Tensor Safe

tensor-safe is a framework to define deep learning models which structure is verified on compilation time. If the models are valid, these can be compiled to Keras framework in Python or JavaScript.

Instalation instructions

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:

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:

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:

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

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:

yarn add --dev file/:<path-to-tensor-safe>/extra/javascript/babel-plugin-tensor-safe

yarn add --dev file/:<path-to-tensor-safe>/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:

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:

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;
}