In Keras, loss functions represent measures of how well a neural network is approximating the target function during training. The aim of training is to minimize this loss function as much as possible by adjusting the model parameters, such as weights and biases.
The loss function takes two inputs: - the predicted output of the neural network - the true output, which is the ground-truth label for that particular example
The loss function will then output a scalar value that indicates how well the network’s output matches the true output, and this value acts as an indicator of the network’s performance. The optimizer uses this value to update the weights and biases of the network to improve its performance.
Keras provides a wide range of loss functions that are tailored for different types of problems, such as classification, regression, and sequence prediction. Some commonly used loss functions are:
- Mean Squared Error (MSE): Used for regression problems, where the target output is continuous. This loss function computes the squared difference between the predicted output and the true output, averaged over all training examples, and gives importance to large differences.
- Mean Absolute Error (MAE): Also used for regression problems, this loss function computes the absolute difference between the predicted output and the true output, averaged over all training examples. It gives equal importance to small and large differences, and is less sensitive to outliers than MSE.
- Binary Cross Entropy (BCE): Used for binary classification problems, where the target output is either 0 or 1. This loss function measures the difference between the predicted probability of the positive class (class 1) and the true probability, and is commonly used with sigmoid activation in the last layer.
- Categorical Cross Entropy (CCE): Used for multi-class classification problems, where the target output is one-hot encoded. This loss function measures the difference between the predicted probability distribution over all classes and the true probability distribution, and is commonly used with softmax activation in the last layer.
These are just a few examples of the many loss functions available in Keras, and choosing the right one depends on the problem at hand.