Value Error For a JavaScript Async Function

Question

I have a value error with my JavaScript code on the first line of an async function, there is a value error, which I cannot seem to find the fix for, here is the code for the specific function that I'm having the issue with.

async function trainModel() {
  // Define the model architecture
 const model = tf.sequential();
 let inputs = [];
 let labels = [];


model.add(tf.layers.dense({ units: 8, inputShape: [8], activation: 'relu' }));
model.add(tf.layers.dense({ units: 8, activation: 'relu' }));
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));

model.compile({ loss: 'binaryCrossentropy', optimizer: 'adam', metrics: ['accuracy'] });

const batchSize = 32; // increase the batch size

const xs = tf.tensor(inputs);
const ys = tf.tensor(labels);

// Define callbacks to track the progress of the training

const callbacks = [
  tf.callbacks.earlyStopping(monitor='val_loss', patience=4),
];

// Train the model using fit

model.fit(xs, ys, {
    epochs: 1000,
    validationData: [xs, ys],
    callbacks: [earlyStop]
  });


  // Save the model to the filesystem
  await model.save('file://model');
}

trainModel();

I tried to enter the code into a linux shell program and I want this function to be free of errors so that I can integrate it into the rest of my code.

Answer 1

can you try code below?

async function trainModel() {
      // Define the model architecture
      const model = tf.sequential();
      let inputs = [];
      let labels = [];
    
      model.add(tf.layers.dense({ units: 8, inputShape: [8], activation: 'relu' }));
      model.add(tf.layers.dense({ units: 8, activation: 'relu' }));
      model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
    
      model.compile({ loss: 'binaryCrossentropy', optimizer: 'adam', metrics: ['accuracy'] });
    
      const batchSize = 32; // increase the batch size
    
      const xs = tf.tensor(inputs);
      const ys = tf.tensor(labels);
    
      // Define callbacks to track the progress of the training
      const earlyStop = tf.callbacks.earlyStopping(monitor='val_loss', patience=4);
      const callbacks = [earlyStop];
    
      // Train the model using fit
      model.fit(xs, ys, {
        epochs: 1000,
        validationData: [xs, ys],
        callbacks: callbacks
      });
    
      // Save the model to the filesystem
      await model.save('file://model');
    }
    
    trainModel();