets.CIFAR10(root='./data', train=train, download=True, transform=tf)
    loader = DataLoader(cifar, batch_size=batch_size, shuffle=True)
    for imgs, labels in loader:
        imgs = imgs.numpy()
        labels = np.eye(10)[labels.numpy()]  # One-hot
        yield Tensor(imgs), Tensor(labels)

# ----------- Model Definition -----------
class SimpleCNN(Module):
    def __init__(self):
        super().__init__()
        self.conv1 = Conv2D(3, 8, kernel_size=3, stride=1, padding=1)
        self.pool = MaxPool2D(kernel_size=2)
        self.conv2 = Conv2D(8, 16, kernel_size=3, stride=1, padding=1)
        self.fc1 = Linear(16 * 8 * 8, 64, activation='relu')
        self.fc2 = Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.pool(x)
        B, C, H, W = x.data.shape
        x = x.reshape(B, C * H * W)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

# ----------- Training Function -----------
def train(model, epochs=5, lr=0.01, batch_size=32):
    loss_fn = CrossEntropyLoss()

    for epoch in range(epochs):
        total_loss = 0.0
        num_batches = 0
        start = time.time()

        for x, y in numpy_dataloader(batch_size=batch_size, train=True):
            out = model(x)
            loss = loss_fn(out, y)

            # Backward pass
            for p in model.parameters():
                p.grad = np.zeros_like(p.data)
            loss.backward()

            # SGD step
            for p in model.parameters():
                p.data -= lr * p.grad

            total_loss += loss.data.item() if hasattr(loss.data, 'item') else loss.data
            num_batches += 1

        print(f"[Epoch {epoch+1}] Loss: {total_loss/num_batches:.4f} Time: {time.time()-start:.2f}s")

# ----------- Accuracy Evaluation -----------
def evaluate(model):
    correct = 0
    total = 0
    for x, y in numpy_dataloader(train=False):
        out = model(x)
        preds = np.argmax(out.data, axis=-1)
        labels = np.argmax(y.data, axis=-1)
        correct += np.sum(preds == labels)
        total += x.data.shape[0]
    print(f"Accuracy: {(correct / total) * 100:.2f}%")

# ----------- Run Training -----------
if __name__ == "__main__":
    model = SimpleCNN()
    train(model, epochs=5, lr=0.01, batch_size=64)
    evaluate(model)

...

See the docs/ folder for more details.