Output Layer
An Output Layer is the final layer of a neural network that converts the information learned by earlier layers into the model's final prediction or output.
What is Output Layer?
The output layer receives processed information from the network's hidden layers and produces the result required for the task. Its structure and activation function depend on the problem. For example, a classification model may use a sigmoid or softmax output, while a regression model may produce a continuous numerical value.
Why is Output Layer Important?
The output layer determines how a neural network communicates its prediction. Choosing an appropriate output structure and activation function helps ensure that the model's final results are represented in a form suitable for the intended task.
Common use cases
Output layers are commonly used in classification, regression, image recognition, natural language processing, speech recognition, forecasting, and other neural network applications.