tensorboard_utils.py

from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import itertools
import io
import tensorflow as tf

def plot_confusion_matrix(cm, class_names = None):
    """
    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
    """

    if class_names is None:
        class_names = [i for i in range(len(cm))]
    
    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

def createConfusionMatrix(y_true, y_pred):
    # Build confusion matrix
    cm_matrix = confusion_matrix(y_true, y_pred)
    figure = plot_confusion_matrix(cm_matrix, class_names=None)

    return figure