onsider we have a cross$-$entropy loss function for binary classification:

L$=[$ ln$()+(1)$ ln$(1)],$ where $$ is the probability out from the output layer activation function. We've built a computation graph of the network as shown below. The blue letters below are intermediate variable labels to help you understand the connection between the network architecture graph above and the computation graph.