Comments (6)
Hi,
Thanks for releasing this exciting package.
Would you be able to add a classification task example to the tutorial?
thanks
Hashem
from gpflux.
Hey there! Sorry to interrupt. For some reason, it seems like I do not have the authorization to view the tutorial/documentation. I wonder if that is expected? Thank you so much!
from gpflux.
Hi @tingyuansen, thanks for bringing this to our attention. The documentation and tutorial links should all have been updated by now. Definitely let us know if you spot more broken ones.
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Thank you!
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On "GPflux with neural net layers", please discuss usage patterns and usage constraints when building hybrid models.
For example, can one put a GPLayer in the middle of a neural net model, preceded and followed by Keras layers? The existing code example (only one) has a GPLayer as the last layer but preceded by a linear Dense layer:
tf.keras.layers.Dense(1, activation="linear"),
gp_layer,
Is the linear Dense layer with output dimension 1 required?
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Hi, Thanks for releasing this exciting package. Would you be able to add a classification task example to the tutorial? thanks Hashem
I would really appreciate if an example of a classification would be provided. Especially for a multivariate data and multiclass problem. I tried to come up with a gpflux code to do that. I failed. The error outputs are extremely unhelpful. Thus, an example would be highly appreciated.
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Related Issues (20)
- Clean up BayesianDenseLayer
- Attempting to learn models with multidimensional inputs leads to an error. HOT 6
- Optimisation problem in gpflux HOT 1
- Enquiry about model.predict() with batch_size and sampling from each layer after model fitting
- Usage of NatGradModel/Wrapper to train variational parameters
- GPLayer doesn't seem to support multiple input units HOT 3
- GPLayer's prediction seems to be too confident HOT 3
- Customize NatGrad Model to turn off variational parameters in hidden layers HOT 3
- ModelCheckpoint with save_weights_only=False crashes HOT 3
- GPflux for text classification? HOT 2
- Bug when computing the mean of marginal variational q(f) HOT 3
- Relax tensorflow versions
- Incorrect WilsonSample for multioutput case
- How to save a DeepGP model
- Deep GP fit on Step Data HOT 1
- dtype errors on using the model if keras backend is not set to float64
- Add version upper bound before adjust gpflow>=2.6 HOT 4
- Fix for new GPflow heteroskedastic likelihood breaks for quadrature dependent likelihoods
- DistributionLambda for latent variable amortization/recognition networks/encoders
- Protobuf minor version pin HOT 1
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