title: ANN training progress resets every new training session using FANN
body: <p>I have a standard neural network which I have trained for some time, but not until perfection. After the training session is complete, I save the network on disk.</p>
  
  <p>After some time I want to resume training the network from where it left. The problem is, it seems that every time I start a new training session, the weights and biases seem to be totally reset, which means I'm training the network from scratch all over again:</p>
  
  <p>Previous session:</p>
  
  <p><a href="https://i.stack.imgur.com/9q0rR.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/9q0rR.png" alt="enter image description here"></a></p>
  
  <p>New session:</p>
  
  <p><a href="https://i.stack.imgur.com/eVBJw.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/eVBJw.png" alt="enter image description here"></a></p>
  
  <p>Here is the excerpt from my training function:</p>
  
  <pre><code>void trainNet(fann *net) {
      const unsigned int
          max_epochs = 1000,
          epochs_between_reports = 10;
      const float desired_error = 0.01f;
      net -&gt; learning_momentum = 0.1f;
      fann_train_on_file(net, "sessions.data", max_epochs, epochs_between_reports, desired_error);
      fann_save(net, "network.net");
      fann_destroy(net);
  }
  </code></pre>
  
  <p>What am I missing? It seems so intuitive to me that you could train a network over a span of multiple sessions. Am I wrong? Is it a limitation of the library?</p>
  
  <p>The training data has remained constant between sessions. This isn't limited to this specific network, either -- networks of any format seem to invoke the same issue.</p>