README.md

autograd-light

A lightweight implementation of pytorch's autograd. For education purposes. Inspired by Karpathy and Hotz.

It includes enough functions to create and train a fully connected neural network, from nothing but numpy. It uses the principle of automatic differentiation which underlies all widely used deep learning frameworks today.

Builds upon karpathy/micrograd. View geohot/tinygrad to see how you would expand on this further and then of course pytorch.

Installation

pip install autograd tests require python 3.8 (walrus)

Example

from autograd.tensor import Tensor
from autograd.other import SGD, NLLLoss, fetch_mnist, init_layer

class Net:
    def __init__(self):
        self.l1 = Tensor(init_layer(28*28, 800))
        self.l2 = Tensor(init_layer(800, 10))

    def forward(self, x):
        return x.dot(self.l1).relu().dot(self.l2).log_softmax()

    def parameters(self):
        return [self.l1, self.l2]
        
net = Net()
optim = SGD(net.parameters(), lr=1e-7)
loss_fn = NLLLoss
BS = 128

See test_net.py for full training loop. Achieves ~94% accuracy on MNIST.