电子商务网站推广实训心得,哪有深圳网站页面设计,贵阳网站网站建设,网站建设公司比较好的有哪些#x1f349;CSDN小墨晓末:https://blog.csdn.net/jd1813346972 个人介绍: 研一#xff5c;统计学#xff5c;干货分享 擅长Python、Matlab、R等主流编程软件 累计十余项国家级比赛奖项#xff0c;参与研究经费10w、40w级横向 文… CSDN小墨晓末:https://blog.csdn.net/jd1813346972 个人介绍: 研一统计学干货分享 擅长Python、Matlab、R等主流编程软件 累计十余项国家级比赛奖项参与研究经费10w、40w级横向 文章目录 1 探索数据集1.1 读取并显示数据示例1.2 数据集大小1.3 自变量因变量构建1.4 One-hot编码1.5 图像数据示例1.6 pickle包保存python对象 2 构建神经网络并训练2.1 读取pickle文件2.2 神经网络核心关键函数定义2.3 神经网络模型定义2.4 模型训练2.4.1 预测概率2.4.2 训练集正确率2.4.3 测试集正确率2.4.4 训练集判别矩阵2.4.5 不同数字预测精确率 2.5 结果可视化2.5.1 每次epoch训练预测情况2.5.2 迭代30次正确率绘图 3 模型优化3.1 调整神经元数量3.1.1 每次epoch训练预测情况3.1.2 正确率绘图 3.2 更换隐藏层层数3.2.1 每次epoch训练预测情况3.2.2 正确率绘图 该篇文章以Python实战的形式利用神经网络识别mnist手写数字数据集包括pickle操作神经网络关键模型关键函数定义识别效果评估及可视化等内容建议收藏练手 1 探索数据集
1.1 读取并显示数据示例 运行程序
import numpy as np
import matplotlib.pyplot as pltimage_size 28 # width and length
num_of_different_labels 10 # i.e. 0, 1, 2, 3, ..., 9
image_pixels image_size * image_sizetrain_data np.loadtxt(D:\\mnist_train.csv, delimiter,)
test_data np.loadtxt(D:\\mnist_test.csv, delimiter,)
test_data[:10]#测试集前十行运行结果
array([[7., 0., 0., ..., 0., 0., 0.],[2., 0., 0., ..., 0., 0., 0.],[1., 0., 0., ..., 0., 0., 0.],...,[9., 0., 0., ..., 0., 0., 0.],[5., 0., 0., ..., 0., 0., 0.],[9., 0., 0., ..., 0., 0., 0.]])1.2 数据集大小 运行程序
print(test_data.shape)
print(train_data.shape)运行结果
(10000, 785)
(60000, 785)该mnist数据集训练集共10000个数据有785维测试集有60000个数据785维。
1.3 自变量因变量构建 运行程序
##第一列为预测类别
train_imgs np.asfarray(train_data[:, 1:]) / 255
test_imgs np.asfarray(test_data[:, 1:]) / 255
train_labels np.asfarray(train_data[:, :1])
test_labels np.asfarray(test_data[:, :1])1.4 One-hot编码 运行程序
import numpy as nplable_range np.arange(10)for label in range(10):one_hot (lable_rangelabel).astype(int)print(label: , label, in one-hot representation: , one_hot)# 将数据集的标签转换为one-hot labellabel_range np.arange(num_of_different_labels)train_labels_one_hot (label_rangetrain_labels).astype(float)
test_labels_one_hot (label_rangetest_labels).astype(float)1.5 图像数据示例 运行程序
# 示例
for i in range(10):img train_imgs[i].reshape((28,28))plt.imshow(img, cmapGreys)plt.show()运行结果 1.6 pickle包保存python对象 因为csv文件读取到内存比较慢我们用pickle这个包来保存python对象这里面python对象指的是numpy array格式的train_imgs, test_imgs, train_labels, test_labels 运行程序:
import picklewith open(D:\\pickled_mnist.pkl, bw) as fh:data (train_imgs, test_imgs, train_labels,test_labels)pickle.dump(data, fh)2 构建神经网络并训练
2.1 读取pickle文件 运行程序
import picklewith open(D:\\19实验\\实验课大作业\\pickled_mnist.pkl, br) as fh:data pickle.load(fh)train_imgs data[0]
test_imgs data[1]
train_labels data[2]
test_labels data[3]train_labels_one_hot (lable_rangetrain_labels).astype(float)
test_labels_one_hot (label_rangetest_labels).astype(float)image_size 28 # width and length
num_of_different_labels 10 # i.e. 0, 1, 2, 3, ..., 9
image_pixels image_size * image_size2.2 神经网络核心关键函数定义 运行程序
import numpy as npdef sigmoid(x):return 1 / (1 np.e ** -x)
##激活函数
activation_function sigmoidfrom scipy.stats import truncnorm
##数据标准化
def truncated_normal(mean0, sd1, low0, upp10):return truncnorm((low - mean) / sd, (upp - mean) / sd, locmean, scalesd)
##构建神经网络模型
