import numpy as np
import tensorflow as tf
import keras
from keras.datasets import cifar10



def load():

    # Load the CIFAR10 data.
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()

    # Input image dimensions.
    input_shape = x_train.shape[1:]

    # Normalize data.
    x_train = x_train.astype('float32') / 255
    x_test = x_test.astype('float32') / 255

    # Subtract pixel mean
    x_train_mean = np.mean(x_train, axis=0)
    x_train -= x_train_mean
    x_test -= x_train_mean

    print("Load CIFAR-10 dataset.")
    print('x_train shape:', x_train.shape)
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')
    print('y_train shape:', y_train.shape)
    
    # Convert class vectors to binary class matrices.
    num_classes = 10
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    return (x_train, y_train), (x_test, y_test)