tf = require('@tensorflow/tfjs@1.5.1')
model = await tf
.loadLayersModel("https://karlo-emilo.github.io/drawingClassifiers/model.json");
predictions = {
predictButton; // Wait for the predicton button to be clicked!
return await model.predict(preprocessCanvas(canvas)).data(); // this will run whenever "tensor" updates
}
preprocessCanvas = (canvas) => {
// Preprocess image for the network
let tensor = tf.browser.fromPixels(canvas); // Shape: (300, 300, 3) - RGB image
//tensor = await tensor.data().forEachAsync(greyScale => Math.abs(greyScale - 255))
console.log(tensor.data());
tensor = tf.image.resizeNearestNeighbor(tensor, [28, 28]) // Shape: (28, 28, 3) - RGB image
tensor = tensor.mean(2) // Shape: (28, 28) - grayscale
// tensor.print()
// tensor = tensor.flatten() // to 1D array
// tensor = tensor.reshape(784)
tensor = tf.reshape(tensor, [1, 28*28])
tensor = tensor.toFloat(); // Network works with floating points inputs
console.log('input:', tensor.data());
return tensor;
}
result = tf.argMax(predictions).data(); // this will run whenever "predictions" updates
bestGuess = labels[result]; // this will run whenever "result" updates
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.
