Constant Tensor vs Variable Tensor in TensorFlow.js

What is the Difference Between a Constant Tensor and a Variable Tensor in TensorFlow.js?

In TensorFlow.js, you can store data as tensors, but not all tensors are the same. Two common types are constant tensors and variable tensors. While they may seem similar, they behave differently and are used in different situations. Let’s break it down!

Constant Tensor

A constant tensor is a tensor whose values never change after being created. Think of it like a fixed number or a piece of data that you don’t want to modify. Once you set the values, you can’t update or change them.

For example:

import * as tf from '@tensorflow/tfjs';

// Create a constant tensor
const constTensor = tf.tensor([1, 2, 3, 4]);
console.log(constTensor.toString());

In this case, constTensor is a constant tensor and its values [1, 2, 3, 4] will remain the same throughout the program. This is useful when you have data that doesn’t need to be changed, like a fixed input for a machine learning model.

Variable Tensor

A variable tensor, on the other hand, is a tensor that can be updated or modified. This makes it useful when you need to change the values during the program, like when training a machine learning model where the model’s weights get updated after each iteration.

You can create a variable tensor using the tf.variable() function:

const varTensor = tf.variable(tf.tensor([1, 2, 3, 4]));
console.log(varTensor.toString());

// Now let's update the variable tensor
varTensor.assign(tf.tensor([5, 6, 7, 8]));
console.log(varTensor.toString());

Here, varTensor starts with the values [1, 2, 3, 4], but after using the assign function, the values are updated to [5, 6, 7, 8]. This is great when you want to change the data as the program runs.

Why is This Important?

• Constant tensors are great for storing things like configuration settings, static inputs, or data that doesn’t need to change.
• Variable tensors are essential for machine learning models, where you need to update the model’s weights as it learns from the data.