Tensorflow Confusion Matrix in TensorBoard

Question

I want to have a visual of confusion matrix in tensorboard. To do this, I am modifying Evaluation example of Tensorflow Slim: https://github.com/tensorflow/models/blob/master/slim/eval_image_classifier.py

In this example code, Accuracy already provided but it is not possible to add "confusion matrix" metric directly because it is not streaming.

What is difference between streaming metrics and non-streaming ones?

Therefore, I tried to add it like this:

c_matrix = slim.metrics.confusion_matrix(predictions, labels)

#These operations needed for image summary
c_matrix = tf.cast(c_matrix, uint8)
c_matrix = tf.expand_dims(c_matrix, 2)
c_matrix = tf.expand_dims(c_matrix, 0)

op = tf.image_summary("confusion matrix", c_matrix, collections=[])
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)

This creates an image in tensorboard but probably there is a formatting problem. Matrix should be normalized between 0-1 so that It produces meaningful image.

How can I produce a meaningful confusion matrix? How can I deal with multi batch evaluation process?

Answer 1

Here is something I have put together That works reasonably well. Still need to adjust a few things like the tick placements etc.

Here is the function that will pretty much do everything for you.

from textwrap import wrap
import re
import itertools
import tfplot
import matplotlib
import numpy as np
from sklearn.metrics import confusion_matrix



def plot_confusion_matrix(correct_labels, predict_labels, labels, title='Confusion matrix', tensor_name = 'MyFigure/image', normalize=False):
''' 
Parameters:
    correct_labels                  : These are your true classification categories.
    predict_labels                  : These are you predicted classification categories
    labels                          : This is a lit of labels which will be used to display the axix labels
    title='Confusion matrix'        : Title for your matrix
    tensor_name = 'MyFigure/image'  : Name for the output summay tensor

Returns:
    summary: TensorFlow summary 

Other itema to note:
    - Depending on the number of category and the data , you may have to modify the figzie, font sizes etc. 
    - Currently, some of the ticks dont line up due to rotations.
'''
cm = confusion_matrix(correct_labels, predict_labels, labels=labels)
if normalize:
    cm = cm.astype('float')*10 / cm.sum(axis=1)[:, np.newaxis]
    cm = np.nan_to_num(cm, copy=True)
    cm = cm.astype('int')

np.set_printoptions(precision=2)
###fig, ax = matplotlib.figure.Figure()

fig = matplotlib.figure.Figure(figsize=(7, 7), dpi=320, facecolor='w', edgecolor='k')
ax = fig.add_subplot(1, 1, 1)
im = ax.imshow(cm, cmap='Oranges')

classes = [re.sub(r'([a-z](?=[A-Z])|[A-Z](?=[A-Z][a-z]))', r'\1 ', x) for x in labels]
classes = ['\n'.join(wrap(l, 40)) for l in classes]

tick_marks = np.arange(len(classes))

ax.set_xlabel('Predicted', fontsize=7)
ax.set_xticks(tick_marks)
c = ax.set_xticklabels(classes, fontsize=4, rotation=-90,  ha='center')
ax.xaxis.set_label_position('bottom')
ax.xaxis.tick_bottom()

ax.set_ylabel('True Label', fontsize=7)
ax.set_yticks(tick_marks)
ax.set_yticklabels(classes, fontsize=4, va ='center')
ax.yaxis.set_label_position('left')
ax.yaxis.tick_left()

for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
    ax.text(j, i, format(cm[i, j], 'd') if cm[i,j]!=0 else '.', horizontalalignment="center", fontsize=6, verticalalignment='center', color= "black")
fig.set_tight_layout(True)
summary = tfplot.figure.to_summary(fig, tag=tensor_name)
return summary

#

And here is the rest of the code that you will need to call this functions.

''' confusion matrix summaries '''
img_d_summary_dir = os.path.join(checkpoint_dir, "summaries", "img")
img_d_summary_writer = tf.summary.FileWriter(img_d_summary_dir, sess.graph)
img_d_summary = plot_confusion_matrix(correct_labels, predict_labels, labels, tensor_name='dev/cm')
img_d_summary_writer.add_summary(img_d_summary, current_step)

Confuse away!!!