title: Tensorflow: Saving and restoring the model parameters
body: <p>I am a beginner in TensorFlow, currently training a CNN.</p>
  
  <p>I am using <strong><em>Saver</em></strong> in order to save the parameters used by the model, but I am having concerns whether this would itself store all the Variables used by the model, and is sufficient to restore the values to <strong><em>re-run</em></strong> the program for performing classification/testing on the trained network.</p>
  
  <p>Let us look at the famous example MNIST given by TensorFlow. </p>
  
  <p>In the example, we have bunch of Convolutional blocks, all of which have weight, and bias variables that gets initialised when the program is run.</p>
  
  <pre><code>W_conv1 = init_weight([5,5,1,32])
  b_conv1 = init_bias([32])
  </code></pre>
  
  <p>After having processed several layers, we create a session, and initialise all the variables added to the graph. </p>
  
  <pre><code>sess = tf.Session()
  sess.run(tf.initialize_all_variables())
  saver = tf.train.Saver()
  </code></pre>
  
  <p>Here, is it possible to comment the saver.save code, and replace it by saver.restore(sess,file_path) after the training, in order to restore the weight, bias, etc., parameters back to the graph? Is this how it should be ?</p>
  
  <pre><code>for i in range(1000):
   ...
  
    if i%500 == 0:
      saver.save(sess,"model%d.cpkt"%(i))
  </code></pre>
  
  <p>I am currently training on large dataset, so terminating, and restarting the training is a waste of time, and resources so I request someone to please clarify before the I start the training.</p>