Tensorflow.js tf.Sequential class .evaluateDataset() Method

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"

// Defining an array x
const Array_x = [
   [1, 1, 1, 1, 1, 1, 1, 1],
   [1, 1, 1, 1, 1, 1, 1, 1],
   [1, 1, 1, 1, 1, 1, 1, 1],
   [1, 1, 1, 1, 1, 1, 1, 1],
];

// Defining an array y
const Array_y = [1, 1, 1, 1];

// Defining dataset of x
const Dataset_x = tf.data.array(Array_x);

// Defining dataset of y
const Dataset_y = tf.data.array(Array_y);

// Defining dataset of xy using zip method
const Dataset_xy = tf.data.zip({xs: Dataset_x, ys: Dataset_y})
     .batch(5)
     .shuffle(3);

// Training Model 
const gfg = tf.sequential();
  
// Adding layer to model  
const layer = tf.layers.dense({units:1, 
               inputShape : [8]});
   gfg.add(layer);
    
// Compiling our model 
const config = {optimizer:'sgd', 
              loss:'meanSquaredError'};
  gfg.compile(config);

// Calling evaluateDataset() method
const res = await gfg.evaluateDataset(Dataset_xy, 
tf.ones([7, 10]), tf.ones([7, 1]), {
   batchSize: 5,
});

// Printing output
res.print();

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"

// Defining dataset of xy using zip method
const Dataset_xy = tf.data.zip({
    xs: tf.data.array([[1, 0, 1, 2, 1]]), 
    ys: tf.data.array([1, 2, 1, 3])}).batch(8);

// Training Model 
const gfg = tf.sequential();
  
// Adding layer to model  
const layer = tf.layers.dense({units:1, 
    inputShape : [5], activation: 'sigmoid'});

gfg.add(layer);
    
// Compiling our model 
const config = {optimizer:'sgd', 
              loss:'meanSquaredError'};

gfg.compile(config);

// Calling evaluateDataset() method
const res = await gfg.evaluateDataset(
    Dataset_xy, tf.truncatedNormal([7, 10]), 
    tf.randomNormal([7, 1]), 
    {batchSize: 2, steps: 1});

// Printing output
console.log(JSON.stringify(res));