Comments (4)
@Sangeethbala Yes, you could use TensorFlow's Dynamic RNN, or some other things in eager mode for example.
(#4)
from seq2seq-signal-prediction.
Thank you. I have a single input which has 2000 data points. In your code, i guess you are using input_seq_len as 2000 and input_dim as 1. is it possible to use input_seq_len as 1 and input_dim as 2000. I tried this and it worked and ran fastly too. Could you tell what difference it makes if we swap the role of both?
from seq2seq-signal-prediction.
The input seq_length
is the number of times we see the input, and each time we see the input we view input_dim
values at once. You don't want the input seq_length
to be equal to 1, otherwise your problem would be solvable by a "feedforward neural network" instead of a "recurrent neural network". You wouldn't have any time steps any more, so it wouldn't need to be recurrent.
from seq2seq-signal-prediction.
Note: using a feedforward neural network would be simpler if your problem is simply solvable by something that. You could thus use other simpler ML techniques.
from seq2seq-signal-prediction.
Related Issues (17)
- Different Length X, Y HOT 3
- Error when re-running training cell in notebook with TensorFlow 1.10.0 HOT 1
- input_seq_len and input_dim
- Clean pipeline using Neuraxle HOT 2
- How to predict after training HOT 1
- Answer to Exercise 4 HOT 1
- "'Tensorflow2ModelStep' object has no attribute 'name'" HOT 2
- issue with Tensorflow2ModelStep HOT 1
- How to save the model?
- Some ideas to improve this project HOT 5
- Different input and output dimensions (not lengths) HOT 5
- What is reshaped_inputs used for? HOT 1
- clarification on dec_inp HOT 1
- Regenerate charts in English rather than in French [master branch only]
- How to get the output of the encoder part
- Test Solutions / Examples
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from seq2seq-signal-prediction.