一、bottleneck特徵說明

將所有影象穿過（VGG16去掉最後全連線層）得到輸出，作為新的輸入。

二、bottleneck特徵提取程式碼

1、載入預處理影象庫

from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import image
import numpy as np
import glob

img_paths = glob.glob("images-bottleneck/*.jpg")

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in img_paths]
    return np.vstack(list_of_tensors)

# calculate the image input. you will learn more about how this works the project!
img_input = preprocess_input(paths_to_tensor(img_paths))

print(img_input.shape)
 

2、匯入VGG16

from keras.applications.vgg16 import VGG16
model = VGG16()
model.summary()

model.predict(img_input).shape

3、匯入VGG16，但去掉最後3個全連線層

fromfrom  keras.applications.vgg16keras.a  import VGG16
model = VGG16(include_top=False)
model.summary()

4、最大池化層的輸出

print(model.predict(img_input).shape)