A logit layer is a specific type of layer used in neural networks, particularly in classification tasks. Its primary role is to transform the outputs of the previous layer (often called logits) into probabilities that sum to one. This transformation is achieved using the logistic function, also known as the sigmoid function.
In technical terms, the logit layer takes an input vector of real numbers and applies the logistic function to each element. The logistic function is defined as:
σ(x) = 1 / (1 + e^(-x))
Where e is the base of the natural logarithm, and x is the input value. The output of the logit layer is a vector of values between 0 and 1, representing the predicted probabilities of each class in a multi-class classification problem.
For binary classification, the logit layer typically has one output neuron (which outputs a probability) and often uses a binary cross-entropy loss function during training. In multi-class settings, it often employs the softmax function in conjunction with a logit layer to ensure that the probabilities across all classes sum to one.
The logit layer is crucial in making predictions interpretable, as the probabilities can be directly compared to determine the most likely class. This layer is commonly found in the output stage of neural networks designed for tasks such as image recognition, natural language processing, and other machine learning applications where classification is required.