How to initialise only optimizer variables in Tensorflow?

In TensorFlow, if you need to initialize only the optimizer's variables, you can use TensorFlow's features to specifically designate these variables and initialize them with appropriate commands. Below are the detailed steps and code examples:

Step 1: Build the Model

First, build your model and define the optimizer. For this example, we use a simple model:

python
# Build the Model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(32,)),
    tf.keras.layers.Dense(1)
])

# Define the Optimizer
optimizer = tf.keras.optimizers.Adam()

Step 2: Identify the Optimizer Variables

Before proceeding, retrieve all relevant variables of the optimizer. Typically, the optimizer creates specialized variables such as gradient accumulators (e.g., for momentum), which you can obtain by calling the optimizer's variables() method.

python
# Retrieve the Optimizer Variables
optimizer_vars = optimizer.variables()

Step 3: Initialize the Optimizer Variables

Once you have the optimizer's variables, use the tf.variables_initializer function to initialize them separately:

python
# Initialize the Optimizer Variables
init_op = tf.variables_initializer(optimizer_vars)

# Execute the initialization within the session
sess = tf.compat.v1.Session()  # Using TensorFlow 1.x Session environment
sess.run(init_op)

For TensorFlow 2.x, you can use the tf.compat.v1 global session or perform initialization within tf.function:

python
tf.compat.v1.global_variables_initializer().run(session=tf.compat.v1.Session())

or initialize within tf.function:

python
@tf.function
def init_optimizer_vars():
    for var in optimizer_vars:
        var.assign(tf.zeros_like(var))

init_optimizer_vars()

Example Explanation

In this example, we first create a simple neural network model and define an Adam optimizer. Then, we specifically extract the optimizer's variables and initialize them separately. The benefit is that you can control the initialization of these variables at different stages of model training, which facilitates more flexible training strategies. This method is particularly useful when reinitializing the optimizer state during training, such as in transfer learning or model reset scenarios.