CNN文本特征提取后，如何将最终提取的特征向量输出？

打算对问津以句为单位进行分类预测，词向量组成句子特征矩阵，利用testcnn对矩阵进行处理提取句子的特征向量，所以请问各位大佬，如何将cnn所提取的最终特征向量提取出来？

将softmax层的前一层输出就是了，

from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
import numpy as np

model = VGG16(weights='imagenet', include_top=False)

img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

features = model.predict(x)

以pretrained model为例子，加载weight以后去除作为classifier的那几层，用model.predict输出来的就是feature

from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
import numpy as np

model = VGG16(weights='imagenet', include_top=False)

img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

features = model.predict(x)