Implementing operational gates

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.

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 towards the target value of 50 again. The interesting thing to note is that the solution for this second example is not unique. There are many combinations of model variables that will allow the output to be 50. With neural networks, we do not care so much about the values...