text: #coding=utf-8
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.layers import Conv2D, MaxPool2D
  from keras.optimizers import SGD
  from keras import backend as K
  
  K.set_image_dim_ordering('tf')
  
  
  import cv2
  import numpy as np
  
  
  plateType  = ["蓝牌","单层黄牌","新能源车牌","白色","黑色-港澳"]
  def Getmodel_tensorflow(nb_classes):
      # nb_classes = len(charset)
  
      img_rows, img_cols = 9, 34
      # number of convolutional filters to use
      nb_filters = 32
      # size of pooling area for max pooling
      nb_pool = 2
      # convolution kernel size
      nb_conv = 3
  
      # x = np.load('x.npy')
      # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
      # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
      # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
  
      model = Sequential()
      model.add(Conv2D(16, (5, 5),input_shape=(img_rows, img_cols,3)))
      model.add(Activation('relu'))
      model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
      model.add(Flatten())
      model.add(Dense(64))
      model.add(Activation('relu'))
      model.add(Dropout(0.5))
      model.add(Dense(nb_classes))
      model.add(Activation('softmax'))
      model.compile(loss='categorical_crossentropy',
                    optimizer='adam',
                    metrics=['accuracy'])
      return model
  
  model = Getmodel_tensorflow(5)
  model.load_weights("./model/plate_type.h5")
  model.save("./model/plate_type.h5")
  
  def SimplePredict(image):
      image = cv2.resize(image, (34, 9))
      image = image.astype(np.float) / 255
      res = np.array(model.predict(np.array([image]))[0])
      return res.argmax()
  
  
  