Fully Connected (FC) layers in CNNs are traditional neural network layers where every neuron connects to every neuron in the previous layer. They typically appear at the end of the network for final classification or regression.
Key Characteristics:
• Each neuron receives all features from previous layer
• No spatial structure preservation
• High parameter count
• Excellent for decision making
💡 Key Insight
FC layers act as the "brain" that makes final decisions based on features extracted by convolutional layers.
Why Use FC Layers?
🎯
Final Classification
Convert feature maps to class probabilities
🔗
Feature Combination
Combine spatial features for global understanding
📐
Dimensionality Reduction
Reduce feature dimensions to output size
📈
Complex Patterns
Learn complex non-linear relationships
CNN Architecture with FC Layers
Conv Layers
Feature Extraction 32×32×64
→
Pool Layers
Downsampling 16×16×64
→
Flatten
Reshape 16384 × 1
→
FC Layer 1
Dense 512 neurons
→
FC Layer 2
Output 10 classes
Click on any layer to learn more about its role in the CNN!