Python/Gensim - What is the meaning of syn0 and syn0norm?
I know that in gensims KeyedVectors-model, one can access the embedding matrix by the attribute model.syn0. There is also a syn0norm, which doesn't seem to work for the glove model I recently loaded. I think I also have seen syn1 somewhere previously.
I haven't found a doc-string for this and I'm just wondering what's the logic behind this?
So if syn0 is the embedding matrix, what is syn0norm? What would then syn1 be and generally, what does syn stand for?
python deep-learning nlp gensim word-embedding
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
add a comment |
I know that in gensims KeyedVectors-model, one can access the embedding matrix by the attribute model.syn0. There is also a syn0norm, which doesn't seem to work for the glove model I recently loaded. I think I also have seen syn1 somewhere previously.
I haven't found a doc-string for this and I'm just wondering what's the logic behind this?
So if syn0 is the embedding matrix, what is syn0norm? What would then syn1 be and generally, what does syn stand for?
python deep-learning nlp gensim word-embedding
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
add a comment |
I know that in gensims KeyedVectors-model, one can access the embedding matrix by the attribute model.syn0. There is also a syn0norm, which doesn't seem to work for the glove model I recently loaded. I think I also have seen syn1 somewhere previously.
I haven't found a doc-string for this and I'm just wondering what's the logic behind this?
So if syn0 is the embedding matrix, what is syn0norm? What would then syn1 be and generally, what does syn stand for?
python deep-learning nlp gensim word-embedding
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
I know that in gensims KeyedVectors-model, one can access the embedding matrix by the attribute model.syn0. There is also a syn0norm, which doesn't seem to work for the glove model I recently loaded. I think I also have seen syn1 somewhere previously.
I haven't found a doc-string for this and I'm just wondering what's the logic behind this?
So if syn0 is the embedding matrix, what is syn0norm? What would then syn1 be and generally, what does syn stand for?
python deep-learning nlp gensim word-embedding
python deep-learning nlp gensim word-embedding
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
edited Nov 14 '18 at 19:21
blue-phoenox
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
asked Nov 14 '18 at 13:56
blue-phoenoxblue-phoenox
4,181101643
4,181101643
add a comment |
add a comment |
1 Answer
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These names were inherited from the original Google word2vec.c implementation, upon which the gensim Word2Vec class was based. (I believe syn0 only exists in recent versions for backward-compatbility.)
The syn0 array essentially holds raw word-vectors. From the perspective of the neural-network used to train word-vectors, these vectors are a 'projection layer' that can convert a one-hot encoding of a word into a dense embedding-vector of the right dimensionality.
Similarity operations tend to be done on the unit-normalized versions of the word-vectors. That is, vectors that have all been scaled to have a magnitude of 1.0. (This makes the cosine-similarity calculation easier.) The syn0norm array is filled with these unit-normalized vectors, the first time they're needed.
This syn0norm will be empty until either you do an operation (like most_similar()) that requires it, or you explicitly do an init_sims() call. If you explicitly do an init_sims(replace=True) call, you'll actually clobber the raw vectors, in-place, with the unit-normed vectors. This saves the memory that storing both vectors for every word would otherwise require. (However, some word-vector uses may still be interested in the original raw vectors of varying magnitudes, so only do this when you're sure most_similar() cosine-similarity operations are all you'll need.)
The syn1 (or syn1neg in the more common case of negative-sampling training) properties, when they exist on a full model (and not for a plain KeyedVectors object of only word-vectors), are the model neural network's internal 'hidden' weights leading to the output nodes. They're needed during model training, but not a part of the typical word-vectors collected after training.
I believe the syn prefix is just a convention from neural-network variable-naming, likely derived from 'synapse'.
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
Great explanation, thank you!
– blue-phoenox
Nov 16 '18 at 7:21
add a comment |
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These names were inherited from the original Google word2vec.c implementation, upon which the gensim Word2Vec class was based. (I believe syn0 only exists in recent versions for backward-compatbility.)
The syn0 array essentially holds raw word-vectors. From the perspective of the neural-network used to train word-vectors, these vectors are a 'projection layer' that can convert a one-hot encoding of a word into a dense embedding-vector of the right dimensionality.
Similarity operations tend to be done on the unit-normalized versions of the word-vectors. That is, vectors that have all been scaled to have a magnitude of 1.0. (This makes the cosine-similarity calculation easier.) The syn0norm array is filled with these unit-normalized vectors, the first time they're needed.
This syn0norm will be empty until either you do an operation (like most_similar()) that requires it, or you explicitly do an init_sims() call. If you explicitly do an init_sims(replace=True) call, you'll actually clobber the raw vectors, in-place, with the unit-normed vectors. This saves the memory that storing both vectors for every word would otherwise require. (However, some word-vector uses may still be interested in the original raw vectors of varying magnitudes, so only do this when you're sure most_similar() cosine-similarity operations are all you'll need.)
The syn1 (or syn1neg in the more common case of negative-sampling training) properties, when they exist on a full model (and not for a plain KeyedVectors object of only word-vectors), are the model neural network's internal 'hidden' weights leading to the output nodes. They're needed during model training, but not a part of the typical word-vectors collected after training.
