Tensorflow.js tf.losses.sigmoidCrossEntropy() 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. 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 Tensorflow.js tf.losses.sigmoidCrossEntropy() function calculates the sigmoid cross entropy loss between two given tensors.
Syntax:
tf.losses.sigmoidCrossEntropy(
multiClassLabels, logits, weights,
labelSmoothing, reduction
);
Parameters:
- multiClassLabels: It is the ground truth output tensor of different shapes like num classes, batch size. It is similar in dimensions as ‘predictions‘.
- logits: It is the outputs that are being predicted.
- weights: These are those tensors whose rank is either 0 or 1, and they must be broad castable to lebels.
- labelSmoothing: If the value is greater than 0, then it means it will smooth the labels.
- reduction: It is the type of reduction to apply to loss. It must be of Reduction type.
Note: The weights, labelSmoothing and reduction are optional parameters.
Return value: It returns tf.Tensor.
Example 1:
Javascript
import * as tf from "@tensorflow/tfjs"
let geek1 = tf.tensor3d([[[1], [2]], [[3], [4]]]);
let geek2 = tf.tensor3d([7, 11, 13, 4], [2, 2, 1])
geek = tf.losses.sigmoidCrossEntropy(geek1, geek2)
geek.print();
|
Output:
Tensor
-12.245229721069336
Example 2:
Javascript
import * as tf from "@tensorflow/tfjs"
tf.losses.sigmoidCrossEntropy(
tf.tensor4d([[[[9], [8]], [[7], [5]]]]),
tf.tensor4d([[[[1], [2]], [[3], [4]]]])
).print();
|
Output:
Tensor
-13.873268127441406
Reference: https://js.tensorflow.org/api/latest/#losses.sigmoidCrossEntropy
Last Updated :
30 Aug, 2021
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