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 = 2partitions = [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 = 3partitions = [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)