I believe the syn prefix is just a convention from neural-network variable-naming, likely derived from 'synapse'.
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
Great explanation, thank you!
– blue-phoenox
Nov 16 '18 at 7:21
add a comment |
These names were inherited from the original Google word2vec.c implementation, upon which the gensim Word2Vec class was based. (I believe syn0 only exists in recent versions for backward-compatbility.)
The syn0 array essentially holds raw word-vectors. From the perspective of the neural-network used to train word-vectors, these vectors are a 'projection layer' that can convert a one-hot encoding of a word into a dense embedding-vector of the right dimensionality.
Similarity operations tend to be done on the unit-normalized versions of the word-vectors. That is, vectors that have all been scaled to have a magnitude of 1.0. (This makes the cosine-similarity calculation easier.) The syn0norm array is filled with these unit-normalized vectors, the first time they're needed.
This syn0norm will be empty until either you do an operation (like most_similar()) that requires it, or you explicitly do an init_sims() call. If you explicitly do an init_sims(replace=True) call, you'll actually clobber the raw vectors, in-place, with the unit-normed vectors. This saves the memory that storing both vectors for every word would otherwise require. (However, some word-vector uses may still be interested in the original raw vectors of varying magnitudes, so only do this when you're sure most_similar() cosine-similarity operations are all you'll need.)
The syn1 (or syn1neg in the more common case of negative-sampling training) properties, when they exist on a full model (and not for a plain KeyedVectors object of only word-vectors), are the model neural network's internal 'hidden' weights leading to the output nodes. They're needed during model training, but not a part of the typical word-vectors collected after training.
I believe the syn prefix is just a convention from neural-network variable-naming, likely derived from 'synapse'.
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
Great explanation, thank you!
– blue-phoenox
Nov 16 '18 at 7:21
add a comment |
These names were inherited from the original Google word2vec.c implementation, upon which the gensim Word2Vec class was based. (I believe syn0 only exists in recent versions for backward-compatbility.)
The syn0 array essentially holds raw word-vectors. From the perspective of the neural-network used to train word-vectors, these vectors are a 'projection layer' that can convert a one-hot encoding of a word into a dense embedding-vector of the right dimensionality.
Similarity operations tend to be done on the unit-normalized versions of the word-vectors. That is, vectors that have all been scaled to have a magnitude of 1.0. (This makes the cosine-similarity calculation easier.) The syn0norm array is filled with these unit-normalized vectors, the first time they're needed.
This syn0norm will be empty until either you do an operation (like most_similar()) that requires it, or you explicitly do an init_sims() call. If you explicitly do an init_sims(replace=True) call, you'll actually clobber the raw vectors, in-place, with the unit-normed vectors. This saves the memory that storing both vectors for every word would otherwise require. (However, some word-vector uses may still be interested in the original raw vectors of varying magnitudes, so only do this when you're sure most_similar() cosine-similarity operations are all you'll need.)
The syn1 (or syn1neg in the more common case of negative-sampling training) properties, when they exist on a full model (and not for a plain KeyedVectors object of only word-vectors), are the model neural network's internal 'hidden' weights leading to the output nodes. They're needed during model training, but not a part of the typical word-vectors collected after training.
I believe the syn prefix is just a convention from neural-network variable-naming, likely derived from 'synapse'.
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
These names were inherited from the original Google word2vec.c implementation, upon which the gensim Word2Vec class was based. (I believe syn0 only exists in recent versions for backward-compatbility.)
The syn0 array essentially holds raw word-vectors. From the perspective of the neural-network used to train word-vectors, these vectors are a 'projection layer' that can convert a one-hot encoding of a word into a dense embedding-vector of the right dimensionality.
Similarity operations tend to be done on the unit-normalized versions of the word-vectors. That is, vectors that have all been scaled to have a magnitude of 1.0. (This makes the cosine-similarity calculation easier.) The syn0norm array is filled with these unit-normalized vectors, the first time they're needed.
This syn0norm will be empty until either you do an operation (like most_similar()) that requires it, or you explicitly do an init_sims() call. If you explicitly do an init_sims(replace=True) call, you'll actually clobber the raw vectors, in-place, with the unit-normed vectors. This saves the memory that storing both vectors for every word would otherwise require. (However, some word-vector uses may still be interested in the original raw vectors of varying magnitudes, so only do this when you're sure most_similar() cosine-similarity operations are all you'll need.)
The syn1 (or syn1neg in the more common case of negative-sampling training) properties, when they exist on a full model (and not for a plain KeyedVectors object of only word-vectors), are the model neural network's internal 'hidden' weights leading to the output nodes. They're needed during model training, but not a part of the typical word-vectors collected after training.
I believe the syn prefix is just a convention from neural-network variable-naming, likely derived from 'synapse'.
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
answered Nov 16 '18 at 7:12
gojomogojomo
19.5k64467
19.5k64467
Great explanation, thank you!
– blue-phoenox
Nov 16 '18 at 7:21
add a comment |
Great explanation, thank you!
– blue-phoenox
Nov 16 '18 at 7:21
Great explanation, thank you!
– blue-phoenox
Nov 16 '18 at 7:21
Great explanation, thank you!
– blue-phoenox
Nov 16 '18 at 7:21
add a comment |
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