Great question! These two functions are tied to variable initialization in TensorFlow, but there are key differences you should know about:

1. Deprecation Status #

• tf.initialize_all_variables() is a legacy, deprecated method. It was the standard way to initialize variables in TensorFlow versions before 0.12. Once TensorFlow 0.12 launched, this function was marked deprecated because it didn't account for the growing distinction between different variable types in the framework.
• tf.global_variables_initializer() is the current, recommended replacement. It was introduced to fix the limitations of the older function and align with TensorFlow's expanded variable scoping system.

2. Scope of Variables Initialized #

• tf.initialize_all_variables() tries to initialize every variable in your computation graph—this includes both global variables (the ones you define with tf.Variable()) and local variables (like those used in metrics, batch normalization moving averages, or temporary variables scoped with tf.local_variable()). This could cause unintended behavior if you wanted to handle local variables separately (e.g., restoring them from a checkpoint instead of initializing from scratch).
• tf.global_variables_initializer() only targets global variables. If you need to initialize local variables, you'll have to use tf.local_variables_initializer() explicitly. You can run both initializers together for convenience using tf.group():

  sess.run(tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()))
  

3. Best Practices #

• If you're using any TensorFlow version >= 0.12, always use tf.global_variables_initializer(). Using the deprecated initialize_all_variables() will trigger warning messages in your console, and while it's kept for backward compatibility in older releases, it's not supported in newer TensorFlow versions (like TF 2.x—note that eager execution in TF2 changes initialization entirely, as variables initialize automatically when created).
• For TF2 users: These initializer functions are mostly relevant if you're working with the compat.v1 module or building computation graphs explicitly.

To sum it up: initialize_all_variables() is outdated and imprecise, while global_variables_initializer() is the modern, controlled tool that lets you target the specific variable set you need.

内容的提问来源于stack exchange，提问作者akshaymalviya