plot.py

def plot_confusion_matrix(cm, class_names):
    """
	Code from: https://towardsdatascience.com/exploring-confusion-matrix-evolution-on-tensorboard-e66b39f4ac12
    Returns a matplotlib figure containing the plotted confusion matrix.
    
    Args:
       cm (array, shape = [n, n]): a confusion matrix of integer classes
       class_names (array, shape = [n]): String names of the integer classes
    """
    
    figure = plt.figure(figsize=(8, 8))
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    plt.title("Confusion matrix")
    plt.colorbar()
    tick_marks = np.arange(len(class_names))
    plt.xticks(tick_marks, class_names, rotation=45)
    plt.yticks(tick_marks, class_names)
    
    # Normalize the confusion matrix.
    cm = np.around(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], decimals=2)
    
    # Use white text if squares are dark; otherwise black.
    threshold = cm.max() / 2.
    
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        color = "white" if cm[i, j] > threshold else "black"
        plt.text(j, i, cm[i, j], horizontalalignment="center", color=color)
        
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    return figure

def plot_to_image(figure):
    """
    Converts the matplotlib plot specified by 'figure' to a PNG image and
    returns it. The supplied figure is closed and inaccessible after this call.
    """
    
    buf = io.BytesIO()
    
    # Use plt.savefig to save the plot to a PNG in memory.
    plt.savefig(buf, format='png')
    
    # Closing the figure prevents it from being displayed directly inside
    # the notebook.
    plt.close(figure)
    buf.seek(0)
    
    # Use tf.image.decode_png to convert the PNG buffer
    # to a TF image. Make sure you use 4 channels.
    image = tf.image.decode_png(buf.getvalue(), channels=4)
    
    # Use tf.expand_dims to add the batch dimension
    image = tf.expand_dims(image, 0)
    
    return image