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# Python – tensorflow.math.cumulative_logsumexp()

• Last Updated : 05 Jul, 2021

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

cumulative_logsumexp() is used to calculate the cumulative log-sum-exp of input tensor. This operation is equivalent to tensorflow.math.log( tensorflow.math.cumsum( tensorflow.math.exp(x))) but it is numerically more stable.

Syntax: tensorflow.math.cumulative_logsumexp(   x, axis, exclusive, reverse, name)

Parameters:

• x: It’s the input tensor. Allowed dtypes for this tensor are  float16, float32, float64.
• axis(optional): It’s a tensor of type int32. It’s value should  be in the range  A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).  Default value is 0.
• exclusive(optional): It’s of type bool. Default value is False.
• reverse(optional): It’s of type bool. Default value is False.
• name(optional): It’s defines the name for the operation.

Returns:

It returns a tensor of same dtype as x.

Example 1:

## Python3

 `# importing the library``import` `tensorflow as tf` `# initializing the input``a ``=` `tf.constant([``1``, ``2``, ``4``, ``5``], dtype ``=` `tf.float64) ` `# Printing the input``print``(``"Input: "``,a)` `# Cumulative log-sum-exp``res  ``=` `tf.math.cumulative_logsumexp(a)` `# Printing the result``print``(``"Output: "``,res)`

Output:

```Input:  tf.Tensor([1. 2. 4. 5.], shape=(4,), dtype=float64)
Output:  tf.Tensor([1.         2.31326169 4.16984602 5.36184904], shape=(4,), dtype=float64)```

Example 2: In this example both reverse and exclusive are set to True.

## Python3

 `# importing the library``import` `tensorflow as tf` `# initializing the input``a ``=` `tf.constant([``2``, ``3``, ``4``, ``5``], dtype ``=` `tf.float64) ` `# Printing the input``print``(``"Input: "``,a)` `# Cumulative log-sum-exp``res  ``=` `tf.math.cumulative_logsumexp(a, reverse ``=` `True``, exclusive ``=` `True``)` `# Printing the result``print``(``"Output: "``,res)`

Output:

```Input:  tf.Tensor([2. 3. 4. 5.], shape=(4,), dtype=float64)
Output:  tf.Tensor([ 5.40760596e+000  5.31326169e+000  5.00000000e+000 -1.79769313e+308], shape=(4,), dtype=float64)```

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