data_loading_and_preprocessing.py

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import cifar10
from keras.utils import to_categorical

# Load CIFAR-10 dataset
(X_train, y_train), (X_test, y_test) = cifar10.load_data()

# Display data shapes
print(f"X_train shape: {X_train.shape}")
print(f"y_train shape: {y_train.shape}")
print(f"X_test shape: {X_test.shape}")

# Define labels
labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 
          'dog', 'frog', 'horse', 'ship', 'truck']

# Visualize class distribution in training set
classes_name = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']
classes, counts = np.unique(y_train, return_counts=True)
plt.barh(classes_name, counts)
plt.title('Class distribution in training set')
plt.show()

# Visualize class distribution in testing set
classes, counts = np.unique(y_test, return_counts=True)
plt.barh(classes_name, counts)
plt.title('Class distribution in testing set')
plt.show()

# Scale the data
X_train = X_train / 255.0
X_test = X_test / 255.0

# Transform target variable into one-hot encoding
y_cat_train = to_categorical(y_train, 10)
y_cat_test = to_categorical(y_test, 10)