A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

Overview

micrograd

awww

A tiny Autograd engine (with a bite! :)). Implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API. Both are tiny, with about 100 and 50 lines of code respectively. The DAG only operates over scalar values, so e.g. we chop up each neuron into all of its individual tiny adds and multiplies. However, this is enough to build up entire deep neural nets doing binary classification, as the demo notebook shows. Potentially useful for educational purposes.

Installation

pip install micrograd

Example usage

Below is a slightly contrived example showing a number of possible supported operations:

from micrograd.engine import Value

a = Value(-4.0)
b = Value(2.0)
c = a + b
d = a * b + b**3
c += c + 1
c += 1 + c + (-a)
d += d * 2 + (b + a).relu()
d += 3 * d + (b - a).relu()
e = c - d
f = e**2
g = f / 2.0
g += 10.0 / f
print(f'{g.data:.4f}') # prints 24.7041, the outcome of this forward pass
g.backward()
print(f'{a.grad:.4f}') # prints 138.8338, i.e. the numerical value of dg/da
print(f'{b.grad:.4f}') # prints 645.5773, i.e. the numerical value of dg/db

Training a neural net

The notebook demo.ipynb provides a full demo of training an 2-layer neural network (MLP) binary classifier. This is achieved by initializing a neural net from micrograd.nn module, implementing a simple svm "max-margin" binary classification loss and using SGD for optimization. As shown in the notebook, using a 2-layer neural net with two 16-node hidden layers we achieve the following decision boundary on the moon dataset:

2d neuron

Tracing / visualization

For added convenience, the notebook trace_graph.ipynb produces graphviz visualizations. E.g. this one below is of a simple 2D neuron, arrived at by calling draw_dot on the code below, and it shows both the data (left number in each node) and the gradient (right number in each node).

from micrograd import nn
n = nn.Neuron(2)
x = [Value(1.0), Value(-2.0)]
y = n(x)
dot = draw_dot(y)

2d neuron

Running tests

To run the unit tests you will have to install PyTorch, which the tests use as a reference for verifying the correctness of the calculated gradients. Then simply:

python -m pytest

License

MIT

Owner
Andrej
I like to train Deep Neural Nets on large datasets.
Andrej
Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
Reformer, the efficient Transformer, in Pytorch

Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH

Phil Wang 1.8k Jan 06, 2023
Pytorch bindings for Fortran

Pytorch bindings for Fortran

Dmitry Alexeev 46 Dec 29, 2022
Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd

Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd

Fangjun Kuang 119 Jan 03, 2023
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

README TabNet : Attentive Interpretable Tabular Learning This is a pyTorch implementation of Tabnet (Arik, S. O., & Pfister, T. (2019). TabNet: Attent

DreamQuark 2k Dec 27, 2022
PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

PyTorch implementation of [1611.06440 Pruning Convolutional Neural Networks for Resource Efficient Inference] This demonstrates pruning a VGG16 based

Jacob Gildenblat 836 Dec 26, 2022
PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL components from published papers, standardized evaluation, and experiment management.

GCL: Graph Contrastive Learning Library for PyTorch 592 Jan 07, 2023
You like pytorch? You like micrograd? You love tinygrad! ❤️

For something in between a pytorch and a karpathy/micrograd This may not be the best deep learning framework, but it is a deep learning framework. Due

George Hotz 9.7k Jan 05, 2023
High-fidelity performance metrics for generative models in PyTorch

High-fidelity performance metrics for generative models in PyTorch

Vikram Voleti 5 Oct 24, 2021
Model summary in PyTorch similar to `model.summary()` in Keras

Keras style model.summary() in PyTorch Keras has a neat API to view the visualization of the model which is very helpful while debugging your network.

Shubham Chandel 3.7k Dec 29, 2022
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

News March 3: v0.9.97 has various bug fixes and improvements: Bug fixes for NTXentLoss Efficiency improvement for AccuracyCalculator, by using torch i

Kevin Musgrave 5k Jan 02, 2023
A PyTorch implementation of L-BFGS.

PyTorch-LBFGS: A PyTorch Implementation of L-BFGS Authors: Hao-Jun Michael Shi (Northwestern University) and Dheevatsa Mudigere (Facebook) What is it?

Hao-Jun Michael Shi 478 Dec 27, 2022
An optimizer that trains as fast as Adam and as good as SGD.

AdaBound An optimizer that trains as fast as Adam and as good as SGD, for developing state-of-the-art deep learning models on a wide variety of popula

LoLo 2.9k Dec 27, 2022
On the Variance of the Adaptive Learning Rate and Beyond

RAdam On the Variance of the Adaptive Learning Rate and Beyond We are in an early-release beta. Expect some adventures and rough edges. Table of Conte

Liyuan Liu 2.5k Dec 27, 2022
Unofficial PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training

Unofficial PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training

Martin Krasser 251 Dec 25, 2022
A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

Code release for "Bayesian Compression for Deep Learning" In "Bayesian Compression for Deep Learning" we adopt a Bayesian view for the compression of

Karen Ullrich 190 Dec 30, 2022
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

Pretrained models for Pytorch (Work in progress) The goal of this repo is: to help to reproduce research papers results (transfer learning setups for

Remi 8.7k Dec 31, 2022
A very simple and small path tracer written in pytorch meant to be run on the GPU

MentisOculi Pytorch Path Tracer A very simple and small path tracer written in pytorch meant to be run on the GPU Why use pytorch and not some other c

Matthew B. Mirman 222 Dec 01, 2022
Tacotron 2 - PyTorch implementation with faster-than-realtime inference

Tacotron 2 (without wavenet) PyTorch implementation of Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions. This implementati

NVIDIA Corporation 4.1k Jan 03, 2023
Code snippets created for the PyTorch discussion board

PyTorch misc Collection of code snippets I've written for the PyTorch discussion board. All scripts were testes using the PyTorch 1.0 preview and torc

461 Dec 26, 2022