TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
TensorFlow provides build in methods to stack a list of rank-R tensors into one rank-(R+1) tensor in parallel.
Methods Used:
- parallel_stack: This method accepts a list of Tensors and returns a Tensor with all values stacked in parallel. This methods copies pieces of the input into the output as they become available.
- stack: This method accepts a list of Tensors, axis along which values should be stacked and returns a Tensor with all values stacked.
Example 1: This example uses stack method to stack tensors.
Python3
# importing the library import tensorflow as tf # Initializing the Input x = tf.constant([1, 2, 3]) y = tf.constant([4, 5, 6]) z = tf.constant([7, 8, 9]) # Printing the Input print("x: ", x) print("y: ", y) print("z: ", z) # Stacking Tensors res = tf.stack(values =[x, y, z], axis = 0) # Printing the resulting Tensor print("Res: ", res ) |
Output:
x: tf.Tensor([1 2 3], shape=(3, ), dtype=int32) y: tf.Tensor([4 5 6], shape=(3, ), dtype=int32) z: tf.Tensor([7 8 9], shape=(3, ), dtype=int32) Res: tf.Tensor( [[1 2 3] [4 5 6] [7 8 9]], shape=(3, 3), dtype=int32)
Example 2: This example uses parallel_stack method to stack the input Tensors.
Python3
# importing the library import tensorflow as tf # Initializing the Input x = tf.constant([1, 2, 3]) y = tf.constant([4, 5, 6]) z = tf.constant([7, 8, 9]) # Printing the Input print("x: ", x) print("y: ", y) print("z: ", z) # Stacking Tensors res = tf.parallel_stack(values =[x, y, z]) # Printing the resulting Tensor print("Res: ", res ) |
Output:
x: tf.Tensor([1 2 3], shape=(3, ), dtype=int32) y: tf.Tensor([4 5 6], shape=(3, ), dtype=int32) z: tf.Tensor([7 8 9], shape=(3, ), dtype=int32) Res: tf.Tensor( [[1 2 3] [4 5 6] [7 8 9]], shape=(3, 3), dtype=int32)
