【Python实战】——神经网络识别手写数字（一）+https://developer.aliyun.com/article/1506500
2.3 神经网络模型定义
运行程序：
ANN = NeuralNetwork(num_of_in_nodes = image_pixels, #输入 num_of_out_nodes = 10, #输出节点数 num_of_hidden_nodes = 100,#隐藏节点 learning_rate = 0.1)#学习率
2.4 模型训练
2.4.1 预测概率
运行程序：
for i in range(len(train_imgs)): ANN.train(train_imgs[i], train_labels_one_hot[i]) for i in range(20): res = ANN.run(test_imgs[i]) print(test_labels[i], np.argmax(res), np.max(res))
运行结果：
[7.] 7 0.9992648448921 [2.] 2 0.9040034245332168 [1.] 1 0.9992201001324703 [0.] 0 0.9923701545281887 [4.] 4 0.989297708155559 [1.] 1 0.9984582148795715 [4.] 4 0.9957673752296046 [9.] 9 0.9889417895800644 [5.] 6 0.5009071817613537 [9.] 9 0.9879513019542627 [0.] 0 0.9932950902790246 [6.] 6 0.9387061553685657 [9.] 9 0.9962530965286298 [0.] 0 0.9974524110371016 [1.] 1 0.9991354417269441 [5.] 5 0.7607733657668813 [9.] 9 0.9968080255475414 [7.] 7 0.9967748204232602 [3.] 3 0.8820920415159276 [4.] 4 0.9978584850755227
2.4.2 训练集正确率
运行程序：
corrects, wrongs = ANN.evaluate(train_imgs, train_labels)#训练集判别正确和错误数量 print("accuracy train: ", corrects / ( corrects + wrongs))##正确率
运行结果：
accuracy train: 0.9425333333333333
2.4.3 测试集正确率
运行程序：
corrects, wrongs = ANN.evaluate(test_imgs, test_labels) print("accuracy: test", corrects / ( corrects + wrongs))#测试集正确率
运行结果：
accuracy: test 0.9412
2.4.4 训练集判别矩阵
运行程序：
cm = ANN.confusion_matrix(train_imgs, train_labels) print(cm) #训练集判别矩阵
运行结果：
[[5822 1 54 35 15 41 47 12 31 31] [ 2 6638 62 31 17 24 21 64 163 14] [ 6 19 5487 57 16 9 2 45 16 4] [ 7 27 87 5773 3 130 3 16 148 67] [ 11 11 68 8 5332 34 12 48 28 44] [ 10 4 6 69 0 4952 34 5 32 5] [ 31 5 53 19 49 96 5782 5 37 2] [ 1 9 45 35 6 6 0 5812 5 28] [ 20 9 70 32 9 37 15 11 5209 9] [ 13 19 26 72 395 92 2 247 182 5745]]
2.4.5 不同数字预测精确率
运行程序：
for i in range(10): print("digit: ", i, "precision: ", ANN.precision(i, cm))
运行结果：
digit: 0 precision: 0.9829478304913051 digit: 1 precision: 0.9845743102936814 digit: 2 precision: 0.9209466263846928 digit: 3 precision: 0.9416082205186755 digit: 4 precision: 0.9127011297500855 digit: 5 precision: 0.9134845969378343 digit: 6 precision: 0.9770192632646164 digit: 7 precision: 0.9276935355147645 digit: 8 precision: 0.8902751666381815 digit: 9 precision: 0.9657085224407463
2.5 结果可视化
2.5.1 每次epoch训练预测情况
运行程序：
epochs = 30 train_acc=[] test_acc=[] NN = NeuralNetwork(num_of_in_nodes = image_pixels, num_of_out_nodes = 10, num_of_hidden_nodes = 100, learning_rate = 0.1) for epoch in range(epochs): print("epoch: ", epoch) for i in range(len(train_imgs)): NN.train(train_imgs[i], train_labels_one_hot[i]) corrects, wrongs = NN.evaluate(train_imgs, train_labels) print("accuracy train: ", corrects / ( corrects + wrongs)) train_acc.append(corrects / ( corrects + wrongs)) corrects, wrongs = NN.evaluate(test_imgs, test_labels) print("accuracy: test", corrects / ( corrects + wrongs)) test_acc.append(corrects / ( corrects + wrongs))
运行结果：
epoch: 0 accuracy train: 0.94455 accuracy: test 0.9422 epoch: 1 accuracy train: 0.9628 accuracy: test 0.9579 epoch: 2 accuracy train: 0.9699 accuracy: test 0.9637 epoch: 3 accuracy train: 0.9761166666666666 accuracy: test 0.9649 epoch: 4 accuracy train: 0.979 accuracy: test 0.9662 epoch: 5 accuracy train: 0.9820833333333333 accuracy: test 0.9679 epoch: 6 accuracy train: 0.9838166666666667 accuracy: test 0.9697 epoch: 7 accuracy train: 0.9845666666666667 accuracy: test 0.97 epoch: 8 accuracy train: 0.9855333333333334 accuracy: test 0.9703 epoch: 9 accuracy train: 0.9868166666666667 accuracy: test 0.97 epoch: 10 accuracy train: 0.9878166666666667 accuracy: test 0.9714 epoch: 11 accuracy train: 0.98845 accuracy: test 0.9716 epoch: 12 accuracy train: 0.98905 accuracy: test 0.9721 epoch: 13 accuracy train: 0.9898166666666667 accuracy: test 0.9723 epoch: 14 accuracy train: 0.9903 accuracy: test 0.9722 epoch: 15 accuracy train: 0.9907666666666667 accuracy: test 0.9719 epoch: 16 accuracy train: 0.9910833333333333 accuracy: test 0.9715 epoch: 17 accuracy train: 0.9918 accuracy: test 0.9714 epoch: 18 accuracy train: 0.9924166666666666 accuracy: test 0.971 epoch: 19 accuracy train: 0.99265 accuracy: test 0.9712 epoch: 20 accuracy train: 0.9932833333333333 accuracy: test 0.972 epoch: 21 accuracy train: 0.9939333333333333 accuracy: test 0.9716 epoch: 22 accuracy train: 0.9944333333333333 accuracy: test 0.972 epoch: 23 accuracy train: 0.9948 accuracy: test 0.9719 epoch: 24 accuracy train: 0.9950833333333333 accuracy: test 0.9718 epoch: 25 accuracy train: 0.9950833333333333 accuracy: test 0.9722 epoch: 26 accuracy train: 0.99525 accuracy: test 0.9725 epoch: 27 accuracy train: 0.9955833333333334 accuracy: test 0.972 epoch: 28 accuracy train: 0.9958166666666667 accuracy: test 0.9717 epoch: 29 accuracy train: 0.9962666666666666 accuracy: test 0.9717
2.5.2 迭代30次正确率绘图
运行程序：
#正确率绘图 # matplotlib其实是不支持显示中文的 显示中文需要一行代码设置字体 import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt mpl.rcParams['font.family'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False import matplotlib.pyplot as plt x=np.arange(1,31,1) plt.title('迭代30次正确率') plt.plot(x, train_acc, color='green', label='训练集') plt.plot(x, test_acc, color='red', label='测试集') plt.legend() # 显示图例 plt.show()
运行结果：
【Python实战】——神经网络识别手写数字（三）+https://developer.aliyun.com/article/1506502
