Inputs, variables, outputs, and operations

Now with an understanding of the underlying architecture let's proceed to the most common elements that comprise a TensorFlow client. If you read any of the millions of TensorFlow clients available on the internet, they all (the TensorFlow-related code) fall into one of these buckets:

• Inputs: Data used to train and test our algorithms
• Variables: Mutable tensors, mostly defining the parameters of our algorithms
• Outputs: Immutable tensors storing both terminal and intermediate outputs
• Operations: Various transformations for inputs to produce the desired outputs

In our earlier example, in the sigmoid example, we can find instances of all these categories. We list the elements in Table 2.1:

TensorFlow element	Value from example client
Inputs	x
Variables	W and b
Outputs	h
Operations	tf.matmul(...), tf.nn.sigmoid(...)

The following subsections explain each of these TensorFlow elements in more detail.

Defining inputs in TensorFlow

The client...