Working and Structure of Fully Connected Layers in Neural Networks
The extensive connectivity allows for comprehensive information processing and feature integration, making FC layers essential for tasks requiring complex pattern recognition.
Key Operations in Fully Connected Layers
1. Input Processing
Each neuron in an FC layer receives inputs from all neurons of the previous layer, with each connection having a specific weight and each neuron incorporating a bias. The input to each neuron is a weighted sum of these inputs plus a bias:
[Tex]z_j = \sum_i (w_{ij}.x_i) +b_j [/Tex]
Here, [Tex]w_{ij}[/Tex] is the weight from neuron i of the previous layer to neuron j, [Tex]x_i[/Tex] is the input from neuron i, and [Tex]b_j[/Tex] is the bias for neuron j
2. Activation
The weighted sum is then processed through a non-linear activation function, such as ReLU, Sigmoid, or Tanh. This step introduces non-linearity, enabling the network to learn complex functions:
[Tex]a_j = f(z_j)[/Tex]
f denotes the activation function, transforming the linear combination of inputs into a non-linear output.
Example Configuration
Consider a neural network transition from a layer with 4 neurons to an FC layer with 3 neurons:
- Previous Layer (4 neurons) → Fully Connected Layer (3 neurons)
Each neuron in the FC layer receives inputs from all four neurons of the previous layer, resulting in a configuration that involves 12 weights and 3 biases. This design exemplifies the FC layer’s role in transforming and combining features from the input layer, facilitating the network’s ability to perform complex decision-making tasks.
What is Fully Connected Layer in Deep Learning?
Fully Connected (FC) layers, also known as dense layers, are a crucial component of neural networks, especially in the realms of deep learning. These layers are termed “fully connected” because each neuron in one layer is connected to every neuron in the preceding layer, creating a highly interconnected network.
This article explores the structure, role, and applications of FC layers, along with their advantages and limitations.
Table of Content
- Structure of Fully Connected Layers
- Working and Structure of Fully Connected Layers in Neural Networks
- Key Role of Fully Connected Layers in Neural Networks
- Advantages of Fully Connected Layers
- Limitations of Fully Connected Layers
- Conclusion
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