title: Validation loss curve is flat and training loss curve is higher than validation error curve
body: <p>I'm building a LSTM model for prediction senario. My dataset has around 248000 piece of data and I use 24000 (around 10%) as validation set, others are training set. My model learning curve is the following:
  <a href="https://i.stack.imgur.com/98F8B.png" rel="nofollow noreferrer">learning curve</a></p>
  <p>The validation error is always 0.00002 from scratch, and the training error decreased to 0.013533 at epoch 20.</p>
  <p>I've read this carefully: <a href="https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/" rel="nofollow noreferrer">https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/</a></p>
  <p>Is my validation set is unrepresentative? Is the solution to use larger validation set?</p>