keras—神经网络CNN—MNIST手写数字识别

 from keras.datasets import mnist
 from keras.utils import np_utils
 from plot_image_1 import plot_image_1
 from plot_prediction_1 import plot_image_labels_prediction_1
 from show_train_history import show_train_history
 import numpy as np
 import pandas as pd
 from keras.models import Sequential
 from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D
 np.random.seed()
 (x_Train,y_Train),(x_Test,y_Test)=mnist.load_data()
 print('train data=',len(x_Train))
 print('test data=',len(x_Test))
 print('x_train_image:',x_Train.shape)
 print('y_train_label:',y_Train.shape)
 x_Train4D=x_Train.reshape(x_Train.shape[],,,).astype('float32')
 x_Test4D=x_Test.reshape(x_Test.shape[],,,).astype('float32')
 x_Train4D_normalize=x_Train4D/
 x_Test4D_normalize=x_Test4D/
 y_TrainOneHot=np_utils.to_categorical(y_Train)
 y_TestOneHot=np_utils.to_categorical(y_Test)
 model=Sequential()
 model.add(Conv2D(filters=,
                  kernel_size=(,),
                  padding='same',
                  input_shape=(,,),
                  activation='relu'))
 model.add(MaxPooling2D(pool_size=(,)))
 model.add(Conv2D(filters=,
                  kernel_size=(,),
                  padding='same',
                  activation='relu'))
 model.add(MaxPooling2D(pool_size=(,)))
 model.add(Dropout(0.25))
 model.add(Flatten())
 model.add(Dense(,activation='relu'))
 model.add(Dropout(0.5))
 model.add(Dense(,activation='softmax'))
 print(model.summary())
 model.compile(loss='categorical_crossentropy',
               optimizer='adam',metrics=['accuracy'])
 train_history=model.fit(x=x_Train4D_normalize,
                         y=y_TrainOneHot,validation_split=0.2,
                         epochs=,batch_size=,verbose=)
 show_train_history(train_history,'acc','val_acc')
 show_train_history(train_history,'loss','val_loss')
 scores=model.evaluate(x_Test4D_normalize,y_TestOneHot)
 print()
 print('accuracy',scores[])
 prediction=model.predict_classes(x_Test4D_normalize)
 print("prediction[:10]",prediction[:])
 plot_image_labels_prediction_1(x_Test,y_Test,prediction,idx=)
 pd.crosstab(y_Test,prediction,rownames=['label'],colnames=['predict'])

 import matplotlib.pyplot as plt
 def plot_image_1(image):
     fig=plt.gcf()
     fig.set_size_inches(,)
     plt.imshow(image,cmap='binary')
     plt.show()

 import matplotlib.pyplot as plt
 def plot_image_labels_prediction_1(image,labels,prediction,idx,num=):
     fig=plt.gcf()
     fig.set_size_inches(,)
     if num>:num=
     for i in range(,num):
         ax=plt.subplot(,,i+)
         ax.imshow(image[idx],cmap='binary')
         title="label="+str(labels[idx])
         if len(prediction)>:
             title+=",predict="+str(prediction[idx])
         ax.set_title(title,fontsize=)
         ax.set_xticks([]);ax.set_yticks([])
         idx+=
     plt.show()

 import matplotlib.pyplot as plt
 def show_train_history(train_history,train,validation):
     plt.plot(train_history.history[train])
     plt.plot(train_history.history[validation])
     plt.title('Train History')
     plt.ylabel(train)
     plt.xlabel('Epoch')
     plt.legend(['train','validation'],loc='upper left')    #显示左上角标签
     plt.show()