1 #coding=utf-8
2 #1.数据预处理
3 import numpy as np #导入模块，numpy是扩展链接库
4 import pandas as pd
5 import tensorflow
6 import keras
7 from keras.utils import np_utils
8 np.random.seed(10) #设置seed可以产生的随机数据
9 from keras.datasets import mnist #导入模块，下载读取mnist数据
10 (x_train_image,y_train_label),\
11 (x_test_image,y_test_label)=mnist.load_data() #下载读取mnist数据
12 print('train data=',len(x_train_image))
13 print('test data=',len(x_test_image))
14 print('x_train_image:',x_train_image.shape)
15 print('y_train_label:',y_train_label.shape)
16 import matplotlib.pyplot as plt
17 def plot_image(image):
18 fig=plt.gcf()
19 fig.set_size_inches(2,2)
20 plt.imshow(image,cmap='binary')
21 plt.show()
22 y_train_label[0]
23 import matplotlib.pyplot as plt
24 def plot_image_labels_prediction(image,lables,prediction,idx,num=10):
25 fig=plt.gcf()
26 fig.set_size_inches(12,14)
27 if num>25:num=25
28 for i in range(0,num):
29 ax=plt.subplot(5,5,i+1)
30 ax.imshow(image[idx],cmap='binary')
31 title="lable="+str(lables[idx])
32 if len(prediction)>0:
33 title+=",predict="+str(prediction[idx])
34 ax.set_title(title,fontsize=10)
35 ax.set_xticks([]);ax.set_yticks([])
36 idx+=1
37 plt.show()
38 plot_image_labels_prediction(x_train_image,y_train_label,[],0,10)
39 plot_image_labels_prediction(x_test_image,y_test_label,[],0,10)
40 x_Train=x_train_image.reshape(60000,784).astype('float32') #以reshape转化成784个float
41 x_Test=x_test_image.reshape(10000,784).astype('float32')
42 x_Train_normalize=x_Train/255 #将features标准化
43 x_Test_normalize=x_Test/255
44 y_Train_OneHot=np_utils.to_categorical(y_train_label)#将训练数据和测试数据的label进行one-hot encoding转化
45 y_Test_OneHot=np_utils.to_categorical(y_test_label)
46 #2.建立模型
47 from keras.models import Sequential #可以通过Sequential模型传递一个layer的list来构造该模型,序惯模型是多个网络层的线性堆叠
48 from keras.layers import Dense #全连接层
49 from keras.layers import Dropout #避免过度拟合
50 model=Sequential()
51 #建立输入层、隐藏层
52 model.add(Dense(units=1000,
53 input_dim=784,
54 kernel_initializer='normal',
55 activation='relu'))
56 model.add(Dropout(0.5))
57 model.add(Dense(units=1000,
58 kernel_initializer='normal',
59 activation='relu'))
60 model.add(Dropout(0.5))
61 #建立输出层
62 model.add(Dense(units=10,
63 kernel_initializer='normal',
64 activation='softmax'))
65 print(model.summary()) #查看模型的摘要
66 #3、进行训练
67 #对训练模型进行设置，损失函数、优化器、权值
68 model.compile(loss='categorical_crossentropy',
69 optimizer='adam',metrics=['accuracy'])
70 # 设置训练与验证数据比例，80%训练，20%测试，执行10个训练周期，每一个周期200个数据，显示训练过程2次
71 train_history=model.fit(x=x_Train_normalize,
72 y=y_Train_OneHot,validation_split=0.2,
73 epochs=10,batch_size=200,verbose=2)
74 #显示训练过程
75 import matplotlib.pyplot as plt
76 def show_train_history(train_history,train,validation):
77 plt.plot(train_history.history[train])
78 plt.plot(train_history.history[validation])
79 plt.title('Train History')
80 plt.ylabel(train)
81 plt.xlabel('Epoch')
82 plt.legend(['train','validation'],loc='upper left') #显示左上角标签
83 plt.show()
84 show_train_history(train_history,'acc','val_acc') #画出准确率评估结果
85 show_train_history(train_history,'loss','val_loss') #画出误差执行结果
86 #以测试数据评估模型准确率
87 scores=model.evaluate(x_Test_normalize,y_Test_OneHot) #创建变量存储评估后的准确率数据，（特征值，真实值）
88 print()
89 print('accuracy',scores[1])
90 #进行预测
91 prediction=model.predict_classes(x_Test)
92 prediction
93 plot_image_labels_prediction(x_test_image,y_test_label,prediction,idx=340)
94 #4、建立模型提高预测准确率
95 #建立混淆矩阵
96 import pandas as pd #pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的
97 pd.crosstab(y_test_label,prediction,
98 rownames=['label'],colnames=['predict'])
99 #建立真实值与预测值dataFrame
100 df=pd.DataFrame({'label':y_test_label,'predict':prediction})
101 df[:2]
102 df[(df.label==5)&(df.predict==3)]
103 plot_image_labels_prediction(x_test_image,y_test_label,prediction,idx=340,num=1)