About This Architecture

Convolutional neural network forward pass sequence from input tensor through three progressive convolution blocks with 32, 64, and 128 filters, followed by flatten and fully connected layers for 4-class classification. The diagram traces data flow through Phase 1 input ingestion, Phase 2-4 convolution processing with activation functions, and Phase 5 flatten-to-output classification. This architecture demonstrates the standard CNN pattern for image feature extraction and hierarchical representation learning. Fork this diagram on Diagrams.so to customize filter counts, add batch normalization, or adapt for your dataset dimensions and class count.

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How does data flow through a convolutional neural network from input to classification output?

This CNN forward pass diagram shows a 7x7x12 input tensor flowing through three convolution blocks with increasing filter depths (32, 64, 128), activation functions at each stage, a flatten layer, and a fully connected network producing 4-class predictions. Each phase represents a distinct processing stage where spatial features are extracted and refined before classification.