The simplest form of image classification is binary classification. This is where we have a classifier that has just one object to classify, for example, dog/no dog. In this case, a loss function we are likely to use is the binary cross-entropy loss.
The cross entropy function between true labels p and model predictions q is defined as:
With i being the index for each possible element of our labels and predictions.
However, as we are dealing with the binary case when we have only two possible outcomes, y=1 and y=0, then p{} and q {} can be simplified down and we get:
This is equivalent
Iterating over training ...
