7.1 CNN architecture: convolutional layers, pooling, and fully connected layers
2 min read•july 25, 2024
Convolutional Neural Networks (CNNs) are powerful tools. They use convolutional layers to extract features, pooling layers to reduce dimensions, and fully connected layers for final classification or regression. These components work together to analyze visual data effectively.
CNN architecture design involves arranging layers and fine-tuning hyperparameters. The structure typically includes alternating convolutional and pooling layers, followed by fully connected layers. Careful consideration of factors like , filter sizes, and optimizes performance.
CNN Architecture Components
Convolutional Layers
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Extract features from input data and apply filters to detect patterns
Filters (kernels) slide across input data as learnable parameters
Feature maps result from applying filters to input
determines step size for filter movement
controls output size
operation: output=∑(input∗filter)+bias
Activation functions include , , and enhance non-linearity
Pooling Layers
Reduce spatial dimensions, decrease computational complexity, provide translation invariance
selects maximum value in pooling window
calculates average value in pooling window
applies operation across entire
Window size and stride affect pooling behavior
preserves important features
Fully Connected Layers
Perform final classification or regression, combining features from previous layers
Neurons fully connected to previous layer, multiple layers possible