Engineer on the Shiny team at RStudio. Biostats Ph.D. from Vanderbilt.
model_predictions = {
++modelTracker.count
const numObs = inputData.xs.size,
ymin = inputData.ys.min(),
ymax = inputData.ys.max(),
yrange = ymax.sub(ymin),
ynormalized = inputData.ys.sub(ymin).div(yrange);
// We start by initializing weights and biases for our two layers:
// layer from 1d input to hidden layer
const w1_init_var = 2/(1 + hidden_size);
const W1 = dl.variable(dl.randomNormal([1,hidden_size], 0, Math.sqrt(w1_init_var)));
const b1 = dl.variable(dl.zeros([1,hidden_size]));
// layer from hidden layer to 1d output.
const w2_init_var = 2/(hidden_size + 1)
const W2 = dl.variable(dl.randomNormal([hidden_size, 1], 0, Math.sqrt(w2_init_var)));
const b2 = dl.variable(dl.zeros([1,1]));
// Next, we define out model. It takes a scalar input (because out input is 1d)
// and spits out a scalar output.
const netModel = x => dl.tidy(() => {
const first_layer = act_func(x.mul(W1).add(b1))
const second_layer = first_layer.matMul(W2).add(b2)
return second_layer.asScalar()
})
// The objective function: mean squared error between prediction and actual.
const loss = (preds, label) => preds.sub(label).square().mean()
// set our optimizer based on cell above
let optimizer;
switch(optimizer_type){
case 'sgd':
optimizer = dl.train.momentum(learningRate, momentum);
break;
case 'adam':
optimizer = dl.train.adam();
break;
case 'adamax':
optimizer = dl.train.adamax();
break;
}
// Train the model.
async function train(xs, ys, iterations){
for(let i=0; i<numObs; i++){
optimizer.minimize(() => loss(
netModel(extractObs(xs, i)),
extractObs(ys, i)
));
}
}
const runNumAtStart = modelTracker.count;
for (let iter = 0; iter < iterations; iter++) {
if(runNumAtStart !== modelTracker.count) break; // checks to see if we have reinstantiated new run
await train(inputData.xs, ynormalized, iterations);
await dl.nextFrame();
yield {iter, preds: []}
console.log(`Epoch ${iter} | Run ${modelTracker.count}`)
}
const modelOutput = [];
for(let i=0; i<numObs; i++){
modelOutput.push(netModel(extractObs(inputData.xs, i)).mul(yrange).add(ymin).dataSync()[0]);
}
yield {iter: iterations, preds:modelOutput}
try {
yield invalidation;
} finally {
W1.dispose(); b1.dispose();
W2.dispose; b2.dispose();
}
}
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
