title: Train some embeddings, keep others fixed
body: <p>I do sequence classification with Keras, using an RNN and embeddings. My sequences are a bit weird. I have words mixed with special symbols. Words are associated with fixed, pre-trained embeddings, but the special symbol embeddings have to be modified during training.</p>
  
  <p>In an Embedding layer during learning, how can I keep some embeddings fixed while updating others? Is there a way to mask those indices which shouldn't be modified? Or is this a case for a custom Embedding layer?</p>