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Python – tensorflow.math.bincount()
• Last Updated : 13 Jul, 2020

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks. bincount() is present in TensorFlow’s math module. It is used to count occurrences of a each number in integer array.

Syntax: tensorflow.math.bincount( arr, weights, minlength, maxlength, dtype, name)

Parameters:

• arr: It’s tensor of dtype int32 with non-negative values.
• weights(optional): It’s a tensor of same shape as arr. Count of each value in arr is incremented by it’s corresponding weight.
• minlength(optional): It defines the minimum length of returnted output.
• maxlength(optional): It defines the maximum length of returnted output. Bin of the values in arr that are greater than or equal to maxlength is not calculated.
• dtype(optional): It determines the dtype of returned output if weight is none.
• name(optional): It’s an optional argument that defines the name for the operation.

Returns:
It returns a vector with the same dtype as weights or the given dtype. Index of the vector defines the value and it’s value defines the bin of index in arr.

Example 1:

## Python3

 `# importing the library``import` `tensorflow as tf` `# initializing the input``a ``=` `tf.constant([``1``,``2``,``3``,``4``,``5``,``1``,``7``,``3``,``1``,``1``,``5``], dtype ``=` `tf.int32)` `# printing the input``print``(``'a: '``,a)` `# evaluating bin``r ``=` `tf.math.bincount(a)` `# printing result``print``(``"Result: "``,r)`

Output:

```a:  tf.Tensor([1 2 3 4 5 1 7 3 1 1 5], shape=(11,), dtype=int32)
Result:  tf.Tensor([0 4 1 2 1 2 0 1], shape=(8,), dtype=int32)

# bin of 0 in input is 0, bin of 1 in input is 4 and so on

```

Example 2: This example provides weights, so instead of 1 values are incremented by the corresponding weight.

## Python3

 `# importing the library``import` `tensorflow as tf` `# initializing the input``a ``=` `tf.constant([``1``,``2``,``3``,``4``,``5``,``1``,``7``,``3``,``1``,``1``,``5``], dtype ``=` `tf.int32)``weight ``=` `tf.constant([``0``,``2``,``1``,``0``,``2``,``1``,``3``,``3``,``1``,``0``,``5``], dtype ``=` `tf.int32)` `# printing the input``print``(``'a: '``,a)``print``(``'weight: '``,weight)` `# evaluating bin``r ``=` `tf.math.bincount(arr ``=` `a,weights ``=` `weight)` `# printing result``print``(``"Result: "``,r)`

Output:

```a:  tf.Tensor([1 2 3 4 5 1 7 3 1 1 5], shape=(11,), dtype=int32)
weight:  tf.Tensor([0 2 1 0 2 1 3 3 1 0 5], shape=(11,), dtype=int32)
Result:  tf.Tensor([0 2 2 4 0 7 0 3], shape=(8,), dtype=int32)

```

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