Let's see if this loss function does the right thing for our example from Fig. 5 .2. We want the loss to be smaller if the model's estimate is close to correct, and bigger if the model is confused. So first let's suppose the correct gold label for the sentiment example in Fig. 5 .2 is positive, i.e., y = 1. In this case our model is doing well, since from Eq. 5.7 it indeed gave the example a higher probability of being positive (.70) than negative (.30). If we plug σ (w • x + b) = .70 and y = 1 into Eq. 5.12, the right side of the equation drops out, leading to the following loss (we'll use log to mean natural log when the base is not specified):