CODEWITHSUNDEEP
tensorflow
user3222184
user3222184

Reputation: 1111

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

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 probably something bad for production.

I have seen code like this, in which feed_dict is not involved:

for j in range(n_batches):
    X_batch, Y_batch = mnist.train.next_batch(batch_size)
    _, loss_batch = sess.run([optimizer, loss], {X: X_batch, Y:Y_batch})

I have also seen code like this, in which feed_dict is involved: 

for i in range(100): 
    for x, y in data:
        # Session execute optimizer and fetch values of loss
        _, l = sess.run([optimizer, loss], feed_dict={X: x, Y:y}) 
        total_loss += l

I understand feed_dict is that you are feeding in data and try X as the key as if in the dictionary. But here I don't see any difference. So, what exactly is the difference and why do we need feed_dict?

Upvotes: 23

Views: 17100

Answers (1)

David Parks
David Parks

Reputation: 32051

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)

Upvotes: 32

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