Deep Learning with TensorFlow

Restoring a model

In a second file, we create the following script to restore the deployed network.

Let's start by loading the required libraries:

import matplotlib.pyplot as plt import tensorflow as tf import input_data import numpy as np import mnist_data 

And then by adding the MNIST dataset using the following:

mnist = mnist_data.read_data_sets('data', one_hot=True) 

Implement an interactive session:

sess = tf.InteractiveSession() 

The following line imports the meta graph saved, which contains all the information we need on the topology of the model and its variables:

new_saver = tf.train.import_meta_graph('softmax_mnist.ckpt.meta') 

Then import the checkpoint file, which contains the weights we developed during training:

 new_saver.restore(sess, ...