## Tensor

In TensorFlow, data isn’t stored as integers, floats, or strings. These values are encapsulated in an object called a tensor. 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. The tensor returned by `tf.constant()`

is called a constant tensor, because the value of the tensor never changes.

## Session

TensorFlow’s API is built around the idea of a computational graph, a way of visualizing a mathematical process. A “TensorFlow Session” 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.

## Hello World

```
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)
print(output)
```

The code first creates 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.