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Python – tensorflow.math.bincount()

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  • Last Updated : 18 Oct, 2022
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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 returned output.
  • maxlength(optional): It defines the maximum length of returned 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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