#!/usr/bin/python3.5
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['FangSong']  #可顯示中文字符
plt.rcParams['axes.unicode_minus']=False
classes = ['a','b','c','d','e','f','g']
confusion_matrix = np.array([(99,1,2,2,0,0,6),(1,98,7,6,2,1,1),(0,0,86,0,0,2,0),(0,0,0,86,1,0,0),(0,0,0,1,94,1,0),(0,1,5,1,0,96,8),(0,0,0,4,3,0,85)],dtype=np.float64)
 
plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Oranges)  #按照像素顯示出矩陣
plt.title('混淆矩陣')
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=-45)
plt.yticks(tick_marks, classes)
 
thresh = confusion_matrix.max() / 2.
#iters = [[i,j] for i in range(len(classes)) for j in range((classes))]
#ij配對，遍歷矩陣迭代器
iters = np.reshape([[[i,j] for j in range(7)] for i in range(7)],(confusion_matrix.size,2))
for i, j in iters:
    plt.text(j, i, format(confusion_matrix[i, j]),fontsize=7)   #顯示對應的數字
 
plt.ylabel('真實類別')
plt.xlabel('預測類別')
plt.tight_layout()
plt.show()

    fig ,ax= plt.subplots()
    plt.plot(np.arange(iterations), fig_acc，'b')
    plt.plot(np.arange(iterations), fig_realacc, 'r')
    ax.set_xlabel('迭代次數')
    ax.set_ylabel('正確率（%）')
 
    labels = ["訓練正確率", "測試正確率"]
    # labels = [l.get_label() for l in lns]
    plt.legend( labels, loc=7)
    plt.show()