title: Training Tensorflow Inception-v3 Imagenet on modest hardware setup
body: <p>I've been training Inception V3 on a modest machine with a single GPU (GeForce GTX 980 Ti, 6GB). The maximum batch size appears to be around <code>40</code>.</p>
  
  <p>I've used the default learning rate settings specified in the <code>inception_train.py</code> file: <code>initial_learning_rate = 0.1</code>, <code>num_epochs_per_decay = 30</code> and <code>learning_rate_decay_factor = 0.16</code>. After a couple of weeks of training the best accuracy I was able to achieve is as follows (About 500K-1M iterations):</p>
  
  <pre><code>2016-06-06 12:07:52.245005: precision @ 1 = 0.5767 recall @ 5 = 0.8143 [50016 examples]
  2016-06-09 22:35:10.118852: precision @ 1 = 0.5957 recall @ 5 = 0.8294 [50016 examples]
  2016-06-14 15:30:59.532629: precision @ 1 = 0.6112 recall @ 5 = 0.8396 [50016 examples]
  2016-06-20 13:57:14.025797: precision @ 1 = 0.6136 recall @ 5 = 0.8423 [50016 examples]
  </code></pre>
  
  <p>I've tried fiddling with the settings towards the end of the training session, but couldn't see any improvements in accuracy. </p>
  
  <p>I've started a new training session from scratch with <code>num_epochs_per_decay</code> = 10 and <code>learning_rate_decay_factor</code> = 0.001 based on some other posts in this forum, but it's sort of grasping in the dark here. </p>
  
  <p>Any recommendations on good defaults for a small hardware setup like mine?</p>