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TensorFlow – How to stack a list of rank-R tensors into one rank-(R+1) tensor in parallel
• Last Updated : 01 Aug, 2020

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)```

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