Published
Edited
Jun 14, 2019
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tf = require('@tensorflow/tfjs@0.15.3')
model = await tf
.loadModel("https://epfl-exts.github.io/react-course-project/public/model/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
.fromPixels(canvas) // Shape: (300, 300, 3) - RGB image
.resizeNearestNeighbor([28, 28]) // Shape: (28, 28, 3) - RGB image
.mean(2) // Shape: (28, 28) - grayscale
.expandDims(2) // Shape: (28, 28, 1) - network expects 3d values with channels in the last dimension
.expandDims() // Shape: (1, 28, 28, 1) - network makes predictions for "batches" of images
.toFloat(); // Network works with floating points inputs
return tensor.div(255.0); // Normalize [0..255] values into [0..1] range
}
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.
