One of the most fundamental concepts of neural networks is an operating as an operational gate. In this section, we will start with a multiplication operation as a gate, before moving on to consider nested gate operations.
Implementing operational gates
Getting ready
The first operational gate we will implement is 
. To optimize this gate, we declare the a input as a variable and the x input as a placeholder. This means that TensorFlow will try to change the a value and not the x value. We will create the loss function as the difference between the output and the target value, which is 50.
The second, nested operational gate will be 
. Again, we will declare a and b as variables and x as a place holder. We optimize the output...
