# Bridging deep learning and logical reasoning using a differentiable satisfiability solver

## SATNet

Bridging deep learning and logical reasoning using a differentiable satisfiability solver.

This repository contains the source code to reproduce the experiments in the ICML 2019 paper SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver by Po-Wei Wang, Priya L. Donti, Bryan Wilder, and J. Zico Kolter.

## What is SATNet

SATNet is a differentiable (smoothed) maximum satisfiability (MAXSAT) solver that can be integrated into the loop of larger deep learning systems. This (approximate) solver is based upon a fast coordinate descent approach to solving the semidefinite program (SDP) associated with the MAXSAT problem.

#### How SATNet works

A SATNet layer takes as input the discrete or probabilistic assignments of known MAXSAT variables, and outputs guesses for the assignments of unknown variables via a MAXSAT SDP relaxation with weights *S*. A schematic depicting the forward pass of this layer is shown below. To obtain the backward pass, we analytically differentiate through the SDP relaxation (see the paper for more details).

#### Overview of experiments

We show that by integrating SATNet into end-to-end learning systems, we can learn the logical structure of challenging problems in a minimally supervised fashion. In particular, we show that we can:

- Learn the
**parity function**using single-bit supervision (a traditionally hard task for deep networks) - Learn how to play
**9×9 Sudoku (original and permuted)**solely from examples. - Solve a
**"visual Sudoku"**problem that maps images of Sudoku puzzles to their associated logical solutions. (A sample "visual Sudoku" input is shown below.)

## Installation

### Via pip

```
pip install satnet
```

### From source

```
git clone https://github.com/locuslab/SATNet
cd SATNet && python setup.py install
```

#### Package Dependencies

```
conda install -c pytorch tqdm
```

### Via Docker image

```
cd docker
sh ./build.sh
sh ./run.sh
```

## Running experiments

### Jupyter Notebook and Google Colab

Jupyter notebook

and Google Colab

### Run them manually

#### Getting the datasets

The Sudoku dataset and Parity dataset can be downloaded via

```
wget -cq powei.tw/sudoku.zip && unzip -qq sudoku.zip
wget -cq powei.tw/parity.zip && unzip -qq parity.zip
```

#### Sudoku experiments (original, permuted, and visual)

```
python exps/sudoku.py
python exps/sudoku.py --perm
python exps/sudoku.py --mnist --batchSz=50
```

#### Parity experiments

```
python exps/parity.py --seq=20
python exps/parity.py --seq=40
```

## GitHub

### Comments

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