Python code snippet to plot a greyscale confusion matrix. Its tailored to a project I did, but you get the idea.

plot_confusion_matrix.py

from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import random

def main():
    true_labels = [random.randint(1, 10) for i in range(100)]
    predicted_labels = [random.randint(1, 10) for i in range(100)]
    plot = getConfusionMatrixPlot(true_labels, predicted_labels)
    plot.show()

def getConfusionMatrixPlot(true_labels, predicted_labels):
    # Compute confusion matrix
    cm = confusion_matrix(true_labels, predicted_labels)

    # create figure
    fig = plt.figure()
    plt.clf()
    ax = fig.add_subplot(111)
    ax.set_aspect(1)
    res = ax.imshow(cm, cmap=plt.cm.binary,
                    interpolation='nearest', vmin=0, vmax=10)

    # add color bar
    plt.colorbar(res)

    # annotate confusion entries
    width = len(cm)
    height = len(cm[0])

    for x in xrange(width):
        for y in xrange(height):
            ax.annotate(str(cm[x][y]), xy=(y, x), horizontalalignment='center',
                        verticalalignment='center', color=getFontColor(cm[x][y]))

    # add genres as ticks
    alphabet = ['blues', 'classical', 'country', 'disco',
                'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock']
    plt.xticks(range(width), alphabet[:width], rotation=30)
    plt.yticks(range(height), alphabet[:height])
    return plt

def getFontColor(value):
    if value < 5:
        return "black"
    else:
        return "white"

if __name__ == "__main__":
    main()

Thanks, the code helps me a lot