Hands-On Convolutional Neural Networks with TensorFlow

Cross-entropy loss (log loss)

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 

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