Machine Learning with TensorFlow 1.x

Loading a pre-trained model to speed up the training

In this section, let's focus on loading the pre-trained model in TensorFlow. We will use the VGG-16 model proposed by K. Simonyan and A. Zisserman from the University of Oxford.

VGG-16 is a very deep neural network with lots of convolution layers followed by max-pooling and fully connected layers. In the ImageNet challenge, the top-5 classification error of the VGG-16 model on the validation set of 1,000 image classes is 8.1% in a single-scale approach:

First, create a file named nets.py in the project directory. The following code defines the graph for the VGG-16 model:

 import tensorflow ...