Why do recurrent layers work so well?Character recognition neural net topology/designWhy is vanishing...
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Why do recurrent layers work so well?
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On a timeseries problem that we try to solve using RNNs the input usually has the shape input_features * timesteps * batchsize and we then feed this input into recurrent layers. An alternative I see would be to flatten the data so that the shape is (input_features * timesteps) * batchsize and use a fully connected layer for our timeseries task. This would clearly work and our Dense Network would be able to find dependencies between the data at different timesteps as well. So what is it that makes recurrent layers more powerful? I would be very thankful for an intuitive explanation.
machine-learning neural-network lstm rnn
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On a timeseries problem that we try to solve using RNNs the input usually has the shape input_features * timesteps * batchsize and we then feed this input into recurrent layers. An alternative I see would be to flatten the data so that the shape is (input_features * timesteps) * batchsize and use a fully connected layer for our timeseries task. This would clearly work and our Dense Network would be able to find dependencies between the data at different timesteps as well. So what is it that makes recurrent layers more powerful? I would be very thankful for an intuitive explanation.
machine-learning neural-network lstm rnn
asked 7 mins ago
ZuberaZubera
1
New contributor
Zubera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
On a timeseries problem that we try to solve using RNNs the input usually has the shape input_features * timesteps * batchsize and we then feed this input into recurrent layers. An alternative I see would be to flatten the data so that the shape is (input_features * timesteps) * batchsize and use a fully connected layer for our timeseries task. This would clearly work and our Dense Network would be able to find dependencies between the data at different timesteps as well. So what is it that makes recurrent layers more powerful? I would be very thankful for an intuitive explanation.
machine-learning neural-network lstm rnn
asked 7 mins ago
ZuberaZubera
1
New contributor
Zubera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
On a timeseries problem that we try to solve using RNNs the input usually has the shape input_features * timesteps * batchsize and we then feed this input into recurrent layers. An alternative I see would be to flatten the data so that the shape is (input_features * timesteps) * batchsize and use a fully connected layer for our timeseries task. This would clearly work and our Dense Network would be able to find dependencies between the data at different timesteps as well. So what is it that makes recurrent layers more powerful? I would be very thankful for an intuitive explanation.
machine-learning neural-network lstm rnn
machine-learning neural-network lstm rnn
asked 7 mins ago
ZuberaZubera
1
New contributor
Zubera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
ZuberaZubera
1
New contributor
Zubera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
ZuberaZubera
1
New contributor
Zubera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 7 mins ago
ZuberaZubera
1
asked 7 mins ago
ZuberaZubera
1
1
New contributor
Zubera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Zubera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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