# Flux.JS

Run Flux models in the browser, via tensorflow.js.

Note that if you get errors running this package, you may need to run `Pkg.checkout("ASTInterpreter2")`

.

## JS Output

You can see what Flux.JS sees with `@code_js`

, which works like `@code_typed`

or
`@code_native`

. Flux.JS simply accepts a function of arrays along with example
inputs, and generates JavaScript code for you. Here's the simplest possible
example:

```
julia> x = rand(10)
10-element Array{Float64,1}:
0.299338
⋮
0.267917
julia> @code_js identity(x)
let model = (function () {
let math = tf;
function model(kinkajou) {
return kinkajou;
};
model.weights = [];
return model;
})();
flux.fetchWeights("model.bson").then((function (ws) {
return model.weights = ws;
}));
```

You can see that there's some setup code as Flux.JS expects to load some weights
for a model. But the core of it is this function, which is exactly like the
`identity`

function in Julia.

```
function model(kinkajou) {
return kinkajou;
};
```

Let's try something more interesting; `f`

takes two arguments and multiplies
them.

```
julia> f(W,x) = W*x
julia> @code_js f(rand(5,10),rand(10))
let model = (function () {
let math = tf;
function model(bear, giraffe) {
return math.matrixTimesVector(bear, giraffe);
};
model.weights = [];
return model;
})();
flux.fetchWeights("model.bson").then((function (ws) {
return model.weights = ws;
}));
```

Because Flux models are just Julia functions, we can use the same macro with them too. You'll now notice that the weights are being used.

```
julia> m = Chain(Dense(10,5,relu),Dense(5,2),softmax)
julia> @code_js m(x)
let model = (function () {
let math = tf;
function badger(eland) {
return math.add(math.matrixTimesVector(model.weights[0], eland), model.weights[1]);
};
function chimpanzee(mongoose) {
return math.relu(math.add(math.matrixTimesVector(model.weights[2], mongoose), model.weights[3]));
};
function model(shark) {
return math.softmax(badger(chimpanzee(shark)));
};
model.weights = [];
return model;
})();
flux.fetchWeights("model.bson").then((function (ws) {
return model.weights = ws;
}));
```

There is also early support for RNNs (we compile stateful models directly, no unrolling).

```
julia> m = Chain(RNN(10,5))
julia> @code_js m(x)
let model = (function () {
let math = tf;
let init = [0.017732, 0.00991122, -0.00712077, -0.00161244, -0.00232475];
let states = init.slice();
function nightingale(seal, mongoose) {
return [seal, mongoose];
};
function cat(horse) {
let weasel = math.tanh(math.add(math.add(math.matrixTimesVector(model.weights[0], horse), math.matrixTimesVector(model.weights[1], states[0])), model.weights[2]));
let coati = nightingale(weasel, weasel);
states[0] = coati[1];
return coati[2];
};
function model(fish) {
return cat(fish);
};
model.reset = (function () {
states = init.slice();
return;
});
model.weights = [];
return model;
})();
flux.fetchWeights("model.bson").then((function (ws) {
return model.weights = ws;
}));
```

In general, the more useful entry point to the package is `FluxJS.compile`

.

`julia> FluxJS.compile("mnist", m, rand(10))`

This will produce two files in the current directory: (1) `mnist.js`

, which
contains the same JavaScript code as above; (2) `mnist.bson`

, which contains the
model weights in a JS-loadable format.

## Browser Setup

Firstly, you'll need the following scripts in your `<head>`

. The `flux.js`

script can be found here.

```
<head>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.9.0"></script>
<script src="https://unpkg.com/bson/browser_build/bson.js"></script>
<script src="flux.js"></script> <!-- Or embed the script directly -->
</head>
```

From here, you can either link the generated code as another script, or embed it
directly. In real applications you'll most likely want to wait on the
`fetchWeights`

promise, to avoid trying to use the model before it's ready.

```
<script>
let model = (function () {
let math = tf;
function model(kinkajou) {
return kinkajou;
};
model.weights = [];
return model;
})();
flux.fetchWeights("model.bson").then((function (ws) {
return model.weights = ws;
}));
</script>
```

In the page, you can run the model from the dev tools.

```
> x = tf.tensor([1,2,3,4,5,6,7,8,9,10])
Tensor {isDisposed: false, size: 10, shape: Array(1), dtype: "float32", strides: Array(0), …}
> await model(x).data()
Float32Array(25) [0.0262143611907959, -0.04852187633514404, …]
```

See the tensorflow.js docs for more information on how to work with its tensor objects.