4-4-1-3. Hello, Tensor World!

Hello, Tensor World!

Let’s analyze the Hello World script you ran. For reference, I’ve added the code below.

import tensorflow as tf

# Create TensorFlow object called hello_constant
hello_constant = tf.constant('Hello World!')

with tf.Session() as sess:
    # Run the tf.constant operation in the session
    output = sess.run(hello_constant)


In TensorFlow, data isn’t stored as integers, floats, or strings. These values are encapsulated in an object called a tensor. In the case of hello_constant = tf.constant('Hello World!')hello_constant is a 0-dimensional string tensor, but tensors come in a variety of sizes as shown below:

# A is a 0-dimensional int32 tensor
A = tf.constant(1234) 
# B is a 1-dimensional int32 tensor
B = tf.constant([123,456,789]) 
# C is a 2-dimensional int32 tensor
C = tf.constant([ [123,456,789], [222,333,444] ])

tf.constant() is one of many TensorFlow operations you will use in this lesson. The tensor returned by tf.constant() is called a constant tensor, because the value of the tensor never changes.


TensorFlow’s api is built around the idea of a computational graph, a way of visualizing a mathematical process. Let’s take the TensorFlow code you ran and turn that into a graph:

A “TensorFlow Session”, as shown above, is an environment for running a graph. The session is in charge of allocating the operations to GPU(s) and/or CPU(s), including remote machines. Let’s see how you use it.

with tf.Session() as sess:
    output = sess.run(hello_constant)

The code has already created the tensor, hello_constant, from the previous lines. The next step is to evaluate the tensor in a session.

The code creates a session instance, sess, using tf.Session. The sess.run() function then evaluates the tensor and returns the results.

After you run the above, you will see the following printed out:

'Hello World!'
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