def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    '\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    '
    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)
    if normalize:
        cm = (cm.astype('float') / cm.sum(axis=1)[:, np.newaxis])
        print('Normalized confusion matrix')
    else:
        print('Confusion matrix, without normalization')
    print(cm)
    thresh = (cm.max() / 2.0)
    for (i, j) in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[(i, j)], horizontalalignment='center', color=('white' if (cm[(i, j)] > thresh) else 'black'))
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    '\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    '
    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)
    if normalize:
        cm = (cm.astype('float') / cm.sum(axis=1)[:, np.newaxis])
        print('Normalized confusion matrix')
    else:
        print('Confusion matrix, without normalization')
    print(cm)
    thresh = (cm.max() / 2.0)
    for (i, j) in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[(i, j)], horizontalalignment='center', color=('white' if (cm[(i, j)] > thresh) else 'black'))
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')