class NeuralNetwork:def __init__(self, num_of_in_nodes, #输入节点数num_of_out_nodes, #输出节点数num_of_hidden_nodes,#隐藏节点数learning_rate):#学习率self.num_of_in_nodes num_of_in_nodesself.num_of_out_nodes num_of_out_nodesself.num_of_hidden_nodes num_of_hidden_nodesself.learning_rate learning_rate self.create_weight_matrices()#初始为一个隐藏节点 def create_weight_matrices(self):#创建权重矩阵# A method to initialize the weight #matrices of the neural network#一种初始化神经网络权重矩阵的方法rad 1 / np.sqrt(self.num_of_in_nodes) X truncated_normal(mean0, sd1, low-rad, upprad) #形成指定分布self.weight_1 X.rvs((self.num_of_hidden_nodes, self.num_of_in_nodes)) #rvs:产生服从指定分布的随机数rad 1 / np.sqrt(self.num_of_hidden_nodes)X truncated_normal(mean0, sd1, low-rad, upprad)self.weight_2 X.rvs((self.num_of_out_nodes, self.num_of_hidden_nodes)) #rvs: 产生服从指定分布的随机数def train(self, input_vector, target_vector):## input_vector and target_vector can #be tuple, list or ndarray#input_vector np.array(input_vector, ndmin2).T#输入target_vector np.array(target_vector, ndmin2).T#输出output_vector1 np.dot(self.weight_1, input_vector) #隐藏层值output_hidden activation_function(output_vector1)#删除不激活output_vector2 np.dot(self.weight_2, output_hidden)#输出output_network activation_function(output_vector2)##删除不激活# calculate output errors:计算输出误差output_errors target_vector - output_network# update the weights:更新权重tmp output_errors * output_network * (1.0 - output_network) self.weight_2 self.learning_rate * np.dot(tmp, output_hidden.T)# calculate hidden errors:计算隐藏层误差hidden_errors np.dot(self.weight_2.T, output_errors)# update the weights:tmp hidden_errors * output_hidden * (1.0 - output_hidden)self.weight_1 self.learning_rate * np.dot(tmp, input_vector.T)#测试集def run(self, input_vector):# input_vector can be tuple, list or ndarrayinput_vector np.array(input_vector, ndmin2).Toutput_vector np.dot(self.weight_1, input_vector)output_vector activation_function(output_vector)output_vector np.dot(self.weight_2, output_vector)output_vector activation_function(output_vector)return output_vector#判别矩阵def confusion_matrix(self, data_array, labels):cm np.zeros((10, 10), int)for i in range(len(data_array)):res self.run(data_array[i])res_max res.argmax()target labels[i][0]cm[res_max, int(target)] 1return cm #精确度def precision(self, label, confusion_matrix):col confusion_matrix[:, label]return confusion_matrix[label, label] / col.sum()#评估def evaluate(self, data, labels):corrects, wrongs 0, 0for i in range(len(data)):res self.run(data[i])res_max res.argmax()if res_max labels[i]:corrects 1else:wrongs 1return corrects, wrongs2.3 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 0.99785848507552272.4.2 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: 0.94253333333333332.4.3 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 0.94122.4.4 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: 0.96570852244074632.5 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 0.97172.5.2 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 xnp.arange(1,31,1)plt.title(迭代30次正确率)
plt.plot(x, train_acc, colorgreen, label训练集)
plt.plot(x, test_acc, colorred, label测试集)plt.legend() # 显示图例
plt.show()运行结果 3 模型优化
3.1 调整神经元数量
3.1.1 每次epoch训练预测情况 运行程序
##更换隐藏神经元数量为50
epochs 50
train_acc[]
test_acc[]
NN NeuralNetwork(num_of_in_nodes image_pixels, num_of_out_nodes 10, num_of_hidden_nodes 50,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: 0.93605
accuracy: test 0.935
epoch: 1
accuracy train: 0.95185
accuracy: test 0.9501
epoch: 2
accuracy train: 0.9570333333333333
accuracy: test 0.9526
epoch: 3
accuracy train: 0.9630833333333333
accuracy: test 0.9556
epoch: 4
accuracy train: 0.9640166666666666
accuracy: test 0.9556
epoch: 5
accuracy train: 0.9668333333333333
accuracy: test 0.957
epoch: 6
accuracy train: 0.96765
accuracy: test 0.957
epoch: 7
accuracy train: 0.9673166666666667
accuracy: test 0.9566
epoch: 8
accuracy train: 0.96875
accuracy: test 0.9559
epoch: 9
accuracy train: 0.97145
accuracy: test 0.957
epoch: 10
accuracy train: 0.974
accuracy: test 0.9579
epoch: 11
accuracy train: 0.9730666666666666
accuracy: test 0.9569
