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# Tensorflow.js tf.metrics.meanSquaredError() 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 Tensorflow tf.metrics.meanSquaredError() function is a Loss or metric function used to  Computes the mean squared error between y_true and y_pred. the y_true is a truth tensor and y_pred is the Prediction Tensor.

Syntax:

`tf.metrics.meanSquaredError(tensor1, tensor2);`

Parameters: This function accepts two parameters which are illustrated below:

• tensor1: It is the truth tensor (y_true).
• tensor2: It is the prediction tensor (y_pred).

Return Value: It returns the mean square error tensor between truth tensor and prediction tensor.

Example 1:

## Javascript

 `// Importing the tensorflow.Js library` `// import * as tf from "@tensorflow/tfjs"`   `// Creating the tensor` `let truth = tf.tensor1d([6, 4]);` `let prediction = tf.tensor1d([-3, -4]);`   `// Calculating mean squared Error ` `// between truth and prediction tensor` `const mse = tf.metrics.meanSquaredError(truth, prediction);`   `// Printing mean square error` `mse.print();`

Output:

```Tensor
72.5```

Example 2:

## Javascript

 `// Importing the tensorflow.Js library` `// import * as tf from "@tensorflow/tfjs"`   `// Calculating mean squared Error between ` `// truth and prediction tensor` `let mse = tf.metrics.meanSquaredError(` `    ``tf.tensor1d([0, 1, 2, 3]), ` `    ``tf.tensor1d([-8,-9, -10, -11])` `);`   `// Printing mean square error ` `mse.print();`

Output:

```Tensor
126```
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