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TensorFlow.js Training Losses Complete Reference
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Tensorflow.js tf.losses.cosineDistance() Function

Last Updated : 22 Jul, 2021
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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.losses.cosineDistance() function is used to Compute the cosine distance loss between two tensors.

Syntax:

tf.losses.cosineDistance(labels, predictions,          axis, weights?, reduction?)

Parameters: This function accepts five parameters in which the last two are optional, which are illustrated below:

  • labels: The ground truth output tensor. The dimensions will be the same as predictions.
  • predictions: The predicted outputs.
  • axis: The cosine distance is computed along this dimension.
  • weights: It is a Tensor having rank 0, or the same rank as labels, and it must be broadcastable to labels that mean all the dimensions must be either 1, or the same as the corresponding losses dimension. This parameter is optional.
  • reduction: The type of reduction to apply to loss. This is also an optional parameter.

Return Value: It returns a Tensor which is a cosine distance loss between two tensors.

Example 1:

JavaScript 
// Importing the tensorflow.Js library import * as tf from "@tensorflow/tfjs"  // Creating labels tensor const a = tf.tensor2d([[1, 4, 5], [5, 5, 7]]);  // Creating predictions tensor const b = tf.tensor2d([[3, 2, 5], [3, 2, 7]])  // Computing cosine distance cosine = tf.losses.cosineDistance(a, b) cosine.print();


Output:

Tensor     -109

Example 2: In this example, we will pass one extra parameter that is weight. This is an optional parameter.

JavaScript 
// Importing the tensorflow.Js library import * as tf from "@tensorflow/tfjs"  // Creating labels tensor const a = tf.tensor2d([     [1, 4, 5, 5, 5, 7],     [4, 7, 6, 8, 9, 4] ]);  // Creating predictions tensor const b = tf.tensor2d([     [3, 2, 5, 3, 2, 7],     [3, 5, 7, 2, 4, 5] ]);  // Computing cosine distance cosine = tf.losses.cosineDistance(a, b , 1) cosine.print();


Output:

Tensor    -134.5

Reference: https://js.tensorflow.org/api/latest/#losses.cosineDistance
 


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TensorFlow.js Training Losses Complete Reference

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    TensorFlow.js is an open-source JavaScript library designed by Google to develop Machine Learning models and deep learning neural networks. TensorFlow.js Training Losses Functions: TensorFlow.js tf.losses.absoluteDifference() FunctionTensorFlow.js tf.losses.computeWeightedLoss() FunctionTensorFlow.j
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    Tensorflow.js tf.train.Optimizer Class
    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.train.Optimizer() class is used to extend Serializable class. This tf.train.Optimizer() class contains three inbuilt functions
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    Tensorflow.js tf.train.Optimizer class .minimize() Method
    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 .minimize() method executes the given function f() and tries to minimize the scalar output of f() by computing the gradients of y
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    TensorFlow.js Training Classes Complete Reference
    TensorFlow.js is an open-source JavaScript library designed by Google to develop Machine Learning models and deep learning neural networks. TensorFlow.js Training Classes Functions: TensorFlow.js tf.train.Optimizer ClassTensorFlow.js tf.train.Optimizer class .minimize() MethodTensorFlow.js tf.train.
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    Tensorflow.js Performance

    Tensorflow.js tf.time() Function
    Tensorflow.js is an open-source library that was developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment. The tf.time() function is used to execute the stated function, f() as well as return a promise in order that determine
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    Tensorflow.js tf.nextFrame() Function
    Introduction: Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.  The .nextFrame() function is used to return a promise which determines at which a requestAnimationFrame
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    Tensorflow.js tf.profile() 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 tf.
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    TensorFlow.js Performance Memory Complete Reference
    TensorFlow.js is an open-source JavaScript library designed by Google to develop Machine Learning models and deep learning neural networks. TensorFlow.js Performance Memory Functions: TensorFlow.js tf.tidy() FunctionTensorFlow.js tf.dispose() FunctionTensorFlow.js tf.keep() FunctionTensorFlow.js tf.
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    Tensorflow.js Environment

    Tensorflow.js tf.Environment Class
    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.Environment() class includes assessed flags and the registered platform. It is every time utilized like a global singleton and
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    Tensorflow.js tf.disposeVariables() Function
    Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment. The .disposeVariables() function is used to dispose every single variable stored in the backend engine. Syntax: tf.disp
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    Tensorflow.js tf.enableDebugMode() Function
    Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment. The .enableDebugMode() function is used to enable the debug mode that would register data regarding every single execut
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    Tensorflow.js tf.enableProdMode() Function
    Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment. The .enableProdMode() function is used to enable the mode of production that deactivates the exactness restraints in su
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    Tensorflow.js tf.engine() Function
    Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment. The .engine() function is used to return the global engine which saves the path of every single tensor as well as backe
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    Tensorflow.js tf.env() Function
    Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment. The .env() function is used to return the present environment i.e. a global entity. Moreover, the environment object in
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    TensorFlow.js Environment Complete Reference
    TensorFlow.js is an open-source JavaScript library designed by Google to develop Machine Learning models and deep learning neural networks. TensorFlow.js Environment Functions TensorFlow.js tf.Environment ClassTensorFlow.js tf.disposeVariables() FunctionTensorFlow.js tf.enableDebugMode() FunctionTen
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    Tensorflow.js Constraints

    Tensorflow.js tf.metrics.binaryAccuracy() 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.metrics.binaryAccuracy() function is used to calculate how often predictions match binary labels. And the function takes two te
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    Tensorflow.js tf.metrics.binaryCrossentropy() Function
    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.binaryCrossentropy() function is binary crossentropy metric function which uses binary tensors and returns tf.Tens
    1 min read
    Tensorflow.js tf.metrics.categoricalAccuracy() 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.metrics.categoricalAccuracy() function is used to return categorical accuracy between two tensor. It takes two tensor as a para
    2 min read
    Tensorflow.js tf.metrics.categoricalCrossentropy() 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 tf.
    1 min read
    Tensorflow.js tf.metrics.cosineProximity() 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 tf.
    2 min read
    Tensorflow.js tf.metrics.meanAbsoluteError() 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.metrics.meanAbsoluteError() is used to calculate mean absolute error. The mean absolute error is defined as the mean of absolut
    2 min read
    Tensorflow.js tf.metrics.meanAbsolutePercentageError() Function
    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 whi
    2 min read
    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_
    2 min read
    Tensorflow.js tf.metrics.precision() Function
    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:   t
    2 min read
    Tensorflow.js tf.metrics.recall() 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.metrics.recall() function is used to compute the recall of the predictions with respect to the labels. 'Recall' is one of the m
    2 min read
    Tensorflow.js tf.metrics.sparseCategoricalAccuracy() Function
    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.sparseCategoricalAccuracy() function is sparse categorical accuracy metric function which uses indices and logits
    1 min read
    TensorFlow.js Metrics Complete Reference
    TensorFlow.js is an open-source JavaScript library designed by Google to develop Machine Learning models and deep learning neural networks. Metrics functions are used to manipulate the matrices. TensorFlow.js Metrics functions: TensorFlow.js tf.metrics.binaryAccuracy() FunctionTensorFlow.js tf.metri
    1 min read
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