How to create a model for csv data set and to calculate predicted result using TensorFlow JS - tensorflow

Question

I am new to TensorFlow JS.
I followed the TensorFlow JS documentation to create model and train it to calculate predicted result from the created model.
But I don't know how to train the created model for CSV file and calculate predicted result for two or more column in the CSV file.
Could anybody guide me with a sample for creating, training model with CSV file and to calculate the predicted result?
const csvUrl = 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';
function save(model) {
return model.save('downloads://boston_model');
}
function load() {
return tf.loadModel('indexeddb://boston_model');
}
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(([rawFeatures, rawLabel]) =>
// Convert rows from object form (keyed by column name) to array form.
[Object.values(rawFeatures), Object.values(rawLabel)])
.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
model.fitDataset(flattenedDataset, {
epochs: 10,
callbacks: {
onEpochEnd: async (epoch, logs) => {
console.log(epoch, logs.loss);
}
}
});
const savedModel=save(model);
}
run().then(() => console.log('Done'));

Answer 1

Using tf.data.csv you can train a model with a csv file.
However the browser cannot directly read a file. Thus, you have to serve the csv file on a local server
Update
Your model uses only a single perceptron. Using multiple perceptrons can help improving the accuracy of the model, ie adding multiple layers. You can have a look here as how it is done.