Storing the network topology and trained weights

In most deep learning frameworks, it is straightforward to store the network architecture and the trained weights. However, because this can be extremely important, we will demonstrate how to store your model with TensorFlow in the following recipe.

How to do it... 

1. We start by importing the libraries:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

2. Next, we load the MNIST data:

mnist = input_data.read_data_sets('Data/mnist', one_hot=True)

3. We define the TensorFlow placeholders as follows:

n_classes = 10
input_size = 784

x = tf.placeholder(tf.float32, shape=[None, input_size])
y = tf.placeholder(tf.float32, shape=[None, n_classes...