How do I send the results of a convolutional layer and non-deep-learning features into a dense layer in...
0
$begingroup$
- I understand that I can set up a convolutional network for 1-dimensional sequence/time series.
model = Sequential()
model.add(Conv1D())
model.add(GlobalMaxPooling1D())
model.add(Dense())
- Let's say I'd like to use "regular" (non-deep-learning) features too in my model, how should I best combine the two at a dense layer?
Concretely, let's assume that, for each row of my dataset, there are 1k points in the time series, along with 100 "regular" features.
- To generalize my question, let's say there are now two kinds of time series plus regular features for each row. If I would like to have a separate convolutional block for each time series, how do I combine all three?
deep-learning keras convolution
asked 1 min ago
wswwsw
1193
$endgroup$
add a comment |
0
$begingroup$
- I understand that I can set up a convolutional network for 1-dimensional sequence/time series.
model = Sequential()
model.add(Conv1D())
model.add(GlobalMaxPooling1D())
model.add(Dense())
- Let's say I'd like to use "regular" (non-deep-learning) features too in my model, how should I best combine the two at a dense layer?
Concretely, let's assume that, for each row of my dataset, there are 1k points in the time series, along with 100 "regular" features.
- To generalize my question, let's say there are now two kinds of time series plus regular features for each row. If I would like to have a separate convolutional block for each time series, how do I combine all three?
deep-learning keras convolution
asked 1 min ago
wswwsw
1193
$endgroup$
add a comment |
0
0
0
$begingroup$
- I understand that I can set up a convolutional network for 1-dimensional sequence/time series.
model = Sequential()
model.add(Conv1D())
model.add(GlobalMaxPooling1D())
model.add(Dense())
- Let's say I'd like to use "regular" (non-deep-learning) features too in my model, how should I best combine the two at a dense layer?
Concretely, let's assume that, for each row of my dataset, there are 1k points in the time series, along with 100 "regular" features.
- To generalize my question, let's say there are now two kinds of time series plus regular features for each row. If I would like to have a separate convolutional block for each time series, how do I combine all three?
deep-learning keras convolution
asked 1 min ago
wswwsw
1193
$endgroup$
- I understand that I can set up a convolutional network for 1-dimensional sequence/time series.
model = Sequential()
model.add(Conv1D())
model.add(GlobalMaxPooling1D())
model.add(Dense())
- Let's say I'd like to use "regular" (non-deep-learning) features too in my model, how should I best combine the two at a dense layer?
Concretely, let's assume that, for each row of my dataset, there are 1k points in the time series, along with 100 "regular" features.
- To generalize my question, let's say there are now two kinds of time series plus regular features for each row. If I would like to have a separate convolutional block for each time series, how do I combine all three?
deep-learning keras convolution
deep-learning keras convolution
asked 1 min ago
wswwsw
1193
asked 1 min ago
wswwsw
1193
asked 1 min ago
wswwsw
1193
asked 1 min ago
wswwsw
1193
asked 1 min ago
wswwsw
1193
1193
add a comment |
add a comment |
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