Preprocessors data shape Keras The Next CEO of Stack OverflowTensorflow ValueError on session.run with batch trainingDynamic tensor shape for tensorflow RNNKeras- LSTM- Input Size Error“Invalid shape for y” for Keras LSTM w/ return_sequences=True (and sklearn API)Keras : variable input sequence: padding vs shape None?Tensorflow error with input to dynamic_rnn ValueError: Cannot feed value of shape (2,) for Tensor 'Placeholder:0', which has shape '(1, ?)'R to_categorical for sequence labeling with KerasKeras LSTM Input - ValueError: Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (1745, 1)Keras embedding layer error using word2vec with gensimHow do I know the correct format for my input data into my keras RNN?
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Preprocessors data shape Keras
The Next CEO of Stack OverflowTensorflow ValueError on session.run with batch trainingDynamic tensor shape for tensorflow RNNKeras- LSTM- Input Size Error“Invalid shape for y” for Keras LSTM w/ return_sequences=True (and sklearn API)Keras : variable input sequence: padding vs shape None?Tensorflow error with input to dynamic_rnn ValueError: Cannot feed value of shape (2,) for Tensor 'Placeholder:0', which has shape '(1, ?)'R to_categorical for sequence labeling with KerasKeras LSTM Input - ValueError: Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (1745, 1)Keras embedding layer error using word2vec with gensimHow do I know the correct format for my input data into my keras RNN?
I have a dataset of pos-tagged sentences of variable length and a label of 22 categories.
I want to run an RNN on each sentence. Because the amount of tags is roughly 50, I used one_hot(sentence, num_class=50) to get integer arrays of variable size. When I transform the array to np.array It has shape (23578,). My goal is to run the RNN on each sentence where the input is the PoS class number, which means I need the input size to be 1.
How do I reshape the dataset so I'll have data of shape (23578, None, 1)?
I know I can pad the sequence but I want to leave it to as is.
python numpy keras recurrent-neural-network
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
add a comment |
I have a dataset of pos-tagged sentences of variable length and a label of 22 categories.
I want to run an RNN on each sentence. Because the amount of tags is roughly 50, I used one_hot(sentence, num_class=50) to get integer arrays of variable size. When I transform the array to np.array It has shape (23578,). My goal is to run the RNN on each sentence where the input is the PoS class number, which means I need the input size to be 1.
How do I reshape the dataset so I'll have data of shape (23578, None, 1)?
I know I can pad the sequence but I want to leave it to as is.
python numpy keras recurrent-neural-network
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
AFAIK the answer is that you must pad the sentences; you may use different padding in each batch; but I'm not aware any sequence model that doesn't use padding.
– Pedro Marques
Mar 8 at 22:57
add a comment |
I have a dataset of pos-tagged sentences of variable length and a label of 22 categories.
I want to run an RNN on each sentence. Because the amount of tags is roughly 50, I used one_hot(sentence, num_class=50) to get integer arrays of variable size. When I transform the array to np.array It has shape (23578,). My goal is to run the RNN on each sentence where the input is the PoS class number, which means I need the input size to be 1.
How do I reshape the dataset so I'll have data of shape (23578, None, 1)?
I know I can pad the sequence but I want to leave it to as is.
python numpy keras recurrent-neural-network
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
I have a dataset of pos-tagged sentences of variable length and a label of 22 categories.
I want to run an RNN on each sentence. Because the amount of tags is roughly 50, I used one_hot(sentence, num_class=50) to get integer arrays of variable size. When I transform the array to np.array It has shape (23578,). My goal is to run the RNN on each sentence where the input is the PoS class number, which means I need the input size to be 1.
How do I reshape the dataset so I'll have data of shape (23578, None, 1)?
I know I can pad the sequence but I want to leave it to as is.
python numpy keras recurrent-neural-network
python numpy keras recurrent-neural-network
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
asked Mar 8 at 15:33
Ariel BereslavskyAriel Bereslavsky
12
12
AFAIK the answer is that you must pad the sentences; you may use different padding in each batch; but I'm not aware any sequence model that doesn't use padding.
– Pedro Marques
Mar 8 at 22:57
add a comment |
AFAIK the answer is that you must pad the sentences; you may use different padding in each batch; but I'm not aware any sequence model that doesn't use padding.
– Pedro Marques
Mar 8 at 22:57
AFAIK the answer is that you must pad the sentences; you may use different padding in each batch; but I'm not aware any sequence model that doesn't use padding.
– Pedro Marques
Mar 8 at 22:57
AFAIK the answer is that you must pad the sentences; you may use different padding in each batch; but I'm not aware any sequence model that doesn't use padding.
– Pedro Marques
Mar 8 at 22:57
add a comment |
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AFAIK the answer is that you must pad the sentences; you may use different padding in each batch; but I'm not aware any sequence model that doesn't use padding.
– Pedro Marques
Mar 8 at 22:57