What's the difference between tf.placeholder and tf.Variable?

Question

I\'m a newbie to TensorFlow. I\'m confused about the difference between tf.placeholder and tf.Variable. In my view, tf.placeholder is

Answer 1

Variables

A TensorFlow variable is the best way to represent shared, persistent state manipulated by your program. Variables are manipulated via the tf.Variable class. Internally, a tf.Variable stores a persistent tensor. Specific operations allow you to read and modify the values of this tensor. These modifications are visible across multiple tf.Sessions, so multiple workers can see the same values for a tf.Variable. Variables must be initialized before using.

Example:

x = tf.Variable(3, name="x")
y = tf.Variable(4, name="y")
f = x*x*y + y + 2

This creates a computation graph. The variables (x and y) can be initialized and the function (f) evaluated in a tensorflow session as follows:

with tf.Session() as sess:
     x.initializer.run()
     y.initializer.run()
     result = f.eval()
print(result)
42

Placeholders

A placeholder is a node (same as a variable) whose value can be initialized in the future. These nodes basically output the value assigned to them during runtime. A placeholder node can be assigned using the tf.placeholder() class to which you can provide arguments such as type of the variable and/or its shape. Placeholders are extensively used for representing the training dataset in a machine learning model as the training dataset keeps changing.

Example:

A = tf.placeholder(tf.float32, shape=(None, 3))
B = A + 5

Note: 'None' for a dimension means 'any size'.

with tf.Session as sess:
    B_val_1 = B.eval(feed_dict={A: [[1, 2, 3]]})
    B_val_2 = B.eval(feed_dict={A: [[4, 5, 6], [7, 8, 9]]})

print(B_val_1)
[[6. 7. 8.]]
print(B_val_2)
[[9. 10. 11.]
 [12. 13. 14.]]

References:

1. https://www.tensorflow.org/guide/variables
2. https://www.tensorflow.org/api_docs/python/tf/placeholder
3. O'Reilly: Hands-On Machine Learning with Scikit-Learn & Tensorflow