Embedding layer initializer

[thomas.al...@gmail.com]

Whats the difference between initializing a Keras Embedding layer using "embeddings_initializer" versus setting "weights" directly as in:

# create embedding matrix (from pretrained embeddings for example)

embeding_matrix = ...

# create the embedding layer this way

embedded_sequences = Embedding(num_words,

                            EMBEDDING_DIM,

                      ----> embeddings_initializer=Constant(embedding_matrix),

                            input_length=MAX_SEQUENCE_LENGTH,

                            trainable=False,

                            )(sequence_input)

# or this way? 

embedded_sequences = Embedding(num_words,

                            EMBEDDING_DIM,

                      ----> weights=[embedding_matrix],

                            input_length=MAX_SEQUENCE_LENGTH,

                            trainable=False,

                            )(sequence_input)

Are they the same?  Is one better than the other?

[Sergey O.]

The initializer is intended to be used with one of the random initializers:

https://keras.io/initializers/

I suppose you could hack the initializer and give it a set of pre-trained weights...

Assuming it works, it shouldn't be any different than re-assigning the weights after random initialization.

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