Function csv

Create a CSVDataset by reading and decoding CSV file(s) from provided URL or local path if it's in Node environment.

Note: If isLabel in columnConfigs is true for at least one column, the element in returned CSVDataset will be an object of {xs:features, ys:labels}: xs is a dict of features key/value pairs, ys is a dict of labels key/value pairs. If no column is marked as label, returns a dict of features only.

const csvUrl =
'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';

async function run() {
  // We want to predict the column "medv", which represents a median value of
  // a home (in $1000s), so we mark it as a label.
  const csvDataset = tf.data.csv(
    csvUrl, {
      columnConfigs: {
        medv: {
          isLabel: true
        }
      }
    });

  // Number of features is the number of column names minus one for the label
  // column.
  const numOfFeatures = (await csvDataset.columnNames()).length - 1;

  // Prepare the Dataset for training.
  const flattenedDataset =
    csvDataset
    .map(({xs, ys}) =>
      {
        // Convert xs(features) and ys(labels) from object form (keyed by
        // column name) to array form.
        return {xs:Object.values(xs), ys:Object.values(ys)};
      })
    .batch(10);

  // Define the model.
  const model = tf.sequential();
  model.add(tf.layers.dense({
    inputShape: [numOfFeatures],
    units: 1
  }));
  model.compile({
    optimizer: tf.train.sgd(0.000001),
    loss: 'meanSquaredError'
  });

  // Fit the model using the prepared Dataset
  return model.fitDataset(flattenedDataset, {
    epochs: 10,
    callbacks: {
      onEpochEnd: async (epoch, logs) => {
        console.log(epoch + ':' + logs.loss);
      }
    }
  });
}

await run();
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