Neural Network Projects with Python

Loss function – error metric for neural network training

In an earlier section, we defined the loss function as a way to evaluate the goodness of our predictions (that is, how far off our predictions are). The nature of our problem should dictate the loss function used. There are several loss functions implemented in Keras, but the most commonly used loss functions are mean_squared_error, categorical_crossentropy, and binary_crossentropy.

As a general rule of thumb, this is how you should choose which loss function to use:

• mean_squared_error if the problem is a regression problem
• categorical_crossentropy if the problem is a multiclass classification problem
• binary_crossentropy if the problem is a binary classification problem

In certain ...