build a sequential model for transfert learning

mnist-sequential.js

async function buildModel(featureExtractor, units) {
      return tf.sequential({
        layers: [
          // Flattens the input to a vector so we can use it in a dense layer. While
          // technically a layer, this only performs a reshape (and has no training
          // parameters).
          // slice so as not to take the batch size
          tf.layers.flatten(
            { inputShape: featureExtractor.outputs[0].shape.slice(1) }),
          // add all the layers of the model to train
          tf.layers.dense({
            units: units,
            activation: 'relu',
            kernelInitializer: 'varianceScaling',
            useBias: true
          }),
          // Layer 2. The number of units of the last layer should correspond
          // to the number of classes we want to predict.
          tf.layers.dense({
            units: NUM_CLASSES,
            kernelInitializer: 'varianceScaling',
            useBias: false,
            activation: 'softmax'
          })
        ]
      });
    }