In TensorFlow, what is tf.identity used for?

In TensorFlow, the primary function of tf.identity is to return a new tensor with the same values and shape as the input tensor. Although it appears to be a straightforward copy operation, within the TensorFlow computational graph, it serves several critical roles:

1. Name Scope: Using tf.identity allows creating a tensor with a distinct name for variables or tensors, which is particularly useful in the TensorFlow computational graph when differentiating operations that handle the same data.

2. Control Dependency: In TensorFlow's execution model, the execution order of the computational graph is automatically determined by data dependencies. Using tf.identity enables the manual addition of control dependencies, which forces TensorFlow to complete specific operations before executing the tf.identity operation. This is especially useful for ensuring operations execute in the intended sequence.

3. Variable Update Synchronization: During neural network training, tf.identity can ensure that all operations using a specific variable access the latest value of that variable. For example, in a parameter server architecture, it facilitates synchronizing variable updates across multiple training steps.

For instance, consider training a deep learning model with an intermediate variable a. To ensure it is correctly referenced after each update, we can use tf.identity to create a copy b = tf.identity(a), and use b elsewhere in the model. This guarantees that all operations referencing b utilize the latest value of a.

In summary, while tf.identity may seem simple, its practical applications in TensorFlow are diverse, primarily focused on enhancing computational graph control and data flow management.