8 questions
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AttributeError: 'tuple' object has no attribute 'rank' when using tensorflow_probability.layers.DenseVariational
I'm trying to use bayesian neural networks, adding a bayesian layer to a neural networks model. Here is the code I'm using and it's from the keras website.
import numpy as np
import tensorflow as tf
...
- 77
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1
answer
413
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difference between dense-layer types in tensor-flow probability
I study Bayesian Deep learning and for implement it, i found a library tensor-flow probability , for Dense layer in standard deep learning , there Dense-Variational ,Dense-reparameterize and Dense-...
- 37
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1
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149
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validation loss goes up and down [variational inference]
i was training a mlp through variational inference for a regression task on a small dataset with 1 feature. The nn works and the training loss goes down but the validation loss has random spikes ...
- 197
0
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1
answer
164
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The parameter kl_use_exact in DenseVariational layer of TF
Trying to create a Bayesian neural network using DenseVariational layer in Tensorflow probability. My question is when we set the parameter kl_use_exact to False does by that we are not taking the ...
- 27
1
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417
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Variational Inference with Normalizing Flows in Tensorflow Probability
In the last time I've read a little bit about using normalizing flows to improve variational inference f.e. Link1 Link2.
Tensorflow probability already offers RealNVP and MaskedAutoregressiveFlow in ...
- 11
5
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3
answers
791
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How to save a model with DenseVariational layer?
I'm trying to build a model with DenseVariational layer so that it can report epistemic uncertainties. Something like https://www.tensorflow.org/probability/examples/Probabilistic_Layers_Regression#...
- 644
1
vote
2
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898
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Regression Model with 3 Hidden DenseVariational Layers in Tensorflow-Probability returns nan as loss during training
I am getting acquainted with Tensorflow-Probability and here I am running into a problem. During training, the model returns nan as the loss (possibly meaning a huge loss that causes overflowing). ...
- 5,171
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678
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Number of parameters in Tensorflow-Probability network using DenseVariational layers
I cannot figure out why the model returned has in the second layer 189 parameters. The way I calculate them they should be more. Why is this happening?
The code is the following:
# Define the prior ...
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