1 from keras.datasets import cifar10

 from keras.utils import np_utils

 import matplotlib.pyplot as plt

 from keras.models import load_model

 import numpy as np

 np.random.seed()

 (x_img_train,y_label_train),(x_img_test,y_label_test)=cifar10.load_data()

 print('train data=',len(x_img_train))

 print('test data=',len(x_img_test))

 print('x_train_image:',x_img_train.shape)

 print('x_test_image:',x_img_test.shape)

 x_img_train_4D=x_img_train.reshape(x_img_train.shape[],,,).astype('float32')

 x_img_test_4D=x_img_test.reshape(x_img_test.shape[],,,).astype('float32')

 x_img_train_normalize=x_img_train_4D/255.0

 x_img_test_normalize=x_img_test_4D/255.0

 print(x_img_train_normalize[][][])

 y_label_train_OneHot=np_utils.to_categorical(y_label_train)

 y_label_test_OneHot=np_utils.to_categorical(y_label_test)

 print(y_label_train_OneHot[:])

 from keras.models import Sequential

 from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D,ZeroPadding2D,Activation

 model=Sequential()

 model.add(Conv2D(filters=,

                  kernel_size=(,),

                  padding='same',

                  input_shape=(,,),

                  activation='relu'))

 model.add(Dropout(0.25))

 model.add(MaxPooling2D(pool_size=(,)))

 model.add(Conv2D(filters=,

                  kernel_size=(,),

                  padding='same',

                  activation='relu'))

 model.add(Dropout(0.25))

 model.add(MaxPooling2D(pool_size=(,)))

 model.add(Flatten())

 model.add(Dropout(0.25))

 model.add(Dense(,activation='relu'))

 model.add(Dropout(0.25))

 model.add(Dense(,activation='softmax'))

 print(model.summary())

 model.compile(loss='categorical_crossentropy',

               optimizer='adam',metrics=['accuracy'])

 try:

     model=load_model("CnnModel.h5")

     print("Load model successfully!Continuous training model!......")

 except :

     print("Failure of loading model!Start training a new model......")

 train_history=model.fit(x=x_img_train_normalize,

                         y=y_label_train_OneHot,validation_split=0.2,

                         epochs=,batch_size=,verbose=)

 model.save("CnnModel.h5")

 print("Saved model to disk")

 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()

 show_train_history(train_history,'acc','val_acc')

 show_train_history(train_history,'loss','val_loss')

 scores=model.evaluate(x_img_test_normalize,y_label_test_OneHot)

 print()

 print('accuracy',scores[])

 prediction=model.predict_classes(x_img_test_normalize)

 print("prediction[:10]",prediction[:])

 import matplotlib.pyplot as plt

 label_dict={:"airplane",:"automobile",:"bird",:"cat",:"deer",:"dog",:"frog",:"horse",:"ship",:"truck"}

 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=str(i)+','+label_dict[labels[i][]]

         if len(prediction)>:

             title+="=>"+label_dict[prediction[i]]

         ax.set_title(title,fontsize=)

         ax.set_xticks([]);ax.set_yticks([])

         idx+=

     plt.show()

 plot_image_labels_prediction_1(x_img_test,y_label_test,prediction,,)

 Predicted_Probability=model.predict(x_img_test_normalize)

 def show_Predicted_Probability(y,prediction,x_img_test,Predicted_Probability,i):

     print('label:',label_dict[y[i][]],'predict:',label_dict[prediction[i]])

     plt.figure(figsize=(,))

     plt.imshow(np.reshape(x_img_test[i],(,,)))

     plt.show()

     for j in range():

         print(label_dict[j]+

               'Probability:%1.9f'%(Predicted_Probability[i][j]))

 show_Predicted_Probability(y_label_test,prediction,x_img_test,Predicted_Probability,)

 show_Predicted_Probability(y_label_test,prediction,x_img_test,Predicted_Probability,)

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