我希望在张量板中有一个混淆矩阵的视觉效果.为此,我正在修改Tensorflow Slim的评估示例：https://github.com/tensorflow/models/blob/master/slim/eval_image_classifier.py

在此示例代码中,Accuracy已经提供但是不可能直接添加“confusion matrix”度量标准,因为它不是流式传输.

流媒体指标和非流媒体指标有什么区别？

因此,我试图像这样添加它：

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)

这会在tensorboard中创建一个图像,但可能存在格式问题.矩阵应在0-1之间归一化,以便产生有意义的图像.

我怎样才能产生有意义的混淆矩阵？我该如何处理多批评估过程？

解决方法:

这是我把它放在一起的东西,它运作得相当好.仍然需要调整一些事情,如刻度线位置等.

这个函数几乎可以为你做所有事情.

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

#

以下是您需要调用此函数的其余代码.

''' 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)

混淆!!!