About This Architecture

Two-layer CNN architecture processing 224x224x3 RGB images through stacked convolutional, pooling, batch normalization, and dropout blocks. Layer 1 applies 32 filters with 3x3 kernels followed by 2x2 max pooling and batch norm, reducing spatial dimensions to 112x112x32; Layer 2 applies 64 filters with identical kernel size, further reducing to 56x56x64. The flattened feature maps (200,704 units) feed into a 512-unit dense layer with ReLU activation and 0.5 dropout, culminating in a 10-class softmax output layer for multi-class classification. This architecture demonstrates regularization best practices—batch normalization, dropout at 0.25 and 0.5 rates, and parameter-efficient convolutions—essential for preventing overfitting on moderate datasets. Fork and customize this diagram on Diagrams.so to adjust filter counts, kernel sizes, or add skip connections for your specific classification task. The explicit dimension tracking and parameter counts (896 params in Layer 1, 18,496 in Layer 2) help practitioners understand computational complexity and memory footprint.

People also ask

What does a typical two-layer CNN architecture look like with batch normalization and dropout for image classification?

This diagram shows a two-layer CNN that processes 224x224x3 images through Conv2D layers (32 and 64 filters), ReLU activations, 2x2 max pooling for spatial reduction, batch normalization for stable training, and dropout (0.25–0.5) for regularization. The flattened features feed into a 512-unit dense layer and 10-class softmax output, demonstrating modern best practices for preventing overfitting w