DL0022 CNN Architecture

A Convolutional Neural Network (CNN) is structured to efficiently recognize complex patterns in data. It begins with an input layer that feeds in raw data. Convolutional layers then extract key features using filters, which are enhanced through non-linear activation functions like ReLU. Pooling layers are used to reduce the size or dimensions of these features, thereby improving computational efficiency and promoting invariance to small shifts. The extracted features are flattened and passed through fully connected layers that culminate in an output layer for final predictions, typically employing a softmax function for classification tasks. Optional techniques, such as dropout and batch normalization, further refine learning and help prevent overfitting.

(1) Input Layer: Accepts raw data as multi-dimensional arrays.
(2) Convolutional Layers: Use learnable filters (kernels) to scan the input and extract local features.
(3) Activation Functions: Apply non-linearity (commonly ReLU) after each convolution operation.
(4) Pooling Layers: Downsample feature maps using techniques like max or average pooling to reduce spatial dimensions and computations.
(5) Stacked Convolutional and Pooling Blocks: Multiple iterations to progressively extract intricate hierarchical features.
(6) Flattening: Converts feature maps into one-dimensional vectors.
(7) Fully Connected Layers: Learn complex patterns and perform decision-making.
(8) Output Layer: Produces final predictions using appropriate activation functions (e.g., softmax for classification)
(9) Additional Components (Optional): Dropout for regularization, batch normalization for training stability, and skip connections in more advanced models.

Below is a visual representation of a typical CNN architecture. Padding is used in convolution to maintain dimensions.