In Deep learning algorithms, we need some sort of mechanism to optimize and find the best parameters for our data. We implement this mechanism in the form of losses and loss functions. Neural networks are trained using an optimizer and we are required to choose a loss function while configuring our model. It’s very challenging to choose what loss function we require. Different loss functions play slightly different roles in training neural nets. This article will explain the role of Keras loss functions in training deep neural nets. We will also see the loss functions available in Keras deep learning library.
Keras Loss and Keras Loss Functions
Generally, we train a deep neural network using a stochastic gradient descent algorithm. Here we update weights using backpropagation. The optimization algorithm tries to reduce errors in the next evaluation by changing weights.
While optimization, we use a function to evaluate the weights and try to minimize the error. This objective function is our loss function and the evaluation score calculated by this loss function is called loss. In simple words, losses refer to the quality that is computed by the model and try to minimize during model training.
This loss function has a very important role as the improvement in its evaluation score means a better network.
Available Loss Functions in Keras
1. Hinge Losses in Keras
These are the losses in machine learning which are useful for training different classification algorithms. In support vector machine classifiers we mostly prefer to use hinge losses.
Different types of hinge losses in Keras:
- Hinge
- Categorical Hinge
- Squared Hinge
2. Regression Loss functions in Keras
These are useful to model the linear relationship between several independent and a dependent variable.
Different types of Regression Loss function in Keras:
- Mean Square Error
- Mean Absolute Error
- Cosine Similarity
- Huber Loss
- Mean Absolute Percentage Error
- Mean Squared Logarithmic Error
- Log Cosh
3. Binary and Multiclass Loss in Keras
These loss functions are useful in algorithms where we have to identify the input object into one of the two or multiple classes.
Spam classification is an example of such type of problem statements.
- Binary Cross Entropy.
- Categorical Cross Entropy.
- Poisson Loss.
- Sparse Categorical Cross Entropy.
- KLDivergence
Common Loss and Loss Functions in Keras
1. Squared Error
In Squared Error Loss, we calculate the square of the difference between the original and predicted values. We calculate this for each input data in the training set. The mean of these squared errors is the corresponding loss function and it is called Mean Squared Error. This loss is also known as L2 Loss.
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