text: Title: Fast way to load multiple models from files?
  Body: I'm building a system for classifying images into multiple categories, and approaching the problem using multiple networks (all my networks have the same structure, and output `[0,1]` or `[1,0]` (Yes or No).
  
  There's (for the sake of the example) a network for dogs, another network for cats, the third network is for cars, and so and so...
  
  **My first question**: Is this kind of approach the popular one, or is there a different (and hopefully better) approach for classifying a LOT of different subjects?
  
  If I'm on the right path, I wondered if there a fast way to load the models from their files using TFLearn.
  
  Currently, I have a function to define my model structure:
  
      def define_network_model(category_id):
          """
          Defines a network structure
          """`
      
          # Defining image augmentation
          # We want to create a stilted wider dataset
          aug = ImageAugmentation()
          aug.add_random_flip_leftright()
          aug.add_random_blur(sigma_max=3.)
          aug.add_random_rotation(max_angle=20.)
          
          T.reset_default_graph()
      
          # Input layer
          net = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name="input", \
              data_augmentation=aug)
      
          # 2D Convolution layer
          net = conv_2d(net, 32, 5, activation='relu')
          net = max_pool_2d(net, 3)
      
          .... More layers ....
      
          # Output layer
          net = fully_connected(net, 2, activation='softmax')
      
          # Regression layer
          net = regression(net, optimizer='adam', learning_rate=LEARNING_RATE, \
              loss='categorical_crossentropy', name='targets')
          
          # Creating a model
          model = tflearn.DNN(net, tensorboard_dir="tensorboard", \
              best_checkpoint_path=os.path.join(CHECKPOINTS_DIR, category_id), \
              best_val_accuracy=0.7)
      
          return model
  
  And when I want to load the models, I have a categories object I'm iterating through and loading all the categories:
  
      models = {}
      
      for id, cat in categories.items():
          m = define_network_model(id) # Creating the structure
          m.load(os.path.join(MODELS_DIR, id)) # Loading the file
          models[id] = m # Appending to the models list for later evaluation
  
  
  Defining the network every iteration takes **a LOT** of time (>2sec for every iteration).
  
  **My second question**: Am I missing something here? Can the structure be cloned from a model to another in a faster way, or am I just on the wrong path?