Tensorflow.js tf.oneHot() Function
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.
The tf.oneHot() function is used to create a one-hot tf.Tensor. The locations represented by indices take the value as 1 (default value) also known as onValue, while all other locations take the value as 0( default Value) also known as offValue.
Syntax:
tf.oneHot (indices, depth, onValue, offValue)
Parameter: This function accepts three parameters which are illustrated below:
- indices It can tf.Tensor(TypedArray or Array) of indices with dtype int32.
- depth The datatype of depth is number. It is used to represent the depth of the one hot dimension.
- onValue The datatype of onValue is number. It is used to fill in the output when the index matches the location. It is an optional argument.
- offValue The datatype of offValue is number. It is used to fill in the output when the index does not match the location. It is also an optional argument.
Return: It returns a tf.Tensor.
Example 1:
Javascript
import * as tf from "@tensorflow/tfjs"
var val = tf.oneHot(tf.tensor1d([0,1,2], 'int32' ), 3);;
val.print()
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Output:
Tensor
[[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
Example 2:
Javascript
import * as tf from "@tensorflow/tfjs"
var val = tf.oneHot(tf.tensor1d([0,1,2], 'int32' ), 3,9,-1);
val.print()
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Output:
Tensor
[[9 , -1, -1],
[-1, 9 , -1],
[-1, -1, 9 ]]
Reference:https://js.tensorflow.org/api/latest/#oneHot
Last Updated :
10 Aug, 2021
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