Python – tensorflow.dynamic_partition()
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
import tensorflow as tf
data = [ 1 , 2 , 3 , 4 , 5 ]
num_partitions = 2
partitions = [ 0 , 0 , 1 , 0 , 1 ]
print ( 'data: ' , data)
print ( 'partitions:' , partitions)
print ( 'num_partitions:' , num_partitions)
x = tf.dynamic_partition(data, partitions, num_partitions)
print ( 'x[0]: ' , x[ 0 ])
print ( 'x[1]: ' , x[ 1 ])
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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
import tensorflow as tf
data = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]
num_partitions = 3
partitions = [ 0 , 2 , 1 , 0 , 1 , 2 , 2 ]
print ( 'data: ' , data)
print ( 'partitions:' , partitions)
print ( 'num_partitions:' , num_partitions)
x = tf.dynamic_partition(data, partitions, num_partitions)
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)
Last Updated :
10 Jul, 2020
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