Convolutional Neural Networks (CNNs) Explained

Regular neural networks don't understand spatial structure. Feed an image as a flat array and you lose the fact that nearby pixels are related. CNNs solve this.

The Problem with Regular Networks

A 224x224 color image has 224 × 224 × 3 = 150,528 inputs. A fully connected layer with 1000 neurons = 150 million parameters. That's insane and ignores spatial patterns.

How Convolution Works

Instead of connecting every pixel to every neuron, slide a small filter across the image:

Image:          Filter (3x3):    Output:
1 2 3 4         1 0 1           
2 3 4 5   *     0 1 0     =     Result
3 4 5 6         1 0 1
4 5 6 7

The filter detects patterns (edges, textures, shapes). Multiple filters = multiple pattern detectors.

CNN Architecture

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
    # Convolution + Pooling Block 1
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
    layers.MaxPooling2D((2, 2)),
    
    # Convolution + Pooling Block 2
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    
    # Convolution + Pooling Block 3
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    
    # Flatten and Dense layers
    layers.Flatten(),
    layers.Dense(256, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(10, activation='softmax')
])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

Key Layers

Convolutional Layer:

• Applies filters to detect features
• Parameters: number of filters, filter size, stride, padding

Pooling Layer:

• Reduces spatial dimensions
• MaxPooling takes the maximum value in each region
• Makes features translation-invariant

Flatten:

• Converts 2D feature maps to 1D vector

What CNNs Learn

Each layer learns different features:

Layer	What It Learns
Early layers	Edges, colors, simple textures
Middle layers	Shapes, patterns, object parts
Deep layers	Complex objects, faces, scenes

Practical Example: Image Classification

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Data augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True
)

train_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'
)

# Train
history = model.fit(
    train_generator,
    epochs=20,
    validation_data=val_generator
)

Key Takeaway

CNNs use convolutions to efficiently process images by exploiting spatial structure. They learn hierarchical features automatically - edges in early layers, complex objects in deeper layers. Use them for any image-related task. Start with a simple architecture, add data augmentation, and consider transfer learning for faster results.