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@dustinvtran
Hi, I have a question. Can I build bayesian layers with sequential models with lstm using edward2?
I mean building sequential model with LSTMCellReparameterization.
If yes please he…
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Will TensorFlow Probability supports Bayesian RNN layers like LSTM or GRU layers with random kernel, bias, recurrent_kernel, recurrent_bias in the foreseeable future?
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There is a decent amount of research using Bayesian NN layers with the prior set to the posterior initialization (or a function thereof): see e.g. Dziugaite & Roy, 2017. I am currently using this meth…
biggs updated
4 years ago
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> [!NOTE]
> If you have a request to support a specific method, or would like to see priority of one of the listed methods, please open a separate issue, so it won't get buried in this thread. Base…
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How can I implement and using LSTM layers for time-series prediction with Tensorflow Probability? There is no any layer for RNN Deep learning in TFP layers in [tfp.layers](https://www.tensorflow.org/p…
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When I use my custom loss function, I got a wrong loss output if I choose keras.compile:
```
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data as mnist_data
…
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### Issue Type
Bug
### Source
binary
### Keras Version
2.16.0
### Custom Code
No
### OS Platform and Distribution
Linux Ubuntu 20.04
### Python version
3.10
### GPU model and memory
_No r…
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I am trying to train the following Bayesian CNN model, which contains several Bayesian convolution and dense layers. The inputs are images with shape `(64, 80, 3)` and the outputs are normal distribut…
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What the easiest way (in TFP) to convert a Bayesian neural network to a standard neural network?
More precisely, I would like to build a standard neural network `S` where the weights of layer `l` a…
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If you execute the following code
```
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
input = tf.keras.layers.Input(shape=(1,))
layer = tfp.layers.DenseRepa…