title: How to make sense of tensorflow tensorboard Histograms?
body: <p>I would like to know how to make sense of the tensor flow graphs/Histograms generated.
  The code for this can be found <a href="https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py" rel="noreferrer">here.</a>
  This graph is easy to understand Accuracy and loss are straight forward to understand.</p>
  <pre><code>Accuracy- Accuracy of current state of network for given train data.
  Higher is better
  
  Accuracy/Validation -  Accuracy of current state of network for given Validation data which is 
  not seen by network before. Higher is better
  
  Loss- Loss of network on train data. Lower is better.
  Loss/Valadation - Loss of network on test data. Lower is better.
  If loss increases it's a sign of over-fitting.
  Conv2d/L2-Loss - Loss of particular layer wrt train data. 
  </code></pre>
  <p><a href="https://i.stack.imgur.com/LVAWR.png" rel="noreferrer"><img src="https://i.stack.imgur.com/LVAWR.png" alt="enter image description here" /></a></p>
  <p>Basically what the graph signifies and how i could use it to understand my network and if possible what changes i can make to improve it.</p>
  <h1>How Do I interpret the histograms?</h1>
  <p><a href="https://i.stack.imgur.com/P5cMY.png" rel="noreferrer"><img src="https://i.stack.imgur.com/P5cMY.png" alt="enter image description here" /></a></p>