https://github.com/wenkokke/starchild
F* library for verifying neural networks.
https://github.com/wenkokke/starchild
fstar neural-networks smt-solver
Last synced: 5 months ago
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F* library for verifying neural networks.
- Host: GitHub
- URL: https://github.com/wenkokke/starchild
- Owner: wenkokke
- License: other
- Archived: true
- Created: 2020-01-13T16:58:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-25T01:28:45.000Z (about 2 years ago)
- Last Synced: 2024-11-10T15:15:53.069Z (7 months ago)
- Topics: fstar, neural-networks, smt-solver
- Language: F*
- Homepage:
- Size: 16 MB
- Stars: 16
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# StarChild
StarChild is a library for leveraging the interactive theorem proving and SMT checking abilities of F* to verify properties of neural networks.
It currently supports feedforward neural networks using ReLU, sigmoid, and softmax activation functions.
Networks are written as follows:
```fsharp
val layer1 : layer 2 1
let layer1 = { weights = [[228300.0R /. 13.0R]; [228300.0R /. 13.0R]]
; biases = [~.337910.0R /. 13.0R]
; activation = Sigmoid
}
val model : network 2 1 1
let model = NLast layer1
```
Properties of the model can then be checked using F* assertions.
Let’s check if the model correctly implements the AND gate:
```fsharp
let _ = assert (run_network model [1.0R; 1.0R] == [1.0R])
let _ = assert (run_network model [0.0R; 1.0R] == [0.0R])
let _ = assert (run_network model [1.0R; 0.0R] == [0.0R])
let _ = assert (run_network model [0.0R; 0.0R] == [0.0R])
```
Yes! It correctly implements the AND gate!
However, is it robust?
What do we mean by robust?
That’s generally not an easy question.
For this AND gate, let’s take robustness to mean that if the input is within some epsilon of a 0.0 or 1.0, the gate still works:
```fsharp
let epsilon = 0.24R
let truthy x = dist x 1.0R <=. epsilon
let falsy x = dist x 0.0R <=. epsilon
let _ = assert (forall x1 x2. truthy x1 && truthy x2 ==> run_network model [x1; x2] == [1.0R])
let _ = assert (forall x1 x2. falsy x1 && truthy x2 ==> run_network model [x1; x2] == [0.0R])
let _ = assert (forall x1 x2. truthy x1 && falsy x2 ==> run_network model [x1; x2] == [0.0R])
let _ = assert (forall x1 x2. falsy x1 && falsy x2 ==> run_network model [x1; x2] == [0.0R])
```
The network we defined is robust around truthy and falsy inputs!
StarChild ships with a script which can help you convert a subset of Keras models to F* files.
It’s called `convert.py`, and you invoke it like this:
```bash
python convert.py \
-i models/Fashion_MNIST_PCA_100_ReLU_64_Softmax_10.h5 \
-o models/Fashion_MNIST_PCA_100_ReLU_64_Softmax_10.fst
```
Make sure to install the requirements first
StarChild also comes with two example models, trained on the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, using the `train_*.py` scripts.
Currently, F* chokes up when type checking the larger of the models.
---
You may also be interested in StarChild’s friend, [Lazuli](https://github.com/wenkokke/lazuli)!
1: Run `pip install -r requirements.txt`.