Python – tensorflow.dynamic_partition()

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

dynamic_partition() is used to divide the data into number of partitions.

Syntax: tensorflow.dynamic_partition(data, partitions, num_partitions, name)

Parameters:

data : It is the input tensor that need to be partitioned.

partitions: It is Tensor of type int32 and it’s data should be in the range [0, num_partitions).

num_partitions: It defines the number of partitions.

name(optional): It defines the name for the operation.

Returns:

It returns a list of tensor with num_partitions items. Each tensor in the list have same dtype as data.

Example 1: Dividing data into two partitions

Python3

# Importing the library 
import tensorflow as tf 
  
# Initializing the input 
data = [1, 2, 3, 4, 5] 
num_partitions = 2
partitions = [0, 0, 1, 0, 1] 
  
# Printing the input 
print('data: ', data) 
print('partitions:', partitions) 
print('num_partitions:', num_partitions) 
  
# Calculating result 
x = tf.dynamic_partition(data, partitions, num_partitions) 
  
  
# Printing the result 
print('x[0]: ', x[0]) 
print('x[1]: ', x[1]) 

Output:

data:  [1, 2, 3, 4, 5]
partitions: [0, 0, 1, 0, 1]
num_partitions: 2
x[0]:  tf.Tensor([1 2 4], shape=(3, ), dtype=int32)
x[1]:  tf.Tensor([3 5], shape=(2, ), dtype=int32)

Example 2: Dividing into 3 Tensors

Python3

# Importing the library 
import tensorflow as tf 
  
# Initializing the input 
data = [1, 2, 3, 4, 5, 6, 7] 
num_partitions = 3
partitions = [0, 2, 1, 0, 1, 2, 2] 
  
# Printing the input 
print('data: ', data) 
print('partitions:', partitions) 
print('num_partitions:', num_partitions) 
  
# Calculating result 
x = tf.dynamic_partition(data, partitions, num_partitions) 
  
  
# Printing the result 
print('x[0]: ', x[0]) 
print('x[1]: ', x[1]) 
print('x[2]: ', x[2]) 

Output:

data:  [1, 2, 3, 4, 5, 6, 7]
partitions: [0, 2, 1, 0, 1, 2, 2]
num_partitions: 3
x[0]:  tf.Tensor([1 4], shape=(2, ), dtype=int32)
x[1]:  tf.Tensor([3 5], shape=(2, ), dtype=int32)
x[2]:  tf.Tensor([2 6 7], shape=(3, ), dtype=int32)