InferPy
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InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
Hi, First of all, thank you so much for inferpy. Its an wonderful project. I am trying to implement Poisson Matrix factorization using this library. I first implemented the basic...
Make compatible with TF2 (non eager) and latest versions of TFP . This will not affect to the API, only internal changes are required. Some useful code: ``` # check...
Inferpy is compatible with tensorflow-probability (TFP) up to the version 0.7.0 (included). After this, some spacenames in TFP were changed making incompatible with previous versions.
Wrong sample_shape when name is given and using size in the data model ``` import inferpy as inf import tensorflow as tf with inf.datamodel(size=100): x = inf.Normal(tf.ones(4), 1, name="x") x.sample_shape...
Hi, This library is very convenient. Does this library support convolutional neural networks, such as tfp.layers.Convolution2DFlipout? nnetwork = inf.layers.Sequential([ tfp.layers.Convolution2DFlipout(.....), tf.keras.layers.BatchNormalization(), tf.keras.layers.Activation('relu'), tf.keras.layers.MaxPooling2D(......), tf.keras.layers.Flatten(), tfp.layers.DenseFlipout(......), tfp.layers.DenseFlipout(........) ])
I am trying to implement the Rasch Model with the dataset below, I am quite confused at the moment of how to implement this. Could anyone give me a slight...
I am using the same model as in #195 (closed; thank you!), but attempting to use MCMC rather than VI. (Note that I can successfully run the sample notebook MCMC-logregression.ipynb)...
Consider the following model: ``` @inf.probmodel def vae(k, d0, d, decoder): with inf.datamodel(): z = inf.Normal(tf.ones(k), 1,name="z") x = inf.Normal(decoder(d0, d, z), 1, name="x") def decoder(d0, d, z): return inf.keras.Sequential([...
I am having trouble estimating a covariance matrix using a Wishart prior. This may be related to a previously reported issue in tensorflow-probability: [https://github.com/GPflow/GPflow/issues/553](url) Error message: `InvalidArgumentError: Cholesky decomposition was...
In order to build the graph of dependencies we create the model once, in a new graph and session. If the input tensor has been created in a different graph,...
