Python, Keras - ValueError: Cannot feed value of shape (10, 70, 1025) for Tensor u'dense_2_target:0', which...
I am trying to train a RNN by batches.
The input input size
(10, 70, 3075),
where 10 is the batch size, 70 the time dimension, 3075 are the frequency dimension.
There are three outputs whose size is
(10, 70, 1025)
each, basically 10 spectrograms with size (70,1025).
I would like to train this RNN by regression, whose structure is
input_img = Input(shape=(70,3075 ) )
x = Bidirectional(LSTM(n_hid,return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(input_img)
x = Dropout(0.2)(x)
x = Bidirectional(LSTM(n_hid, dropout=0.5, recurrent_dropout=0.2))(x)
x = Dropout(0.2)(x)
o0 = ( Dense(1025, activation='sigmoid'))(x)
o1 = ( Dense(1025, activation='sigmoid'))(x)
o2 = ( Dense(1025, activation='sigmoid'))(x)
The problem is that output dense layers cannot take into account three dimensions, they want something like (None, 1025), which I don't know how to provide, unless I concatenate along the time dimension.
The following error occurs:
ValueError: Cannot feed value of shape (10, 70, 1025) for Tensor u'dense_2_target:0', which has shape '(?, ?)'
Would be the batch_shape option useful in the input layer? I have actually tried it, but I've got the same error.
python tensorflow keras recurrent-neural-network
asked Jan 18 at 20:21
PhysPhys
277
add a comment |
I am trying to train a RNN by batches.
The input input size
(10, 70, 3075),
where 10 is the batch size, 70 the time dimension, 3075 are the frequency dimension.
There are three outputs whose size is
(10, 70, 1025)
each, basically 10 spectrograms with size (70,1025).
I would like to train this RNN by regression, whose structure is
input_img = Input(shape=(70,3075 ) )
x = Bidirectional(LSTM(n_hid,return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(input_img)
x = Dropout(0.2)(x)
x = Bidirectional(LSTM(n_hid, dropout=0.5, recurrent_dropout=0.2))(x)
x = Dropout(0.2)(x)
o0 = ( Dense(1025, activation='sigmoid'))(x)
o1 = ( Dense(1025, activation='sigmoid'))(x)
o2 = ( Dense(1025, activation='sigmoid'))(x)
The problem is that output dense layers cannot take into account three dimensions, they want something like (None, 1025), which I don't know how to provide, unless I concatenate along the time dimension.
The following error occurs:
ValueError: Cannot feed value of shape (10, 70, 1025) for Tensor u'dense_2_target:0', which has shape '(?, ?)'
Would be the batch_shape option useful in the input layer? I have actually tried it, but I've got the same error.
python tensorflow keras recurrent-neural-network
asked Jan 18 at 20:21
PhysPhys
277
add a comment |
I am trying to train a RNN by batches.
The input input size
(10, 70, 3075),
where 10 is the batch size, 70 the time dimension, 3075 are the frequency dimension.
There are three outputs whose size is
(10, 70, 1025)
each, basically 10 spectrograms with size (70,1025).
I would like to train this RNN by regression, whose structure is
input_img = Input(shape=(70,3075 ) )
x = Bidirectional(LSTM(n_hid,return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(input_img)
x = Dropout(0.2)(x)
x = Bidirectional(LSTM(n_hid, dropout=0.5, recurrent_dropout=0.2))(x)
x = Dropout(0.2)(x)
o0 = ( Dense(1025, activation='sigmoid'))(x)
o1 = ( Dense(1025, activation='sigmoid'))(x)
o2 = ( Dense(1025, activation='sigmoid'))(x)
The problem is that output dense layers cannot take into account three dimensions, they want something like (None, 1025), which I don't know how to provide, unless I concatenate along the time dimension.
The following error occurs:
ValueError: Cannot feed value of shape (10, 70, 1025) for Tensor u'dense_2_target:0', which has shape '(?, ?)'
Would be the batch_shape option useful in the input layer? I have actually tried it, but I've got the same error.
python tensorflow keras recurrent-neural-network
asked Jan 18 at 20:21
PhysPhys
277
I am trying to train a RNN by batches.
The input input size
(10, 70, 3075),
where 10 is the batch size, 70 the time dimension, 3075 are the frequency dimension.
There are three outputs whose size is
(10, 70, 1025)
each, basically 10 spectrograms with size (70,1025).
I would like to train this RNN by regression, whose structure is
input_img = Input(shape=(70,3075 ) )
x = Bidirectional(LSTM(n_hid,return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(input_img)
x = Dropout(0.2)(x)
x = Bidirectional(LSTM(n_hid, dropout=0.5, recurrent_dropout=0.2))(x)
x = Dropout(0.2)(x)
o0 = ( Dense(1025, activation='sigmoid'))(x)
o1 = ( Dense(1025, activation='sigmoid'))(x)
o2 = ( Dense(1025, activation='sigmoid'))(x)
The problem is that output dense layers cannot take into account three dimensions, they want something like (None, 1025), which I don't know how to provide, unless I concatenate along the time dimension.
