Cross Validation in Keras
From the Keras documentation, you can load the data into Train and Test sets like this:
(X_train, y_train), (X_test, y_test) = mnist.load_data()

As for cross validation, you could follow this example from here.
from sklearn.model_selection import StratifiedKFold

def load_data():
    # load your data using this function

def create model():
    # create your model using this function

def train_and_evaluate__model(model, data_train, labels_train, data_test, labels_test):
    model.fit...
    # fit and evaluate here.

if __name__ == &quot;__main__&quot;:
    n_folds = 10
    data, labels, header_info = load_data()
    skf = StratifiedKFold(labels, n_folds=n_folds, shuffle=True)

    for i, (train, test) in enumerate(skf):
        print &quot;Running Fold&quot;, i+1, &quot;/&quot;, n_folds
        model = None # Clearing the NN.
        model = create_model()
        train_and_evaluate_model(model, data[train], labels[train], data[test], labels[test])