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

```python 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

```python 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.