keras Bidirectional layer using 4 dimension data
0
I'am designing keras model for classification based on article data.
I have data with 4 dimension as follows
[batch, article_num, word_num, word embedding size]
and i want to feed each (word_num, word embedding) data into keras Bidirectional layer
in order to get result with 3 dimension as follows.
[batch, article_num, bidirectional layer output size]
when i tried to feed 4 dimension data for testing like this
inp = Input(shape=(article_num, word_num, ))
# dims = [batch, article_num, word_num]
x = Reshape((article_num * word_num, ), input_shape = (article_num, word_num))(inp)
# dims = [batch, article_num * word_num]
x = Embedding(word_num, word_embedding_size, input_length = article_num * word_num)(x)
# dims = [batch, article_num * word_num, word_embedding_size]
x = Reshape((article_num , word_num, word_embedding_size),
input_shape = (article_num * word_num, word_embedding_size))(x)
# dims = [batch, article_num, word_num, word_embedding_size]
x = Bidirectional(CuDNNLSTM(50, return_sequences = True),
input_shape=(article_num , word_num, word_embedding_size))(x)
and i got the error
ValueError: Input 0 is incompatible with layer bidirectional_12: expected ndim=3, found ndim=4
how can i achieve this?
machine-learning keras lstm rnn word-embedding
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
asked Nov 26 '18 at 13:13
김동규김동규
313
add a comment |
0
I'am designing keras model for classification based on article data.
I have data with 4 dimension as follows
[batch, article_num, word_num, word embedding size]
and i want to feed each (word_num, word embedding) data into keras Bidirectional layer
in order to get result with 3 dimension as follows.
[batch, article_num, bidirectional layer output size]
when i tried to feed 4 dimension data for testing like this
inp = Input(shape=(article_num, word_num, ))
# dims = [batch, article_num, word_num]
x = Reshape((article_num * word_num, ), input_shape = (article_num, word_num))(inp)
# dims = [batch, article_num * word_num]
x = Embedding(word_num, word_embedding_size, input_length = article_num * word_num)(x)
# dims = [batch, article_num * word_num, word_embedding_size]
x = Reshape((article_num , word_num, word_embedding_size),
input_shape = (article_num * word_num, word_embedding_size))(x)
# dims = [batch, article_num, word_num, word_embedding_size]
x = Bidirectional(CuDNNLSTM(50, return_sequences = True),
input_shape=(article_num , word_num, word_embedding_size))(x)
and i got the error
ValueError: Input 0 is incompatible with layer bidirectional_12: expected ndim=3, found ndim=4
how can i achieve this?
machine-learning keras lstm rnn word-embedding
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
asked Nov 26 '18 at 13:13
김동규김동규
313
add a comment |
0
0
0
I'am designing keras model for classification based on article data.
I have data with 4 dimension as follows
[batch, article_num, word_num, word embedding size]
and i want to feed each (word_num, word embedding) data into keras Bidirectional layer
in order to get result with 3 dimension as follows.
[batch, article_num, bidirectional layer output size]
when i tried to feed 4 dimension data for testing like this
inp = Input(shape=(article_num, word_num, ))
# dims = [batch, article_num, word_num]
x = Reshape((article_num * word_num, ), input_shape = (article_num, word_num))(inp)
# dims = [batch, article_num * word_num]
x = Embedding(word_num, word_embedding_size, input_length = article_num * word_num)(x)
# dims = [batch, article_num * word_num, word_embedding_size]
x = Reshape((article_num , word_num, word_embedding_size),
input_shape = (article_num * word_num, word_embedding_size))(x)
# dims = [batch, article_num, word_num, word_embedding_size]
x = Bidirectional(CuDNNLSTM(50, return_sequences = True),
input_shape=(article_num , word_num, word_embedding_size))(x)
and i got the error
ValueError: Input 0 is incompatible with layer bidirectional_12: expected ndim=3, found ndim=4
how can i achieve this?
machine-learning keras lstm rnn word-embedding
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
asked Nov 26 '18 at 13:13
김동규김동규
313
I'am designing keras model for classification based on article data.
I have data with 4 dimension as follows
[batch, article_num, word_num, word embedding size]
and i want to feed each (word_num, word embedding) data into keras Bidirectional layer
in order to get result with 3 dimension as follows.
