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").
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.
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.