pdfcoffee

}
Chapter 13
const container = {name: 'Accuracy', tab: 'Evaluation'};
tfvis.show.perClassAccuracy(container, classAccuracy, classNames);
labels.dispose();
async function showConfusion(model, data) {
const [preds, labels] = doPrediction(model, data);
const confusionMatrix = await tfvis.metrics.confusionMatrix(
labels, preds);
const container = {name: 'Confusion Matrix', tab: 'Evaluation'};
tfvis.render.confusionMatrix(
container, {values: confusionMatrix}, classNames);
labels.dispose();
}
Finally, the run() function will call all these functions in sequence to build an endto-end
ML pipeline:
import {MnistData} from './data.js';
async function run() {
const data = new MnistData();
await data.load();
await showExamples(data);
const model = getModel();
tfvis.show.modelSummary({name: 'Model Architecture'}, model);
await train(model, data);
await showAccuracy(model, data);
await showConfusion(model, data);
}
document.addEventListener('DOMContentLoaded', run);
Refreshing the browser location http://localhost:8000/index.html will invoke
the run() method above. The table below shows the model architecture, and the
plots below that show the progress of the training.
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