[batch, article_num, bidirectional layer output size]
when i tried to feed 4 dimension data for testing like this
inp = Input(shape=(article_num, word_num, ))
# dims = [batch, article_num, word_num]
x = Reshape((article_num * word_num, ), input_shape = (article_num, word_num))(inp)
# dims = [batch, article_num * word_num]
x = Embedding(word_num, word_embedding_size, input_length = article_num * word_num)(x)
# dims = [batch, article_num * word_num, word_embedding_size]
x = Reshape((article_num , word_num, word_embedding_size),
input_shape = (article_num * word_num, word_embedding_size))(x)
# dims = [batch, article_num, word_num, word_embedding_size]
x = Bidirectional(CuDNNLSTM(50, return_sequences = True),
input_shape=(article_num , word_num, word_embedding_size))(x)
and i got the error
ValueError: Input 0 is incompatible with layer bidirectional_12: expected ndim=3, found ndim=4
how can i achieve this?
machine-learning keras lstm rnn word-embedding
machine-learning keras lstm rnn word-embedding
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
asked Nov 26 '18 at 13:13
김동규김동규
313
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
asked Nov 26 '18 at 13:13
김동규김동규
313
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
edited Nov 26 '18 at 13:55
Daniel Möller
35.5k667104
35.5k667104
asked Nov 26 '18 at 13:13
김동규김동규
313
asked Nov 26 '18 at 13:13
김동규김동규
313
asked Nov 26 '18 at 13:13
김동규김동규
313
313
add a comment |
add a comment |
1 Answer
1
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If you don't want it to touch the article_num dimension, you can try using a TimeDistributed wrapper. But I'm not certain that it will be compatible with bidirectional and other stuff.
inp = Input(shape=(article_num, word_num))
x = TimeDistributed(Embedding(word_num, word_embedding_size)(x))
#option 1
#x1 shape : (batch, article_num, word_num, 50)
x1 = TimeDistributed(Bidirectional(CuDNNLSTM(50, return_sequences = True)))(x)
#option 2
#x2 shape : (batch, article_num, 50)
x2 = TimeDistributed(Bidirectional(CuDNNLSTM(50)))(x)
Hints:
- Don't use
input_shapeeverywhere, you only need it at theInputtensor. - You probably don't need any of the reshapes if you also use a
TimeDistributedin the embedding. - If you don't want
word_numin the final dimension, usereturn_sequences=False.
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
Thank you so much for quick and accurate answer! I achieved what i want with TimeDistributed wrapper even though i am unclear how it works.
– 김동규
Nov 26 '18 at 14:03
Well.... it just ignores thearticle_numdimension (first dimension after the batch size). As if each article were an individual sample in the batch.
– Daniel Möller
Nov 26 '18 at 14:04
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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oldest
votes
0
If you don't want it to touch the article_num dimension, you can try using a TimeDistributed wrapper. But I'm not certain that it will be compatible with bidirectional and other stuff.
inp = Input(shape=(article_num, word_num))
x = TimeDistributed(Embedding(word_num, word_embedding_size)(x))
#option 1
#x1 shape : (batch, article_num, word_num, 50)
x1 = TimeDistributed(Bidirectional(CuDNNLSTM(50, return_sequences = True)))(x)
#option 2
#x2 shape : (batch, article_num, 50)
x2 = TimeDistributed(Bidirectional(CuDNNLSTM(50)))(x)
Hints:
- Don't use
input_shapeeverywhere, you only need it at theInputtensor. - You probably don't need any of the reshapes if you also use a
TimeDistributedin the embedding. - If you don't want
word_numin the final dimension, usereturn_sequences=False.
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
Thank you so much for quick and accurate answer! I achieved what i want with TimeDistributed wrapper even though i am unclear how it works.
– 김동규
Nov 26 '18 at 14:03
Well.... it just ignores thearticle_numdimension (first dimension after the batch size). As if each article were an individual sample in the batch.
