We achieved the optimization of a computational gate via TensorFlow's implicit back propagation. TensorFlow keeps track of our model's operations and variable values and makes adjustments in respect of our optimization algorithm specification and the output of the loss function.
We can keep expanding the operational gates while keeping track of which inputs are variables and which inputs are data. This is important to keep track of, because TensorFlow will change all variables to minimize the loss but not the data, which is declared as placeholders.
The implicit ability to keep track of the computational graph and update the model variables automatically with every training step is one of the great features of TensorFlow and ...
