old_contents: # Import the necessary packages
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.layers import Convolution2D, MaxPooling2D
  from keras.models import Sequential
  
  # A class for various CNN architectures
  class CNNArchitecture:
      def __init__(self):
          pass
  
      @staticmethod
      def select(architecture, *args, **kargs):
  
          # Map strings to functions
          nets = {
              "MiniVGGNet": CNNArchitecture.MiniVGGNet,
          }
  
          # Initialize architecture
          net = nets.get(architecture, None)
  
          # Check if a net has been requested
          if net is None:
              return None
  
          # If the net is named correctly, return the network architecture
          return net(*args, **kargs)
  
      @staticmethod
      def MiniVGGNet(imgrows, imgcols, numchannels, numclasses):
          # Initialize the model
          model = Sequential()
  
          # Define the first set of  CONV -> RELU -> CONV -> RELU -> POOL layers
          model.add(Convolution2D(32, 3, 3, input_shape=(imgrows, imgcols, numchannels), dim_ordering='tf'))
          model.add(Activation("relu"))
          model.add(Convolution2D(32, 3, 3))
          model.add(Activation("relu"))
          model.add(MaxPooling2D(pool_size=(3, 3)))
  
          # Add dropout
          model.add(Dropout(0.25))
  
          # Define the second set of CONV -> RELU -> CONV -> RELU -> POOL layers
          model.add(Convolution2D(64, 3, 3))
          model.add(Activation("relu"))
          model.add(Convolution2D(64, 3, 3))
          model.add(Activation("relu"))
          model.add(MaxPooling2D(pool_size=(2, 2)))
  
          # Add dropout
          model.add(Dropout(0.35))
  
          # Define FC -> RELU layers
          model.add(Flatten())
          model.add(Dense(512))
          model.add(Activation("relu"))
  
          # Add dropout
          model.add(Dropout(0.5))
  
          # Define the SoftMAX classifier
          model.add(Dense(numclasses))
          model.add(Activation("softmax"))
  
          # Return the network architecture
          return model
  
new_contents: # Import the necessary packages
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.layers import Convolution2D, MaxPooling2D
  from keras.models import Sequential
  
  # A class for various CNN architectures
  class CNNArchitecture:
      def __init__(self):
          pass
  
      @staticmethod
      def select(architecture, *args, **kargs):
  
          # Map strings to functions
          nets = {
              "MiniVGGNet": CNNArchitecture.MiniVGGNet
          }
  
          # Initialize architecture
          net = nets.get(architecture, None)
  
          # Check if a net has been requested
          if net is None:
              return None
  
          # If the net is named correctly, return the network architecture
          return net(*args, **kargs)
  
      @staticmethod
      def MiniVGGNet(imgrows, imgcols, numchannels, numclasses):
          # Initialize the model
          model = Sequential()
  
          # Define the first set of  CONV -> RELU -> CONV -> RELU -> POOL layers
          model.add(Convolution2D(32, 3, 3, input_shape=(imgrows, imgcols, numchannels), dim_ordering='tf', border_mode='same'))
          model.add(Activation("relu"))
          model.add(Convolution2D(32, 3, 3, border_mode='same'))
          model.add(Activation("relu"))
          model.add(MaxPooling2D(pool_size=(3, 3)))
  
          # Add dropout
          model.add(Dropout(0.25))
  
          # Define the second set of CONV -> RELU -> CONV -> RELU -> POOL layers
          model.add(Convolution2D(64, 3, 3, border_mode='same'))
          model.add(Activation("relu"))
          model.add(Convolution2D(64, 3, 3, border_mode='same'))
          model.add(Activation("relu"))
          model.add(MaxPooling2D(pool_size=(2, 2)))
  
          # Add dropout
          model.add(Dropout(0.35))
  
          # Define FC -> RELU layers
          model.add(Flatten())
          model.add(Dense(512))
          model.add(Activation("relu"))
  
          # Add dropout
          model.add(Dropout(0.5))
  
          # Define the SoftMAX classifier
          model.add(Dense(numclasses))
          model.add(Activation("softmax"))
  
          # Return the network architecture
          return model
subject: update nets