TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
dynamic_stitch() is used to merge multiple tensors into single tensor.
Syntax: tensorflow.dynamic_stitch( indices, data, name)
Parameter:
- indices: It is a list of Tensors having minimum 1 tensor and each tensor with dtype int32.
- data : It is list of Tensors having same length as indices.
- name(optional): It defines the name for the operation.
Result:
It returns a Tensor of same dtype as data.
Example 1:
Python3
# Importing the library import tensorflow as tf # Initializing the input indices = [[0, 1, 5], [2, 4, 3, 6]] data = [[1, 2, 3], [4, 5, 6, 7]] # Printing the input print('indices:', indices) print('data: ', data) # Calculating result x = tf.dynamic_stitch(indices, data) # Printing the result print('x: ', x) |
Output:
indices: [[0, 1, 5], [2, 4, 3, 6]] data: [[1, 2, 3], [4, 5, 6, 7]] x: tf.Tensor([1 2 4 6 5 3 7], shape=(7, ), dtype=int32)
Example 2:
Python3
# Importing the library import tensorflow as tf # Initializing the input indices = [[0, 1, 6], [5, 4, 3]] data = [[1, 2, 3], [4, 5, 6]] # Printing the input print('indices:', indices) print('data: ', data) # Calculating result x = tf.dynamic_stitch(indices, data) # Printing the result print('x: ', x) |
Output:
indices: [[0, 1, 2], [5, 4, 3]] data: [[1, 2, 3], [4, 5, 6]] x: tf.Tensor([1 2 3 6 5 4], shape=(6, ), dtype=int32)
