About embedding_weights

First, thank you for code sharing.

In w2v.py, I saw your code as follows:

 embedding_weights = [np.array([embedding_model[w] if w in embedding_model
                  else np.random.uniform(-0.25, 0.25, embedding_model.vector_size)
                  for w in vocabulary_inv])]

For obtaining weights from embedding_model, parameter w must be a word, e.g. “happy”. But, in w2v.py, "for w in embedding_model ", w is an index of word

Is that a mistake here? The code “else np.random.uniform(-0.25, 0.25, embedding_model.vector_size)” seems been executed in every loop.

[alexander-rakhlin]

Looks okay. embedding_weights must be a list of len=1 of ndarray with shape=(len(vocabulary_inv), num_features). It was made a list for compatibility with keras layer.set_weights()

[alexander-rakhlin]

Please see updated version