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.precision() function is used to calculate the precision of the expectancy with reference to the names.
Syntax:
tf.metrics.precision(yTrue, yPred)
Parameters:
- yTrue: It is the stated ground truth tensor which is supposed to hold values from 0 to 1 and it can be of type tf.Tensor.
- yPred: It is the stated prediction tensor which is supposed to hold values from 0 to 1 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, 1], [1, 1]]); const z = tf.tensor2d([[1, 0], [0, 1]]); // Calling metrics.precision() method const pre = tf.metrics.precision(y, z); // Printing output pre.print(); |
Output:
Tensor
0.5
Example 2:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Calling metrics.precision() method with // its parameter directly and then // Printing output const output = tf.metrics.precision(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
0.4444444477558136
Reference: https://js.tensorflow.org/api/latest/#metrics.precision
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