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The current implementation of the autoencoder architecture make picking the depth dynamically difficult because the paddings in the inverse transposes and the padding/kernel size in the last convoluti…
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Observations:
1. The reconstruction image is not good.
2. In the current code, the training procedure is very unstable.
3. The dictionary loss (the commitment loss) decreases initially, but signi…
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Currently using MNIST dataset for lecture. Would greatly benefit from using a real-world dataset such as CooperHewitt - or some kind of other highly visual dataset.
sokjc updated
4 years ago
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several days ago, I used the theanets,Autoencoder to extract audio features,while the training processure stoped at the 1536th iteration,and one day passed, the iteration still showed at 1536th iterat…
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## Is your feature request related to a problem? Please describe.
Hello,
I was wondering whether you could include an example (may be from the paper data-driven discovery of coordinates and govern…
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RuntimeError: cutlassF: no kernel found to launch!
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For the autoencoder in pyod, how do I adjust the learning rate?
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The [official instructions](https://pyod.readthedocs.io/en/latest/model_persistence.html) say to use joblib for pickling PyOD models.
This fails for AutoEncoders, or any other TensorFlow-backed mod…
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May I ask what the FeaDownSample function in the Dim_autoencoder.py file is like? The currently provided code is incomplete. Will you consider open sourcing it?