old_contents: from keras.layers import Conv2D
  from keras.models import Sequential
  
  
  def srcnn(input_shape, c=1, f1=9, f2=1, f3=5, n1=64, n2=32):
      model = Sequential()
      model.add(Conv2D(nb_filter=n1, nb_row=f1, nb_col=f1,
                       input_shape=input_shape))
      model.add(Conv2D(nb_filter=n2, nb_row=f2, nb_col=f2))
      model.add(Conv2D(nb_filter=c, nb_row=f3, nb_col=f3))
      return model
  
new_contents: from keras.layers import Conv2D
  from keras.models import Sequential
  
  
  def srcnn(input_shape, c=1, f1=9, f2=1, f3=5, n1=64, n2=32):
      model = Sequential()
      model.add(Conv2D(nb_filter=n1, nb_row=f1, nb_col=f1, activation='relu',
                       input_shape=input_shape))
      model.add(Conv2D(nb_filter=n2, nb_row=f2, nb_col=f2, activation='relu'))
      model.add(Conv2D(nb_filter=c, nb_row=f3, nb_col=f3))
      return model
  
subject: Add ReLU activations