def plot_confusion_matrix(cm, classes, normalize=False, title=
    'Confusion matrix', cmap=None, save_as=None):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    import itertools
    import matplotlib.pyplot as plt
    import numpy as np
    plt.figure()
    if cmap is None:
        cmap = plt.cm.Blues
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, (np.newaxis)]
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.0
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment='center',
            color='white' if cm[i, j] > thresh else 'black')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()
    if save_as is not None:
        plt.savefig(save_as, bbox_inches='tight')
    plt.show()
    plt.clf()
