Great question! The example you shared uses tf.Variable in a somewhat trivial way (wrapping the result of tf.add), which might make it seem unnecessary. But tf.Variable serves critical purposes that regular tensors (like the output of tf.add) can't handle. Let's break down the key reasons:

• Trainable model parameters
  The biggest use case for tf.Variable is defining trainable parameters in machine learning models—think neural network weights, biases, or embedding matrices. These variables are designed to be updated over time via gradient descent (or other optimizers). Regular tensors are immutable; once computed, their values can't be changed in-place. tf.Variable tracks gradients automatically, letting optimizers adjust their values during training to minimize loss.

  Here's a more realistic example of how it's used:

  # Define trainable weights and biases for a neural network layer
  weights = tf.Variable(tf.random.normal([784, 10]), name="layer_weights")
  biases = tf.Variable(tf.zeros([10]), name="layer_biases")
  
  # Build the model computation graph
  inputs = tf.placeholder(tf.float32, shape=[None, 784])
  logits = tf.matmul(inputs, weights) + biases
  loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
  
  # Optimizer will automatically update weights and biases during training
  optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
  

• Persistent state storage
  tf.Variable lets you save and restore state across sessions or program runs. For example, after training a model, you can save your variables to a checkpoint file, then load them later to make predictions or continue training. Regular tensors are transient—they only exist during the current session's computation and can't be easily persisted.

• Explicit state management
  Use tf.Variable when you need to maintain state over multiple computation steps. Common examples include:

  • Counters for tracking training iterations or epochs
  • Running averages for batch normalization (to store mean/variance statistics)
  • Stateful RNN cells that keep track of hidden states across time steps

  A simple counter example to illustrate this:

  counter = tf.Variable(0, name="step_counter")
  increment_counter = tf.assign(counter, counter + 1)
  
  with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      for _ in range(5):
          sess.run(increment_counter)
          print(sess.run(counter))  # Prints 1, 2, 3, 4, 5 sequentially
  

To circle back to your example: wrapping tf.add(a, b) in a tf.Variable is indeed unnecessary—you'd only do that if you needed to persist that sum's value across sessions or update it later. The real power of tf.Variable lies in its ability to act as mutable, trainable state for complex machine learning workflows.

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