Comments (4)
Yes, you can generate samples that are longer than the training samples, by changing the length parameter in https://github.com/fjxmlzn/DoppelGANger/blob/master/example_generating_data/gan_generate_data_task.py#L132 to whatever you want.
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Thanks, I have been reading your paper, and I am trying to understand how this is done. In one sentence, is it just running multiple RNN's and MLP's that inlcude the previous generated sample in the new generation process.
"This unrolled representation commonly conveys
that the RNN is being used many times to generate samples."
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The RNN (more specifically, LSTM) has an internal state, which can store past information, so we don't need to explicitly input the previously generated sample into the next time step. By default, our generator will just input a noise variable to the RNN unit at each time step as the activation. Although, our code also supports adding the previously generated sample as an extra input to the RNN unit; you can turn on this feature by setting feedback=True in config.py.
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Excellent thanks!
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Related Issues (20)
- CLI getting stuck on running example_training/main.py HOT 2
- Dynamic attributes / attributes with time stamp? HOT 6
- Request for min/max used for feature and attribute normalization in input data HOT 2
- The data generated ranges from 0 to 2 HOT 3
- Incomplete training HOT 7
- unreasonable output HOT 6
- Dataset HOT 1
- About two MLPs HOT 1
- Training does not run although the input is of the required form HOT 6
- Generating time series with negative values HOT 4
- is_gen_flag HOT 4
- Attribute problematic result HOT 18
- Problem with tensorflow HOT 1
- Training time HOT 6
- Code of AR and HMM baseline
- unknown output type HOT 6
- Request for availability of the scripts used to reproduce figures HOT 8
- Inference from attributes HOT 3
- Unable to run main.py in example_training HOT 21
- what tensorflow_privacy version are you using when you are using the tensorflow==1.4.0 and python==3.5? HOT 7
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