– Daniel Möller
Nov 26 '18 at 14:04
add a comment |
0
If you don't want it to touch the article_num dimension, you can try using a TimeDistributed wrapper. But I'm not certain that it will be compatible with bidirectional and other stuff.
inp = Input(shape=(article_num, word_num))
x = TimeDistributed(Embedding(word_num, word_embedding_size)(x))
#option 1
#x1 shape : (batch, article_num, word_num, 50)
x1 = TimeDistributed(Bidirectional(CuDNNLSTM(50, return_sequences = True)))(x)
#option 2
#x2 shape : (batch, article_num, 50)
x2 = TimeDistributed(Bidirectional(CuDNNLSTM(50)))(x)
Hints:
- Don't use
input_shapeeverywhere, you only need it at theInputtensor. - You probably don't need any of the reshapes if you also use a
TimeDistributedin the embedding. - If you don't want
word_numin the final dimension, usereturn_sequences=False.
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
Thank you so much for quick and accurate answer! I achieved what i want with TimeDistributed wrapper even though i am unclear how it works.
– 김동규
Nov 26 '18 at 14:03
Well.... it just ignores thearticle_numdimension (first dimension after the batch size). As if each article were an individual sample in the batch.
– Daniel Möller
Nov 26 '18 at 14:04
add a comment |
0
0
0
If you don't want it to touch the article_num dimension, you can try using a TimeDistributed wrapper. But I'm not certain that it will be compatible with bidirectional and other stuff.
inp = Input(shape=(article_num, word_num))
x = TimeDistributed(Embedding(word_num, word_embedding_size)(x))
#option 1
#x1 shape : (batch, article_num, word_num, 50)
x1 = TimeDistributed(Bidirectional(CuDNNLSTM(50, return_sequences = True)))(x)
#option 2
#x2 shape : (batch, article_num, 50)
x2 = TimeDistributed(Bidirectional(CuDNNLSTM(50)))(x)
Hints:
- Don't use
input_shapeeverywhere, you only need it at theInputtensor. - You probably don't need any of the reshapes if you also use a
TimeDistributedin the embedding. - If you don't want
word_numin the final dimension, usereturn_sequences=False.
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
If you don't want it to touch the article_num dimension, you can try using a TimeDistributed wrapper. But I'm not certain that it will be compatible with bidirectional and other stuff.
inp = Input(shape=(article_num, word_num))
x = TimeDistributed(Embedding(word_num, word_embedding_size)(x))
#option 1
#x1 shape : (batch, article_num, word_num, 50)
x1 = TimeDistributed(Bidirectional(CuDNNLSTM(50, return_sequences = True)))(x)
#option 2
#x2 shape : (batch, article_num, 50)
x2 = TimeDistributed(Bidirectional(CuDNNLSTM(50)))(x)
Hints:
- Don't use
input_shapeeverywhere, you only need it at theInputtensor. - You probably don't need any of the reshapes if you also use a
TimeDistributedin the embedding. - If you don't want
word_numin the final dimension, usereturn_sequences=False.
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
edited Nov 26 '18 at 13:51
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
answered Nov 26 '18 at 13:45
Daniel MöllerDaniel Möller
35.5k667104
35.5k667104
Thank you so much for quick and accurate answer! I achieved what i want with TimeDistributed wrapper even though i am unclear how it works.
– 김동규
Nov 26 '18 at 14:03
Well.... it just ignores thearticle_numdimension (first dimension after the batch size). As if each article were an individual sample in the batch.
– Daniel Möller
Nov 26 '18 at 14:04
add a comment |
Thank you so much for quick and accurate answer! I achieved what i want with TimeDistributed wrapper even though i am unclear how it works.
– 김동규
Nov 26 '18 at 14:03
Well.... it just ignores thearticle_numdimension (first dimension after the batch size). As if each article were an individual sample in the batch.
– Daniel Möller
Nov 26 '18 at 14:04
Thank you so much for quick and accurate answer! I achieved what i want with TimeDistributed wrapper even though i am unclear how it works.
– 김동규
Nov 26 '18 at 14:03
Thank you so much for quick and accurate answer! I achieved what i want with TimeDistributed wrapper even though i am unclear how it works.
– 김동규
Nov 26 '18 at 14:03
Well.... it just ignores the
article_num dimension (first dimension after the batch size). As if each article were an individual sample in the batch.– Daniel Möller
Nov 26 '18 at 14:04
Well.... it just ignores the
article_num dimension (first dimension after the batch size). As if each article were an individual sample in the batch.– Daniel Möller
Nov 26 '18 at 14:04
add a comment |
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