Tensorflow.js is an open-source library developed by Google for running machine learning models and deep learning neural networks in the browser or node environment. It also helps the developers to develop ML models in JavaScript language and can use ML directly in the browser or in Node.js.
The tf.Tensor class.bufferSync() method is used to return a tf.TensorBuffer that holds the underlying data.
Syntax:
bufferSync()
Parameters:
- It takes no parameters
Return Value: It returns tf.TensorBuffer
Example 1:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" console.log(tf.tensor([1, 3, 5, 4, 2]).bufferSync()) |
Output:
{
"dtype": "float32",
"shape": [
5
],
"size": 5,
"values": {
"0": 1,
"1": 3,
"2": 5,
"3": 4,
"4": 2
},
"strides": []
}
Example 2:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" const a= tf.tensor2d([[0, 1], [2, 3]]) console.log(a.bufferSync()) |
Output:
{
"dtype": "float32",
"shape": [
2,
2
],
"size": 4,
"values": {
"0": 0,
"1": 1,
"2": 2,
"3": 3
},
"strides": [
2
]
}
Reference: https://js.tensorflow.org/api/latest/#tf.Tensor.bufferSync
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