In the context of TensorFlow, variables are a special type of tensor objects that allow us to store and update the parameters of our models in a TensorFlow session during training. The following sections explain how we can define variables in a graph, initialize those variables in a session, organize variables via the so-called variable scope, and reuse existing variables.
TensorFlow variables store the parameters of a model that can be updated during training, for example, the weights in the input, hidden, and output layers of a neural network. When we define a variable, we need to initialize it with a tensor of values. Feel free to read more about TensorFlow variables at https://www.tensorflow.org/programmers_guide/variables.
TensorFlow provides two ways for defining variables:
tf.Variable(<initial-value>, name="variable-name")tf.get_variable(name, ...)
The first one, tf.Variable, is a class that creates an object for a new variable and adds...