title: Retrain model in Tensorflow
body: <p>I have a simple neural-network using Tensorflow.
  Here is the Session:</p>
  
  <pre><code>with tensorFlow.Session() as sess:
    sess.run(tensorFlow.global_variables_initializer())
    for epoch in range(epochs):
      i = 0
      epochLoss = 0
      for _ in range(int(len(data) / batchSize)):
        ex, ey = nextBatch(i)
        i += 1
        feedDict = {x :ex, y:ey }
        _, cos = sess.run([optimizer,cost], feed_dict= feedDict) 
        epochLoss += cos / (int(len(data)) / batchSize)
      print("Epoch", epoch + 1, "completed out of", epochs, "loss:", "{:.9f}".format(epochLoss))
  
    save_path = saver.save(sess, "model.ckpt")
    print("Model saved in file: %s" % save_path)
  </code></pre>
  
  <p>at the last 2 rows I saved the model and restore the graph in another class:</p>
  
  <pre><code>with new_graph.as_default():
      with tf.Session(graph=new_graph) as sess:
          sess.run(tf.global_variables_initializer())
          new_saver = tf.train.import_meta_graph('model.ckpt.meta')
          new_saver.restore(sess, tf.train.latest_checkpoint('./'))
  </code></pre>
  
  <p>I want to re-train the model, which means not initialize the weights, just to update them from the last point that it stopped.</p>
  
  <p>How can I do that? </p>