Implementing operational gates
One of the most fundamental concepts of neural networks is its functioning 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 f(x) = a · x:
To optimize this gate, we declare the a input as a variable and x as the input tensor of our model. 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 f(x) = a · x + b:
Again, we will declare a and b as variables and x as the input tensor of our model. We optimize the output toward the target value of 50 again. The interesting thing to note is that the solution for this...
