Input

In the last section, you passed a tensor into a session and it returned the result. What if you want to use a non-constant? This is where tf.placeholder() and feed_dict come into place. In this section, you'll go over the basics of feeding data into TensorFlow.

tf.placeholder()

Sadly you can’t just set x to your dataset and put it in TensorFlow, because over time you'll want your TensorFlow model to take in different datasets with different parameters. You need tf.placeholder() !

tf.placeholder() returns a tensor that gets its value from data passed to the tf.session.run() function, allowing you to set the input right before the session runs.

Session’s feed_dict

x = tf.placeholder(tf.string)

with tf.Session() as sess:
    output = sess.run(x, feed_dict={x: 'Hello World'})

Use the feed_dict parameter in tf.session.run() to set the placeholder tensor. The above example shows the tensor x being set to the string "Hello, world" . It's also possible to set more than one tensor using feed_dict as shown below.

x = tf.placeholder(tf.string)
y = tf.placeholder(tf.int32)
z = tf.placeholder(tf.float32)

with tf.Session() as sess:
    output = sess.run(x, feed_dict={x: 'Test String', y: 123, z: 45.67})

Note: If the data passed to the feed_dict doesn’t match the tensor type and can’t be cast into the tensor type, you’ll get the error “ ValueError: invalid literal for …”.

Quiz

Let's see how well you understand tf.placeholder() and feed_dict . The code below throws an error, but I want you to make it return the number 123 . Change line 11, so that the code returns the number 123 .