Fully connected layers (FC layers) are a critical component in Convolutional Neural Networks (CNNs), often used for final classification tasks. Unlike convolutional layers, which focus on spatial hierarchies, FC layers connect every neuron in one layer to every neuron in the next, enabling the network to learn global patterns from the processed features.
Key Features of FC Layers
- All-to-all connectivity: Each neuron is connected to all neurons in the subsequent layer.
- Parameter efficiency: Weights are shared across all spatial positions (unlike dense layers).
- Final decision-making: Typically placed at the end of a CNN to map features to class labels.
Use Cases
- Image classification: After feature extraction, FC layers produce class scores.
- Object detection: Used in conjunction with region proposals to classify detected objects.
- Regression tasks: Can be adapted to predict continuous values instead of classes.
Comparison with Convolutional Layers
| Aspect | Fully Connected Layers | Convolutional Layers |
|---|---|---|
| Connectivity | Complete (dense) | Local (kernel-based) |
| Parameter Sharing | No | Yes |
| Purpose | Final classification | Feature extraction |
Example
For a 28x28 image, a convolutional layer might reduce dimensions to 7x7, while a fully connected layer would then have 7×7×64 = 3136 inputs (assuming 64 feature maps). This transforms spatial data into a flat vector for classification.
For deeper understanding, explore our tutorial on Convolution Layers to see how they work with FC layers in practice. 📘