A Dense Layer, also known as a fully connected layer, is a fundamental component in artificial neural networks, particularly in deep learning models. In a Dense Layer, each neuron is connected to every neuron in the previous layer, which enables the model to learn complex patterns and relationships in the input data.
The primary function of a Dense Layer is to perform a linear transformation of the input data followed by a non-linear activation function. Mathematically, this can be expressed as:
output = activation_function(weights * input + bias)Where:
- weights are the learned parameters that determine the strength of the connection between neurons.
- input is the data fed into the layer.
- bias is an additional parameter that allows the model to fit the training data better.
- activation_function is a non-linear function, such as ReLU (Rectified Linear Unit) or Sigmoid, that introduces non-linearity into the model.
Dense Layers are commonly used in the hidden layers of neural networks and can also be the final layer for classification tasks. In the latter case, a softmax activation function is often applied to output a probability distribution over the predicted classes.
Overall, Dense Layers play a crucial role in enabling neural networks to capture intricate patterns in high-dimensional data, making them integral to various applications, including image recognition, natural language processing, and more.