title: Keras Embedding layer activation function?
body: <p>In the fully connected hidden layer of Keras embedding, what is the activation function leveraged? I'm either misunderstanding the concept of this class or unable to find documentation. I understand that it is encoding from word to real-valued vector of dimension <em>d</em> via answers like the below on stackoverflow:</p>
  
  <blockquote>
    <p>Embedding layers in Keras are trained just like any other layer in your network architecture: they are tuned to minimize the loss function by using the selected optimization method. The major difference with other layers, is that their output is not a mathematical function of the input. Instead the input to the layer is used to index a table with the embedding vectors [1]. However, the underlying automatic differentiation engine has no problem to optimize these vectors to minimize the loss function...</p>
  </blockquote>
  
  <p>In my network, I have a word embedding portion that is then linked to a larger network that is predicting a binary outcome (e.g., click yes/no). I understand that this Keras embedding is not operating like word2vec because here my embedding is being trained and updated against my end cross-entropy function. But, there is no mention of how the embedding fully-connected layer is activated. Thanks!</p>