Understanding Placeholders in TensorFlow.js

Why TensorFlow.js Doesn’t Need Placeholders

In traditional TensorFlow (like TensorFlow 1.x), placeholders were used to define inputs that would be provided later during the execution phase. These were especially useful when you had a large dataset that couldn’t be processed all at once, or when building a dynamic computation graph. However, in TensorFlow.js, placeholders are not necessary because TensorFlow.js uses eager execution by default.

What Is Eager Execution?

Eager execution means that operations on tensors are executed immediately as they are called. You don’t need to define placeholders or feed data later. This makes working with tensors in TensorFlow.js much simpler, more interactive, and intuitive for both beginners and experts.

Example: No Need for Placeholders

In TensorFlow.js, you don’t need to set up placeholders like in older TensorFlow versions. Instead, you directly define tensors and perform operations on them. Here’s a basic example:

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

// Create a tensor directly (no placeholder required)
const inputTensor = tf.tensor([1, 2, 3]);

// Perform an operation (eager execution)
const result = inputTensor.add(10);
result.print();

Tensor
   [11, 12, 13]

In this example, we define a tensor inputTensor and perform an addition operation immediately using eager execution. There’s no need for placeholders because the operation is computed as soon as the code is run.