Placeholders and variables are key tools for using computational graphs in TensorFlow. We must understand the difference and when to best use them to our advantage.
One of the most important distinctions to make with the data is whether it is a placeholder or a variable. Variables are the parameters of the algorithm and TensorFlow keeps track of how to change these to optimize the algorithm. Placeholders are objects that allow you to feed in data of a specific type and shape and depend on the results of the computational graph, such as the expected outcome of a computation.
The main way to create a variable is by using the
Variable() function, which takes a tensor as an input and outputs ...