CNN (Convolutional Neural Network)

A CNN is a neural network architecture designed specifically for processing grid data such as images. It uses convolution operations to extract spatial features.

CNN Architecture

Input → [Conv → ReLU → Pool] × N → Flatten → Dense → Output

CNN with Keras (MNIST)

from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(10, activation='softmax')
])

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

Training on MNIST

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=5, batch_size=128, validation_split=0.1)

loss, acc = model.evaluate(X_test, y_test)
print(f"Test Accuracy: {acc:.4f}")

Pooling Layers

Filters and Kernels

from tensorflow import keras
from tensorflow.keras import layers

# Feature map visualization
model = keras.Sequential([
    layers.Conv2D(16, (3, 3), activation='relu', input_shape=(28, 28, 1))
])
model.compile()

# Displaying the filters
weights = model.layers[0].get_weights()[0]
print(f"Filter shape: {weights.shape}")  # (3, 3, 1, 16)

Data Augmentation

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rotation_range=10,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=True,
    zoom_range=0.1
)

Transfer Learning

from tensorflow import keras
from tensorflow.keras import layers

base_model = keras.applications.MobileNetV2(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)
base_model.trainable = False

model = keras.Sequential([
    base_model,
    layers.GlobalAveragePooling2D(),
    layers.Dense(256, activation='relu'),
    layers.Dropout(0.3),
    layers.Dense(num_classes, activation='softmax')
])

CNN Hyperparameters

Next chapter: RNN, an architecture for sequential data.

Next: RNN →