def plot_confusion_matrix(cm, classes, normalize=False, title=
    'Confusion matrix', cmap=None, print_matrix=True):
    """Check if Normalization Option is Set to True. If so, normalize the raw confusion matrix before visualizing
    #Other code should be equivalent to your previous function."""
    import itertools
    import numpy as np
    import matplotlib.pyplot as plt
    if cmap == None:
        cmap = plt.get_cmap('Blues')
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, (np.newaxis)]
        print('Normalized confusion matrix')
    else:
        print('Confusion matrix, without normalization')
    print(cm)
    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.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()
