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.sparseToDense() function is used to convert a specified sparse representation into a dense Tensor. If the given indices get repeated, the final value is summed over all values for that indices.
Syntax:
tf.sparseToDense(sparseIndices, sparseValues,
outputShape, defaultValue)
Parameters: This function accepts four parameters which are illustrated below:
- sparseIndices: It is a 0-D, 1-D, or 2-D Tensor of data type int32. Here sparseValues[i] get placed at sparseIndices[i], where i is the indices values.
- sparseValues: It is a 0-D or 1-D Tensor of values which corresponds to each row of sparseIndices.
- outputShape: It is the shape of the converted dense output tensor.
- defaultValue: It is the value to set for indices not specified in sparseIndices. Its default value is zero. It is an optional parameter.
Example 1:
Javascript
// Importing the tensorflow.js libraryimport * as tf from "@tensorflow/tfjs"Â
// Initializing indices and valuesconst indices = tf.tensor1d([4, 3, 2, 1, 0], 'int32');const values = tf.tensor1d([1111, 111, 11, 1, 0.1], 'float32');Â
// Specifying shape for the output denseconst shape = [7];Â
// Getting the Dense representation for the above// sparse representationtf.sparseToDense(indices, values, shape).print(); |
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Output:
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Tensor [0.1, 1, 11, 111, 1111, 0, 0]
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Example 2:
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Javascript
// Importing the tensorflow.js libraryimport * as tf from "@tensorflow/tfjs"Â
// Initializing indices and valuesconst indices = tf.tensor1d([1, 2, 3], 'int32');const values = tf.tensor1d([10, 20, 30], 'float32');Â
// Getting the Dense representation for the above// sparse representation along with shape of [6]// and default value of 55tf.sparseToDense(indices, values, [6], 55).print(); |
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Output:
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Tensor [55, 10, 20, 30, 55, 55]
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Reference: https://js.tensorflow.org/api/latest/#sparseToDense
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