# load the model
       
      
     

      
       
      
      
       
        print(
        "[INFO] loading network...")
       
      
     

      
       
      
      
       
        model = load_model(
        "fashion.model"
 
)
       
      
     

      
       
      
      
        
       
      
     

      
       
      
      
       
        # load the image
       
      
     

      
       
      
      
       
        image_path = 
        "10026.jpg"
       
      
     

      
       
      
      
       
        image = cv2.imread(image_path)
       
      
 

     

      
       
      
      
       
        # pre-process the image for classification
       
      
     

      
       
      
      
       
        image = cv2.resize(image, (
        96, 
        96))
       
      
     

      
       
      
      
       
        image = image.astype(
        "float"
 
) / 
        255.0
       
      
     

      
       
      
      
       
        image = img_to_array(image)
       
      
     

      
       
      
      
       
        image = np.expand_dims(image, axis=
        0)
       
      
     

      
       
      
      
       
        print(image, type(image))
       
      
     

      
       
      
      
       
        # extract the layer feature
       
      
     

      
       
      
      
       
        get_3rd_layer_output = K.function([model.layers[
        0].input],[model.layers[
        3].output])
       
      
     

      
       
      
      
       
        feature = get_3rd_layer_output(image)[
        0]
       
      
     

      
       
      
      
       
        # prob = model.predict(image)[0]
       
      
    
    

報錯：TypeError: `inputs` to a TensorFlow backend function should be a list or tuple

原因在於，在使用get_3rd_layer時沒有用[ ]將image框起來，變成一個list。

將該句

feature = get_3rd_layer_output(image)[0]
  

修改為：

feature = get_3rd_layer_output([image])[0]
  

問題解決

參考：https://stackoverflow.com/questions/42045092/does-k-function-method-of-keras-with-tensorflow-backend-work-with-network-layers

一種簡單的方法是建立一個新的Model，使得它的輸出是你想要的那個輸出

  

   

    
     
    
    
     
      from keras.models 
      import Model
     
    
   

    
     
    
    
      
     
    
   

    
     
    
    
     
      model = ...  
      # create the original model
     
    
   

    
     
    
    
      
     
    
   

    
     
    
    
     
      layer_name = 
      'my_layer'
     
    
   

    
     
    
    
     
      intermediate_layer_model = Model(input=model.input,
     
    
   

    
     
    
    
     
                                       output=model.get_layer(layer_name).output)
     
    
   

    
     
    
    
     
      intermediate_output = intermediate_layer_model.predict(data
     
    
  
  

此外，我們也可以建立一個Keras的函式來達到這一目的：

  

   

    
     
    
    
     
      from keras 
      import backend 
      as K
     
    
   

    
     
    
    
      
     
    
   

    
     
    
    
     
      # with a Sequential model
     
    
   

    
     
    
    
     
      get_3rd_layer_output = K.function([model.layers[
      0].input],
     
    
   

    
     
    
    
     
                                        [model.layers[
      3].output])
     
    
   

    
     
    
    
     
      layer_output = get_3rd_layer_output([X])[
      0]
     
    
  
  

  

   

    
     
    
    
     
      # load the model
     
    
   

    
     
    
    
     
      print(
      "[INFO] loading network...")
     
    
   

    
     
    
    
     
      model = load_model(
      "fashion.model")
     
    
   

    
     
    
    
      
     
    
   

    
     
    
    
     
      # load the image
     
    
   

    
     
    
    
     
      image_path = 
      "10026.jpg"
     
    
   

    
     
    
    
     
      image = cv2.imread(image_path)
     
    
   

    
     
    
    
     
      # pre-process the image for classification
     
    
   

    
     
    
    
     
      image = cv2.resize(image, (
      96, 
      96))
     
    
   

    
     
    
    
     
      image = image.astype(
      "float") / 
      255.0
     
    
   

    
     
    
    
     
      image = img_to_array(image)
     
    
   

    
     
    
    
     
      image = np.expand_dims(image, axis=
      0)
     
    
   

    
     
    
    
     
      print(image, type(image))
     
    
   

    
     
    
    
     
      # extract the layer feature
     
    
   

    
     
    
    
     
      get_3rd_layer_output = K.function([model.layers[
      0].input],[model.layers[
      3].output])
     
    
   

    
     
    
    
     
      feature = get_3rd_layer_output(image)[
      0]
     
    
   

    
     
    
    
     
      # prob = model.predict(image)[0]