Understanding Embeddings input and output sizes
$begingroup$
I have been trying for a while to understand the dimensionality of embeddings in neural networks and I think that finally things have clicked on my brain, however I would love to check whether or not my understanding is correct.
- Embeddings are an effective way to transform words into vectors, or at least to reduce the dimensionality of the data (essentially the Bag of Words approach does not work well as data is sparse)
- If I have a text corpus that contains say, 5000 sentences, I could then pad each sentence to a standard size, for example 150 and then use embeddings (possibly the Glove pretrained ones) to get an output with dimensionality of 100, that means that I would have 5000x150x100 elements.
Is my understanding correct? If so this means that I can start training my network using mini-batches of say 16x150x100 elements, the layer after the embedding one could be then a LSTM and so on...
neural-network lstm rnn embeddings
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
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bumped to the homepage by Community♦ 9 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have been trying for a while to understand the dimensionality of embeddings in neural networks and I think that finally things have clicked on my brain, however I would love to check whether or not my understanding is correct.
- Embeddings are an effective way to transform words into vectors, or at least to reduce the dimensionality of the data (essentially the Bag of Words approach does not work well as data is sparse)
- If I have a text corpus that contains say, 5000 sentences, I could then pad each sentence to a standard size, for example 150 and then use embeddings (possibly the Glove pretrained ones) to get an output with dimensionality of 100, that means that I would have 5000x150x100 elements.
Is my understanding correct? If so this means that I can start training my network using mini-batches of say 16x150x100 elements, the layer after the embedding one could be then a LSTM and so on...
neural-network lstm rnn embeddings
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
$endgroup$
bumped to the homepage by Community♦ 9 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have been trying for a while to understand the dimensionality of embeddings in neural networks and I think that finally things have clicked on my brain, however I would love to check whether or not my understanding is correct.
- Embeddings are an effective way to transform words into vectors, or at least to reduce the dimensionality of the data (essentially the Bag of Words approach does not work well as data is sparse)
- If I have a text corpus that contains say, 5000 sentences, I could then pad each sentence to a standard size, for example 150 and then use embeddings (possibly the Glove pretrained ones) to get an output with dimensionality of 100, that means that I would have 5000x150x100 elements.
Is my understanding correct? If so this means that I can start training my network using mini-batches of say 16x150x100 elements, the layer after the embedding one could be then a LSTM and so on...
neural-network lstm rnn embeddings
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
$endgroup$
I have been trying for a while to understand the dimensionality of embeddings in neural networks and I think that finally things have clicked on my brain, however I would love to check whether or not my understanding is correct.
- Embeddings are an effective way to transform words into vectors, or at least to reduce the dimensionality of the data (essentially the Bag of Words approach does not work well as data is sparse)
- If I have a text corpus that contains say, 5000 sentences, I could then pad each sentence to a standard size, for example 150 and then use embeddings (possibly the Glove pretrained ones) to get an output with dimensionality of 100, that means that I would have 5000x150x100 elements.
Is my understanding correct? If so this means that I can start training my network using mini-batches of say 16x150x100 elements, the layer after the embedding one could be then a LSTM and so on...
neural-network lstm rnn embeddings
neural-network lstm rnn embeddings
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
asked Sep 8 '18 at 5:22
Juan Antonio Gomez MorianoJuan Antonio Gomez Moriano
611213
611213
bumped to the homepage by Community♦ 9 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 9 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Both are correct.
Too clarify: when training in mini batches, it is more common to do padding after selecting eachmini batch. Say in your mini batch of size 16, if the longest sentence is of length 35, you want to pad this mini batch to 16x35x100.
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
$endgroup$
add a comment |
$begingroup$
Input text data + vocabulary = one-hot representation
One-hot rep + embedding = featurized vectors
The greater significance of Embedding is conversion of non-contextual one-hot representation to contextual representation / featurized representation. The byproduct of this is dimensionality reduction and sparsity reduction.
Your understanding is correct on this.
answered Dec 26 '18 at 20:16
solver149solver149
112
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add a comment |
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2 Answers
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2 Answers
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$begingroup$
Both are correct.
Too clarify: when training in mini batches, it is more common to do padding after selecting eachmini batch. Say in your mini batch of size 16, if the longest sentence is of length 35, you want to pad this mini batch to 16x35x100.
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
$endgroup$
add a comment |
$begingroup$
Both are correct.
Too clarify: when training in mini batches, it is more common to do padding after selecting eachmini batch. Say in your mini batch of size 16, if the longest sentence is of length 35, you want to pad this mini batch to 16x35x100.
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
$endgroup$
add a comment |
$begingroup$
Both are correct.
Too clarify: when training in mini batches, it is more common to do padding after selecting eachmini batch. Say in your mini batch of size 16, if the longest sentence is of length 35, you want to pad this mini batch to 16x35x100.
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
$endgroup$
Both are correct.
Too clarify: when training in mini batches, it is more common to do padding after selecting eachmini batch. Say in your mini batch of size 16, if the longest sentence is of length 35, you want to pad this mini batch to 16x35x100.
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
answered Sep 8 '18 at 15:11
user12075user12075
1,266515
1,266515
add a comment |
add a comment |
$begingroup$
Input text data + vocabulary = one-hot representation
One-hot rep + embedding = featurized vectors
The greater significance of Embedding is conversion of non-contextual one-hot representation to contextual representation / featurized representation. The byproduct of this is dimensionality reduction and sparsity reduction.
Your understanding is correct on this.
answered Dec 26 '18 at 20:16
solver149solver149
112
$endgroup$
add a comment |
$begingroup$
Input text data + vocabulary = one-hot representation
One-hot rep + embedding = featurized vectors
The greater significance of Embedding is conversion of non-contextual one-hot representation to contextual representation / featurized representation. The byproduct of this is dimensionality reduction and sparsity reduction.
Your understanding is correct on this.
answered Dec 26 '18 at 20:16
solver149solver149
112
$endgroup$
add a comment |
$begingroup$
Input text data + vocabulary = one-hot representation
One-hot rep + embedding = featurized vectors
The greater significance of Embedding is conversion of non-contextual one-hot representation to contextual representation / featurized representation. The byproduct of this is dimensionality reduction and sparsity reduction.
Your understanding is correct on this.
answered Dec 26 '18 at 20:16
solver149solver149
112
$endgroup$
Input text data + vocabulary = one-hot representation
One-hot rep + embedding = featurized vectors
The greater significance of Embedding is conversion of non-contextual one-hot representation to contextual representation / featurized representation. The byproduct of this is dimensionality reduction and sparsity reduction.
Your understanding is correct on this.
answered Dec 26 '18 at 20:16
solver149solver149
112
answered Dec 26 '18 at 20:16
solver149solver149
112
answered Dec 26 '18 at 20:16
solver149solver149
112
answered Dec 26 '18 at 20:16
solver149solver149
112
112
add a comment |
add a comment |
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