X_test = X_test.astype('float32') / 255 # Convert class vectors to binary class matrices. Y_train = keras.utils.to_categorical(Y_train, NB_CLASSES) Y_test = keras.utils.to_categorical(Y_test, NB_CLASSES) '''人为削减''' X_train = X_train[0:1000] Y_train = Y_train[0:1000] X_test = X_test[0:200] Y_test = Y_test[0:200] # initiate RMSprop optimizer opt = keras.optimizers.rmsprop(lr=0.001, decay=1e-6) # train the model using RMSprop model = CNN.createCNN(INPUT_SHAPE, NB_CLASSES) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) modelfile = 'modelweight_10percent.model' #神经网络权重保存 file_path_history = 'historyfile.bin' #保存history,留着作图 if os.path.exists(modelfile): #如果存在之前训练的权重矩阵,载入模型 print('载入模型参数') model.load_weights(modelfile) else: print('训练') history = model.fit(X_train, Y_train, batch_size=BATCH_SIZE,