CNN architecture, or Convolutional Neural Network architecture, refers to the design and structure of a deep learning model specifically designed for analyzing visual data, such as images. It consists of several key components:
Convolutional Layers: These layers perform the main computation in a CNN by applying filters or kernels to the input data, capturing local patterns and features.
Pooling Layers: Pooling layers reduce the spatial dimensions of the feature maps generated by convolutional layers, helping to extract important information and control the computational complexity.
Activation Functions: Activation functions introduce non-linearity into the network, allowing it to learn complex relationships between features and make the model more expressive.
Fully Connected Layers: Fully connected layers connect all the neurons from the previous layer to the next, enabling high-level feature combination and producing the final predictions or classifications.
Dropout: Dropout is a regularization technique used to randomly deactivate a certain percentage of neurons during training, reducing overfitting and improving generalization.
The overall architecture of a CNN typically follows a pattern of alternating convolutional and pooling layers, with fully connected layers at the end. This architecture, combined with appropriate activation functions, regularization techniques, and optimization algorithms, enables CNNs to effectively learn and recognize visual patterns, making them well-suited for tasks such as image classification, object detection, and image segmentation.