epoch: 12
accuracy train: 0.9730166666666666
accuracy: test 0.9581
epoch: 13
accuracy train: 0.9747666666666667
accuracy: test 0.959
epoch: 14
accuracy train: 0.9742166666666666
accuracy: test 0.9581
epoch: 15
accuracy train: 0.97615
accuracy: test 0.9596
epoch: 16
accuracy train: 0.9759
accuracy: test 0.9586
epoch: 17
accuracy train: 0.9773166666666666
accuracy: test 0.9596
epoch: 18
accuracy train: 0.9778833333333333
accuracy: test 0.9606
epoch: 19
accuracy train: 0.9789166666666667
accuracy: test 0.9589
epoch: 20
accuracy train: 0.9777333333333333
accuracy: test 0.9582
epoch: 21
accuracy train: 0.9774
accuracy: test 0.9573
epoch: 22
accuracy train: 0.9796166666666667
accuracy: test 0.9595
epoch: 23
accuracy train: 0.9792666666666666
accuracy: test 0.959
epoch: 24
accuracy train: 0.9804333333333334
accuracy: test 0.9591
epoch: 25
accuracy train: 0.9806
accuracy: test 0.9589
epoch: 26
accuracy train: 0.98105
accuracy: test 0.9596
epoch: 27
accuracy train: 0.9806833333333334
accuracy: test 0.9587
epoch: 28
accuracy train: 0.9809833333333333
accuracy: test 0.9595
epoch: 29
accuracy train: 0.9813333333333333
accuracy: test 0.95953.1.2 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 # 步骤二解决坐标轴负数的负号显示问题 import matplotlib.pyplot as plt xnp.arange(1,31,1)plt.title(神经元数量为50时正确率)
plt.plot(x, train_acc, colorgreen, label训练集)
plt.plot(x, test_acc, colorred, label测试集)plt.legend() # 显示图例
plt.show()运行结果 3.2 更换隐藏层层数
3.2.1 每次epoch训练预测情况 运行程序
#隐藏层层数为2
class NeuralNetwork:def __init__(self, num_of_in_nodes, #输入节点数num_of_out_nodes, #输出节点数num_of_hidden_nodes1,#隐藏第一层节点数num_of_hidden_nodes2,#隐藏第二层节点数learning_rate):#学习率self.num_of_in_nodes num_of_in_nodesself.num_of_out_nodes num_of_out_nodesself.num_of_hidden_nodes1 num_of_hidden_nodes1self.num_of_hidden_nodes2 num_of_hidden_nodes2self.learning_rate learning_rate self.create_weight_matrices()#初始为一个隐藏节点 def create_weight_matrices(self):#创建权重矩阵#A method to initialize the weight #matrices of the neural network#一种初始化神经网络权重矩阵的方法rad 1 / np.sqrt(self.num_of_in_nodes) X truncated_normal(mean0, sd1, low-rad, upprad) #形成指定分布self.weight_1 X.rvs((self.num_of_hidden_nodes1, self.num_of_in_nodes)) #rvs:产生服从指定分布的随机数rad 1 / np.sqrt(self.num_of_hidden_nodes1)X truncated_normal(mean0, sd1, low-rad, upprad)self.weight_2 X.rvs((self.num_of_hidden_nodes2, self.num_of_hidden_nodes1)) #rvs: 产生服从指定分布的随机数rad 1 / np.sqrt(self.num_of_hidden_nodes2)X truncated_normal(mean0, sd1, low-rad, upprad)self.weight_3 X.rvs((self.num_of_out_nodes, self.num_of_hidden_nodes2)) #rvs: 产生服从指定分布的随机数def train(self, input_vector, target_vector):#input_vector and target_vector can #be tuple, list or ndarrayinput_vector np.array(input_vector, ndmin2).T#输入target_vector np.array(target_vector, ndmin2).T#输出output_vector1 np.dot(self.weight_1, input_vector) #隐藏层值output_hidden1 activation_function(output_vector1)#删除不激活output_vector2 np.dot(self.weight_2, output_hidden1)#输出output_hidden2 activation_function(output_vector2)#删除不激活output_vector3 np.dot(self.weight_3, output_hidden2)#输出output_network activation_function(output_vector3)##删除不激活# calculate output errors:计算输出误差output_errors target_vector - output_network# update the weights:更新权重tmp output_errors * output_network * (1.0 - output_network) self.weight_3 self.learning_rate * np.dot(tmp, output_hidden2.T)hidden1_errors np.dot(self.weight_3.T, output_errors)tmp hidden1_errors * output_hidden2 * (1.0 - output_hidden2) self.weight_2 self.learning_rate * np.dot(tmp, output_hidden1.T)# calculate hidden errors:计算隐藏层误差hidden_errors np.dot(self.weight_2.T, hidden1_errors)# update