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Data Loading and Preprocessing Module for CIFAR-10 Image Classification.
This module handles:
- Loading the CIFAR-10 dataset from Keras
- Normalizing pixel values to [0, 1]
- One-hot encoding labels
- Data augmentation via ImageDataGenerator
"""
import numpy as np
from tensorflow import keras
from keras.api.datasets import cifar10
from keras.api.utils import to_categorical
from keras.src.legacy.preprocessing.image import ImageDataGenerator
# CIFAR-10 class names (in order of label index 0-9)
CLASS_NAMES = [
'airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck'
]
NUM_CLASSES = 10
IMG_SHAPE = (32, 32, 3)
def load_cifar10_data():
"""
Load the CIFAR-10 dataset and return raw train/test splits.
Returns:
tuple: (x_train, y_train), (x_test, y_test)
- x: uint8 images of shape (N, 32, 32, 3)
- y: integer labels of shape (N, 1)
"""
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
return (x_train, y_train), (x_test, y_test)
def preprocess_data(x_train, y_train, x_test, y_test):
"""
Normalize images and one-hot encode labels.
Args:
x_train: Training images (uint8).
y_train: Training labels (int).
x_test: Test images (uint8).
y_test: Test labels (int).
Returns:
tuple: (x_train, y_train, x_test, y_test)
- x: float32 images normalized to [0, 1]
- y: one-hot encoded labels of shape (N, 10)
"""
# Normalize pixel values to [0, 1]
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0
# One-hot encode labels
y_train = to_categorical(y_train, NUM_CLASSES)
y_test = to_categorical(y_test, NUM_CLASSES)
return x_train, y_train, x_test, y_test
def create_data_augmentation_generator():
"""
Create an ImageDataGenerator with augmentation transformations.
Augmentations applied:
- Random rotation up to 15 degrees
- Random width/height shift up to 10%
- Random horizontal flip
- Random zoom up to 10%
Returns:
ImageDataGenerator: configured generator for training data augmentation.
"""
datagen = ImageDataGenerator(
rotation_range=15,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
zoom_range=0.1,
fill_mode='nearest'
)
return datagen
def get_prepared_data():
"""
Full pipeline: load CIFAR-10, preprocess, and return ready-to-use data.
Returns:
dict with keys:
'x_train', 'y_train', 'x_test', 'y_test': processed arrays
'datagen': ImageDataGenerator for augmented training
"""
(x_train, y_train), (x_test, y_test) = load_cifar10_data()
x_train, y_train, x_test, y_test = preprocess_data(
x_train, y_train, x_test, y_test
)
datagen = create_data_augmentation_generator()
datagen.fit(x_train)
return {
'x_train': x_train,
'y_train': y_train,
'x_test': x_test,
'y_test': y_test,
'datagen': datagen
}
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