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https://github.com/leopiney/tensor-safe

A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.
https://github.com/leopiney/tensor-safe

ai deep-learning dependent-types functional-programming haskell haskell-library machine-learning

Last synced: 4 months ago
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A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.

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---



# Tensor Safe



`tensor-safe` is a dependently typed framework to define deep learning models whose structure is verified at

compilation time. If the models are valid, these can be compiled to external frameworks, such as Keras framework in Python or JavaScript.



## Install instructions



1. Install `tensor-safe` executable with `cabal new-install`



   `cabal new-install tensor-safe`



2. Install `tensor-safe` library



   `cabal new-install tensor-safe --lib`



## Building instructions and development tools



1. Install `ghc-mod`, `hpack` and `stylish-haskell` with `stack install`



   ```

   cd ~

   stack install ghc-mod hpack stylish-haskell

   ```



2. Run `stack build` in project folder

3. Install `Intero`



   Run `stack build intero` in the project folder



   Ref: https://gitlab.com/vannnns/haskero/blob/master/client/doc/installation.md



## Generate `.cabal` file



Run `hpack` in the root of the project and the file `tensor-safe.cabal` will be generated



## Model definition



Models can be defined as a type using the `MkINetwork` type function. The `MkINetwork` defines a

valid instance of a Network model given a list of `Layers` and a spected input and iutput `Shapes`.



Here's an example of how to define a simple model for the `MNIST` dataset, using `Dense` layers:



```haskell

type MNIST = MkINetwork

    '[

        Flatten,

        Dense 784 42,

        Relu,

        Dense 42 10,

        Sigmoid

    ]

    ('D3 28 28 1)    -- Input

    ('D1 10)         -- Output

```



After that, variable with the model type can be verified with the function `mkINetwork` like this:



```haskell

mnist :: MNIST

mnist = mkINetwork

```



## Nesting networks definitions



You can nest networks definitions easily by adding the networks as layers. For example, in the case of the `MNIST` model defined above, we can abstract the use of Dense and a activation function like this:



```haskell

type DenseRelu i o =

    MkINetwork '[ Dense i o, Relu ] ('D1 i) ('D1 o)



type DenseSigmoid i o =

    MkINetwork '[ Dense i o, Sigmoid ] ('D1 i) ('D1 o)



type MNIST = MkINetwork

    '[

        Flatten,

        DenseRelu 784 42,

        DenseSigmoid 42 10

    ]

    ('D3 28 28 1)    -- Input

    ('D1 10)         -- Output

```



## How to extend layers definitions



Since this library only implements a subset of features that Keras implement, it's likely that for

new projects you'll need to add new layers. Due to the modularization of the library, this can be

done by adding the layer definitions in specific locations of the project:



1. First, add a new auxiliary layer entry for the data type `DLayer` in `TensorSafe.Compile.Expr`.

   This will make possible the compilation of the layer for all instances of `Generator`. Also, add

   to the `LayerGenerator` entry for the newly added layer.

2. Secondly, add the layer definition to the `TensorSafe/Layers` folder. You can copy the

   definitions from the currently defined layers.

3. Then, import and expose your layer definition in the `TensorSafe.Layers` module.

4. Finally, declare how your layer transforms a specific Shape in the `Out` type function.



## Command line interface



> This interface will change in the near future



You can install `tensor-safe` command line tool by running `stack build`. Then you can use it by using `stack exec tensor-safe -- check --path ./path-to-model.hs` or `stack exec tensor-safe -- compile --path ./path-to-model.hs --module-name SomeModule`.



## Tools for JavaScript environment



Add as development dependency the packages `babel-plugin-tensor-safe` and `eslint-plugin-tensor-safe`. These can be found in the `extra/javascript` folder in this project.



You can add them directly from this project like this:



```bash

yarn add --dev file/:/extra/javascript/babel-plugin-tensor-safe



yarn add --dev file/:/extra/javascript/eslint-plugin-tensor-safe

```



Then add to the `.eslintrc.js` file in your JavaScript project the plugin `tensor-safe` and the rule `tensor-safe-model-invalid` like this:



```js

module.exports = {

  plugins: [

     ...

     "tensor-safe"

   ],

  ...

  rules: {

    ...

    "tensor-safe/invalid-model": 1

    ...

  }

};

```



And for the Babel plugin add `"@babel/plugin-tensor-safe"` to the plugins list in the `.babelrc` file inside your JavaScript project.



Then, you can write your deep learning model inside your JS files as in the following example:



```js

function createConvModel() {

  safeModel`

    '[

        Conv2D 1 16 3 3 1 1,

        Relu,

        MaxPooling 2 2 2 2,

        Conv2D 16 32 3 3 1 1,

        Relu,

        MaxPooling 2 2 2 2,

        Conv2D 32 32 3 3 1 1,

        Relu,

        Flatten,

        Dense 288 64,

        Sigmoid,

        Dense 64 10,

        Sigmoid

    ]

    ('D3 28 28 1)  -- Input

    ('D1 10)       -- Output

`;



  return model;

}

```



## Related projects



This project was highly influenced by [Grenade](https://github.com/HuwCampbell/grenade) 💣. Grenade is a cool library to define deep neural networks which are validated using dependent types. What differences TensorSafe from Grenade the most is that TensorSafe doesn't run nor train the models, instead, it compiles the model to external languages that are capable of performing all computations – like Keras for Python or JavaScript. Also, TensorSafe doesn't need to specifically declare all Shapes transformations for all the model layers, instead, it just needs the input and output Shapes to validate the model.



Another worth looking library is [TensorFlow for Haskell](https://github.com/tensorflow/haskell).

This library has all bindings for TensorFlow in C. The issue with this is that it doesn't perform

a lot of type checkings at compilation time. However, there's an open branch that uses dependent

types to solve many of these issues: https://github.com/helq/tensorflow-haskell-deptyped, but the

solution still seems rather complicated for real use.