CNN Architecture

A typical CNN architecture alternates convolution and pooling layers, ending with fully connected layers for classification. The feature extraction part (conv+pool) learns patterns; the classification part makes predictions.

Typical Structure

Input → [Conv → Activation → Pooling] (repeat) → Flatten → Dense → Output

Each conv layer learns increasingly complex features. Early conv: edges, corners. Middle conv: textures, patterns. Late conv: object parts, shapes.

Example: Simple CNN for MNIST

model = Sequential([
    Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
    MaxPooling2D((2,2)),
    Conv2D(64, (3,3), activation='relu'),
    MaxPooling2D((2,2)),
    Conv2D(64, (3,3), activation='relu'),
    Flatten(),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

Depth (Number of Filters)

Common pattern: double the number of filters after each pooling. Example: 32 → 64 → 128. This compensates for reduced spatial size.

Receptive Field

The region of the input that influences a neuron. Deeper layers have larger receptive fields, allowing them to capture larger patterns.

Two Minute Drill

Typical CNN: conv+pool blocks → flatten → dense.
Depth (filters) often increases after each pooling.
Early layers detect simple patterns; deeper layers detect complex objects.
Receptive field grows with depth.

Need more clarification?

Drop us an email at career@quipoinfotech.com

Welcome to Quipoin

Quipoin Menu

CNN Architecture

Need more clarification?