Tensorflow.js is an open-source library developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.
The .metrics.meanAbsolutePercentageError() function is a loss or else a metric function i.e. mean absolute percentage error which uses truth and prediction tensor inputs in order to return tf.Tensor object.
Syntax:
tf.metrics.meanAbsolutePercentageError(yTrue, yPred)
Parameters:
- yTrue: It is the stated truth tensor and it can be of type tf.Tensor.
- yPred: It is the stated prediction tensor and it can be of type tf.Tensor.
Return Value: It returns the tf.Tensor object.
Example 1:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Defining truth and prediction tensors const y = tf.tensor2d([[0, 2], [20, 30]]); const z = tf.tensor2d([[0, 2], [21, 34]]); // Calling metrics.meanAbsolutePercentageError() // method const mape = tf.metrics.meanAbsolutePercentageError(y, z); // Printing output mape.print(); |
Output:
Tensor
[0, 9.166666]
Example 2:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Calling metrics.meanAbsolutePercentageError() // method with its parameter directly and then // Printing output const output = tf.metrics.meanAbsolutePercentageError(tf.tensor( [ [0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [1, 1, 0, 0], [0, 0, 1, 0] ] ), tf.tensor( [ [0, 0, 1, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1], [1, 1, 0, 0] ] )).print(); |
Output:
Tensor
[500025, 0, 0, 250025, 500025]
Reference: https://js.tensorflow.org/api/latest/#metrics.meanAbsolutePercentageError
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