Python - tensorflow.identity_n()

Introduction

In TensorFlow, the tensorflow.identity_n() function is used to return a list of tensors. It takes a list of tensor objects as input and returns a list containing the same tensors.

Syntax

The syntax of tensorflow.identity_n() is as follows:

tensorflow.identity_n(inputs, name=None)

Parameters

The tensorflow.identity_n() function takes the following parameters:

• inputs: A list of tensor objects to be returned.
• name (optional): A string name for the operation.

Return Value

The function returns a list of tensors that are identical to the input tensors.

Example

import tensorflow as tf

# Define some tensor objects
x = tf.constant(2.0)
y = tf.constant(3.0)
z = tf.constant(4.0)

# Use tensorflow.identity_n() to return a list of tensors
output_tensors = tf.identity_n([x, y, z])

# Print the output tensors
print(output_tensors)

In the above example, we define three tensor objects x, y, and z. Then, we pass these tensors to tensorflow.identity_n() function which returns a list of tensors. Finally, we print the output tensors.

Output:

[<tf.Tensor 'Identity_1:0' shape=() dtype=float32>,
 <tf.Tensor 'Identity_2:0' shape=() dtype=float32>,
 <tf.Tensor 'Identity_3:0' shape=() dtype=float32>]

Conclusion

The tensorflow.identity_n() function in Python TensorFlow is helpful when we need to return a list of tensors that are identical to a given list of tensors. It can be useful in various deep learning and machine learning scenarios.