title: How to load numpy array to gensim Keyedvector format?
body: <p>After I trained word embeddings, I saved it as npz format.
  While I am trying to load it as KeyedVectors format, it makes errors.
  How can I load numpy array as gensim.KeyedVectors format?
  I really need it because I need to use functions like most_similar() not just vector values.</p>
  
  <p>in model.py with tensorflow,</p>
  
  <pre><code>self.verb_embeddings = tf.Variable(np.load(cfg.pretrained_target)["embeddings"],
                                                 name="verb_embeddings",
                                                 dtype=tf.float32,
                                                 trainable=cfg.tune_emb)
  </code></pre>
  
  <p>in saving.py</p>
  
  <pre><code>target_emb = sess.run(model.verb_embeddings)
  np.savez_compressed("trained_target_emb.npz", embeddings=target_emb)
  </code></pre>
  
  <p>in main.py</p>
  
  <pre><code> model = KeyedVectors.load('trained_target_emb.npz')
  </code></pre>
  
  <p>I got</p>
  
  <pre><code>_pickle.UnpicklingError: A load persistent id instruction was encountered, but no persistent_load function was specified.
  </code></pre>
  
  <p>also tried</p>
  
  <pre><code> model = KeyedVectors.load_word2vec_format('trained_target_emb.npz')
  </code></pre>
  
  <p>but got</p>
  
  <pre><code> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xde in position 14: invalid continuation byte
  </code></pre>