CNN Deep Learning Architecture Diagram

CNN Image Classification Architecture

A CNN for image classification: two convolutional blocks (conv + batch norm + max pooling) extract features, a flatten layer converts feature maps to a vector, two dense layers learn class representations, and a softmax output produces class probabilities.

Type Schéma fonctionnelNorme Ogata / standard controls textbook conventionMoteur schematex-blockdiagramMis à jour 19/08/2026

Le scénario

An ML student documenting their image-classification CNN architecture for a course report needs a block diagram.

Ce que contient ce dessin

Comprenez les décisions qui le sous-tendent.

Batch normalization after each conv layer inserted before pooling to stabilize training.

Dropout 0.5 between dense layers is the primary regularization mechanism to prevent overfitting.

Softmax output normalizes logits to class probabilities; replace with sigmoid for binary classification.

Add skip connections (ResNet-style) from conv1 to conv2 to combat vanishing gradients in deeper variants.

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