title: Explain with example: how embedding layers in keras works
body: <p>I don't understand the Embedding layer of Keras. Although there are lots of articles explaining it, I am still confused. For example, the code below isfrom imdb sentiment analysis:</p>
  
  <pre><code>top_words = 5000
  max_review_length = 500
  embedding_vecor_length = 32    
  
  model = Sequential()
  model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
  model.add(LSTM(100))
  model.add(Dense(1, activation='sigmoid'))
  model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  print(model.summary())
  model.fit(X_train, y_train, nb_epoch=3, batch_size=64)
  </code></pre>
  
  <p>In this code, what exactly is the embedding layer doing? What would be the output of embedding layer? It would be nice if someone could explain it with some examples maybe!</p>