Tensorflow: When should I use or not use `feed_dict`?

Question

I am kind of confused why are we using feed_dict? According to my friend, you commonly use feed_dict when you use placeholder, and this is

Answer 1

In a tensorflow model you can define a placeholder such as x = tf.placeholder(tf.float32), then you will use x in your model.

For example, I define a simple set of operations as:

x = tf.placeholder(tf.float32)
y = x * 42

Now when I ask tensorflow to compute y, it's clear that y depends on x.

with tf.Session() as sess:
  sess.run(y)

This will produce an error because I did not give it a value for x. In this case, because x is a placeholder, if it gets used in a computation you must pass it in via feed_dict. If you don't it's an error.

Let's fix that:

with tf.Session() as sess:
  sess.run(y, feed_dict={x: 2})

The result this time will be 84. Great. Now let's look at a trivial case where feed_dict is not needed:

x = tf.constant(2)
y = x * 42

Now there are no placeholders (x is a constant) and so nothing needs to be fed to the model. This works now:

with tf.Session() as sess:
  sess.run(y)