text: from keras.applications import resnet, vgg16
  from keras.models import Sequential
  from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout, Flatten
  from keras.models import load_model, Model
  
  class Actvity_classifier():
      def __init__(self, config):
          self.config = config
          self.build_model()
  
      def build_model(self):
          if self.config.model == 'ResNet50':
              model = resnet.ResNet50(include_top=False, weights='imagenet', input_tensor=None, input_shape=(self.config.resize[0], self.config.resize[1], 3), pooling='avg', classes=1000)
          elif self.config.model == 'VGG16':
              model = vgg16.VGG16(include_top=False, weights='imagenet', input_tensor=None, input_shape=(self.config.resize[0], self.config.resize[1], 3), pooling='avg', classes=1000)
          else:
              raise NotImplementedError("This Classifier mode is not yet implemented")
          x = model.output
          x = Dense(512, activation='relu')(x) #add new layer
          x = Dropout(0.5)(x) #add new layer
          x = Dense(512, activation='relu')(x) #add new layer
          x = Dropout(0.5)(x) #add new layer
          if self.config.single_label == True:
              out = Dense(self.config.num_classes, activation='softmax', name='output_layer')(x)
          else:
              out = Dense(self.config.num_classes, activation='sigmoid', name='output_layer')(x)
          self.model = Model(inputs=model.input,outputs= out)
  
  
          self.model.summary()