The following error occurs:
ValueError: Cannot feed value of shape (10, 70, 1025) for Tensor u'dense_2_target:0', which has shape '(?, ?)'
Would be the batch_shape option useful in the input layer? I have actually tried it, but I've got the same error.
python tensorflow keras recurrent-neural-network
python tensorflow keras recurrent-neural-network
asked Jan 18 at 20:21
PhysPhys
277
asked Jan 18 at 20:21
PhysPhys
277
asked Jan 18 at 20:21
PhysPhys
277
asked Jan 18 at 20:21
PhysPhys
277
asked Jan 18 at 20:21
PhysPhys
277
277
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
In this instance the second RNN is collapsing the sequence to a single vector because by default return_sequences=False. To make the model return sequences and run the Dense layer over each timestep separately just add return_sequences=True to the second RNN as well:
x = Bidirectional(LSTM(n_hid, return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(x)
The Dense layers automatically apply to the last dimension so no need to reshape afterwards.
answered Jan 18 at 22:01
nuricnuric
4,9842320
This one worked for me, thanks!
– Phys
Jan 19 at 0:30
add a comment |
To get the right output shape, you can use the Reshape layer:
o0 = Dense(70 * 1025, activation='sigmoid')(x)
o0 = Reshape((70, 1025)))(o0)
This will output (batch_dim, 70, 1025). You can do exactly the same for the other two outputs.
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
In this instance the second RNN is collapsing the sequence to a single vector because by default return_sequences=False. To make the model return sequences and run the Dense layer over each timestep separately just add return_sequences=True to the second RNN as well:
x = Bidirectional(LSTM(n_hid, return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(x)
The Dense layers automatically apply to the last dimension so no need to reshape afterwards.
answered Jan 18 at 22:01
nuricnuric
4,9842320
This one worked for me, thanks!
– Phys
Jan 19 at 0:30
add a comment |
In this instance the second RNN is collapsing the sequence to a single vector because by default return_sequences=False. To make the model return sequences and run the Dense layer over each timestep separately just add return_sequences=True to the second RNN as well:
x = Bidirectional(LSTM(n_hid, return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(x)
The Dense layers automatically apply to the last dimension so no need to reshape afterwards.
answered Jan 18 at 22:01
nuricnuric
4,9842320
This one worked for me, thanks!
– Phys
Jan 19 at 0:30
add a comment |
In this instance the second RNN is collapsing the sequence to a single vector because by default return_sequences=False. To make the model return sequences and run the Dense layer over each timestep separately just add return_sequences=True to the second RNN as well:
x = Bidirectional(LSTM(n_hid, return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(x)
The Dense layers automatically apply to the last dimension so no need to reshape afterwards.
answered Jan 18 at 22:01
nuricnuric
4,9842320
In this instance the second RNN is collapsing the sequence to a single vector because by default return_sequences=False. To make the model return sequences and run the Dense layer over each timestep separately just add return_sequences=True to the second RNN as well:
x = Bidirectional(LSTM(n_hid, return_sequences=True, dropout=0.5, recurrent_dropout=0.2))(x)
The Dense layers automatically apply to the last dimension so no need to reshape afterwards.
answered Jan 18 at 22:01
nuricnuric
4,9842320
answered Jan 18 at 22:01
nuricnuric
4,9842320
answered Jan 18 at 22:01
nuricnuric
4,9842320
answered Jan 18 at 22:01
nuricnuric
4,9842320
4,9842320
This one worked for me, thanks!
– Phys
Jan 19 at 0:30
add a comment |
This one worked for me, thanks!
– Phys
Jan 19 at 0:30
This one worked for me, thanks!
– Phys
Jan 19 at 0:30
This one worked for me, thanks!
– Phys
Jan 19 at 0:30
add a comment |
To get the right output shape, you can use the Reshape layer:
o0 = Dense(70 * 1025, activation='sigmoid')(x)
o0 = Reshape((70, 1025)))(o0)
This will output (batch_dim, 70, 1025). You can do exactly the same for the other two outputs.
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
add a comment |
To get the right output shape, you can use the Reshape layer:
o0 = Dense(70 * 1025, activation='sigmoid')(x)
o0 = Reshape((70, 1025)))(o0)
This will output (batch_dim, 70, 1025). You can do exactly the same for the other two outputs.
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
add a comment |
To get the right output shape, you can use the Reshape layer:
o0 = Dense(70 * 1025, activation='sigmoid')(x)
o0 = Reshape((70, 1025)))(o0)
This will output (batch_dim, 70, 1025). You can do exactly the same for the other two outputs.
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
To get the right output shape, you can use the Reshape layer:
o0 = Dense(70 * 1025, activation='sigmoid')(x)
o0 = Reshape((70, 1025)))(o0)
This will output (batch_dim, 70, 1025). You can do exactly the same for the other two outputs.
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
answered Jan 18 at 21:53
Matias ValdenegroMatias Valdenegro
31.3k45377
31.3k45377
add a comment |
add a comment |
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