the weights:tmp hidden_errors * output_hidden1 * (1.0 - output_hidden1)self.weight_1 self.learning_rate * np.dot(tmp, input_vector.T)#测试集def run(self, input_vector):# input_vector can be tuple, list or ndarrayinput_vector np.array(input_vector, ndmin2).Toutput_vector np.dot(self.weight_1, input_vector)output_vector activation_function(output_vector)output_vector np.dot(self.weight_2, output_vector)output_vector activation_function(output_vector)output_vector np.dot(self.weight_3, output_vector)output_vector activation_function(output_vector)return output_vector#判别矩阵def confusion_matrix(self, data_array, labels):cm np.zeros((10, 10), int)for i in range(len(data_array)):res self.run(data_array[i])res_max res.argmax()target labels[i][0]cm[res_max, int(target)] 1return cm #精确度def precision(self, label, confusion_matrix):col confusion_matrix[:, label]return confusion_matrix[label, label] / col.sum()#评估def evaluate(self, data, labels):corrects, wrongs 0, 0for i in range(len(data)):res self.run(data[i])res_max res.argmax()if res_max labels[i]:corrects 1else:wrongs 1return corrects, wrongs##迭代30次
epochs 30
train_acc[]
test_acc[]
NN NeuralNetwork(num_of_in_nodes image_pixels, num_of_out_nodes 10, num_of_hidden_nodes1 100,num_of_hidden_nodes2 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: 0.8972333333333333
accuracy: test 0.9005
epoch: 1
accuracy train: 0.8891833333333333
accuracy: test 0.8936
epoch: 2
accuracy train: 0.9146833333333333
accuracy: test 0.9182
epoch: 3
D:\ananconda\lib\site-packages\ipykernel_launcher.py:5: RuntimeWarning: overflow encountered in power
accuracy train: 0.8974833333333333
accuracy: test 0.894
epoch: 4
accuracy train: 0.8924166666666666
accuracy: test 0.8974
epoch: 5
accuracy train: 0.91295
accuracy: test 0.914
epoch: 6
accuracy train: 0.9191166666666667
accuracy: test 0.9205
epoch: 7
accuracy train: 0.9117666666666666
accuracy: test 0.9162
epoch: 8
accuracy train: 0.9220333333333334
accuracy: test 0.9222
epoch: 9
accuracy train: 0.9113833333333333
accuracy: test 0.9112
epoch: 10
accuracy train: 0.9134333333333333
accuracy: test 0.911
epoch: 11
accuracy train: 0.9112166666666667
accuracy: test 0.9103
epoch: 12
accuracy train: 0.914
accuracy: test 0.9126
epoch: 13
accuracy train: 0.9206833333333333
accuracy: test 0.9214
epoch: 14
accuracy train: 0.90945
accuracy: test 0.9073
epoch: 15
accuracy train: 0.9225166666666667
accuracy: test 0.9287
epoch: 16
accuracy train: 0.9226
accuracy: test 0.9205
epoch: 17
accuracy train: 0.9239833333333334
accuracy: test 0.9202
epoch: 18
accuracy train: 0.91925
accuracy: test 0.9191
epoch: 19
accuracy train: 0.9223166666666667
accuracy: test 0.92
epoch: 20
accuracy train: 0.9113
accuracy: test 0.9084
epoch: 21
accuracy train: 0.9241666666666667
accuracy: test 0.925
epoch: 22
accuracy train: 0.9236333333333333
accuracy: test 0.9239
epoch: 23
accuracy train: 0.9301166666666667
accuracy: test 0.9259
epoch: 24
accuracy train: 0.9195166666666666
accuracy: test 0.9186
epoch: 25
accuracy train: 0.9200833333333334
accuracy: test 0.9144
epoch: 26
accuracy train: 0.9204833333333333
accuracy: test 0.9186
epoch: 27
accuracy train: 0.9288666666666666
accuracy: test 0.9259
epoch: 28
accuracy train: 0.9293
accuracy: test 0.9282
epoch: 29
accuracy train: 0.9254666666666667
accuracy: test 0.92423.2.2 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 # 步骤二解决坐标轴负数的负号显示问题 import matplotlib.pyplot as plt xnp.arange(1,31,1)plt.title(隐藏层数为2时正确率)
plt.plot(x, train_acc, colorgreen, label训练集)
plt.plot(x, test_acc, colorred, label测试集)plt.legend() # 显示图例
plt.show()运行结果
