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 as tf 
    import numpy as np 
 
 
    def inference(images): 
    with tf.name_scope("preprocess"): 
        mean = tf.constant([123.68, 116.779, 103.939],  
    dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean') 
        input_images = images - mean 
    conv1_1 = _conv2d(input_images, 3, 3, 64, 1, 1,   
    name="conv1_1") 
    conv1_2 = _conv2d...