Python function for extracting image features using bottleneck layer of Keras' ResNet50

keras-resnet-extract-bottleneck-features.py

from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.preprocessing import image
import numpy as np


resnet = ResNet50(include_top=False)

def extract_features(img_paths, batch_size=64):
    """ This function extracts image features for each image in img_paths using ResNet50 bottleneck layer.
        Returned features is a numpy array with shape (len(img_paths), 2048).
    """
    global resnet
    n = len(img_paths)
    img_array = np.zeros((n, 224, 224, 3))
    
    for i, path in enumerate(img_paths):
        img = image.load_img(path, target_size=(224, 224))
        img = image.img_to_array(img)
        img = np.expand_dims(img, axis=0)
        x = preprocess_input(img)
        img_array[i] = x
    
    X = resnet.predict(img_array, batch_size=batch_size, verbose=1)
    X = X.reshape((n, 2048))
    return X

Hi! Where is the "bottleneck" layer defined? The code is very similar to https://keras.io/applications/#resnet50 which I don't think is the bottleneck layers. Thanks.