data scientist at codescene
Public
Edited
Aug 28, 2024
train = generateData(100)
XX = tf.tensor2d([[1,2,3],[4,5,6]])
model = {
// Create model, add layers.
const model = tf.sequential()
model.add(tf.layers.dense({
units: 2,
inputShape: [3]
}))
model.compile({
loss: 'meanSquaredError',
optimizer: 'sgd'
})
// Train model.
const [X, y] = train
model.fit(X, y, {
epochs: 100
})
//model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Model loss.
//model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
// Syntetic data.
//const xs = tf.tensor2d([-1, 0, 1, 2, 3, 4], [6, 1]);
//const ys = tf.tensor2d([-3, -1, 1, 3, 5, 7], [6, 1]);
//model.fit(xs, ys, {epochs: 250});
return model
}
paramTypes = xTypes.map(generateParams)
function truncateLayer(x) {
// Extract mu and sigma.
let [mus, sigmas] = x.unstack(1)
// Expand dimensions
mus = mus.expandDims(1)
sigmas = sigmas.expandDims(1)
// Apply activations.
mus = tf.sigmoid(mus)
sigmas = tf.softplus(sigmas)
}
tf.sigmoid(MU)
tf = require("@tensorflow/tfjs")
ran = require("ranjs")
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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.
