# 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

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]