In this post, we'll learn how to use transfer learning to retrain a model created with TensorFlow.js and Node.js.
Transfer learning is a technique that allows us to use a pre-trained model and adapt it to our own data and task. This is useful when we don't have enough data to train a model from scratch or when we want to use a model that's already been trained on a similar task.
We'll be using a pre-trained model from the TensorFlow.js Model Zoo. The model we'll be using is a MobileNetV2 model that's been trained on the ImageNet dataset.
TensorFlow.js is a JavaScript library for training and deploying machine learning models in the browser and in Node.js.
Node.js is a JavaScript runtime for running server-side code.
A pre-trained model is a model that's been trained on a dataset.
ImageNet is a large dataset of images that's often used for training image classification models.
Transfer learning is a technique that allows us to use a pre-trained model and adapt it to our own data and task.
There are two ways to use transfer learning with TensorFlow.js:
To use a pre-trained model from the TensorFlow.js Model Zoo, you'll need to do the following:
When choosing a pre-trained model, you'll need to consider the following:
To load the model, you'll need to use the tf.loadModel
function. This function takes a URL or a tf.Model
instance.
To retrain the model, you'll need to use the tf.train
function. This function takes an tf.Model
instance and a tf.Tensor
instance. The tf.Tensor
instance contains the training data.
There are several benefits of using transfer learning:
There are several drawbacks of using transfer learning:
In this post, we've learned how to use transfer learning to retrain a model created with TensorFlow.js and Node.js.