A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
tf.GraphKeys.REGULARIZATION_LOSSES to variational inference (#813).ed.MetropolisHastings (#806).We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
This version release comes with several new features, alongside a significant push for better documentation, examples, and unit testing.
ed.KLqp's score function gradient now does more intelligent (automatic) Rao-Blackwellization for variance reduction.ed.WakeSleep).examples/lstm.py)examples/deep_exponential_family.py)examples/sigmoid_belief_network.py)examples/stochastic_block_model.py)examples/cox_process.py)We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
DirichletProcess (#652).We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
Edward requires a TensorFlow version of at least 1.1.0rc0. This includes several breaking API changes:
Normal(loc=0.0, scale=1.0) replaces the older syntax of Normal(mu=0.0, sigma=1.0).MultivariateNormalCholesky is renamed to MultivariateNormalTriL.MultivariateNormalFull is removed.rv.get_batch_shape() is renamed to rv.batch_shape.rv.get_event_shape() is renamed to rv.event_shape.sample_shape argument. This lets its associated tensor to represent more than a single sample (#591).ParamMixture random variable. It is a mixture of random variables where each component has the same distribution (#592).DirichletProcess has persistent states across calls to sample() (#565, #575, #583).ed.complete_conditional function (#588, #605, #613). See a Beta-Bernoulli example.BiGANInference for adversarial feature learning (#597).Inference, MonteCarlo, VariationalInference are abstract classes, preventing instantiation (#582).shape property to random variables. It is the same as get_shape().collections argument to random variables(#609).ed.get_blanket to get Markov blanket of a random variable (#590).ed.get_dims and ed.multivariate_rbf utility functions are removed.We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
sess.run() and eval()) is enabled for RandomVariable (#503).RandomVariable (#515).We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
RandomVariable. For example, this enables x + y (#445).RandomVariable (#483).GANInference. There's a tutorial (#310).WGANInference (#448).VariationalInference is generalized to be a tensor (#467).Inference can now work with tf.Tensor latent variables and observed variables (#488).ed.evaluate and ed.ppc. This includes support for checking implicit models and proper Monte Carlo estimates for the posterior predictive density (#485).tensorflow and tensorflow-gpu, TensorFlow is no longer an explicit dependency (#482).ed.tile utility function is removed (#484).We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
int and float data types (#421).We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.