Extracting bottleneck features with ResNet

The ResNet architecture was introduced in 2015 in the paper Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385). ResNet has a different network architecture than VGG. It consists of micro-architectures that are stacked on top of each other. ResNet won the ILSVRC competition in 2015 and surpassed human performance on the ImageNet dataset. In this recipe, we will demonstrate how to leverage ResNet50 weights to extract bottleneck features. 

How to do it...

1. We start by implementing all Keras tools:

from keras.models import Model
from keras.applications.resnet50 import ResNet50

from keras.applications.resnet50 import preprocess_input
from keras...