Hands-On Transfer Learning with Python

Loss functions

The loss function compares the output of a neural network with the target values in the training, producing a loss value/score to measure how well the predictions of the network match the expected value. In the last section, we saw the need for different types of loss functions for different tasks, such as regression and binary classification. Here are a few other popular loss functions:

• Binary cross-entropy: The log loss or cross-entropy loss for the two-class classification problem discussed in last section on logistic units.
• Categorical cross-entropy: If we have a K class classification problem, then we use the generalized cross-entropy for K classes.
• Mean Squared Error: This is the mean sum squared error we've discussed ...