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Upgrading from Edward to Edward2

This guide outlines how to port code from the Edward probabilistic programming system to Edward2, a probabilistic programming language available in TensorFlow Probability. We recommend Edward users use Edward2 for specifying models and other TensorFlow Probability primitives for performing downstream computation.

Edward2 is a distillation of Edward. It is a low-level language for specifying probabilistic models as programs and manipulating their computation. Probabilistic inference, criticism, and any other part of the scientific process (Box, 1976) use arbitrary TensorFlow ops. Their associated abstractions live in the TensorFlow ecosystem such as in TensorFlow Probability, and do not strictly require Edward2.

We're in the process of porting all Edward features, examples, and tutorials to TensorFlow Probability. For current examples:

Are you having difficulties upgrading to Edward2? Raise a GitHub issue and we're happy to help. Alternatively, if you have tips, feel free to send a pull request to improve this guide.



import edward as ed
from edward.models import Empirical, Gamma, Poisson

## ['criticisms',
##  'inferences',
##  'models',
##  'util',
##   ...,  # criticisms in global namespace for convenience
##   ...,  # inference algorithms in global namespace for convenience
##   ...]  # utility functions in global namespace for convenience

Edward2 / TensorFlow Probability.

import tensorflow_probability as tfp
from tensorflow_probability import edward2 as ed

## [...,  # random variables
##  'as_random_variable',  # various tools for manipulating program execution
##  'get_interceptor',
##  'interception',
##  'make_log_joint_fn']

Probabilistic Models

Edward. You write models inline with any other code, composing random variables. As illustration, consider a deep exponential family (Ranganath et al., 2015). (For runnable versions of the example code presented here, see the full Edward and Edward2 source files.)

bag_of_words = np.random.poisson(5., size=[256, 32000])  # training data as matrix of counts
data_size, feature_size = bag_of_words.shape  # number of documents x words (vocabulary)
units = [100, 30, 15]  # number of stochastic units per layer
shape = 0.1  # Gamma shape parameter

w2 = Gamma(0.1, 0.3, sample_shape=[units[2], units[1]])
w1 = Gamma(0.1, 0.3, sample_shape=[units[1], units[0]])
w0 = Gamma(0.1, 0.3, sample_shape=[units[0], feature_size])

z2 = Gamma(0.1, 0.1, sample_shape=[data_size, units[2]])
z1 = Gamma(shape, shape / tf.matmul(z2, w2))
z0 = Gamma(shape, shape / tf.matmul(z1, w1))
x = Poisson(tf.matmul(z0, w0))

Edward2 / TensorFlow Probability. You write models as functions, where random variables operate with the same behavior as Edward's.

def deep_exponential_family(data_size, feature_size, units, shape):
  """A multi-layered topic model over a documents-by-terms matrix."""
  w2 = ed.Gamma(0.1, 0.3, sample_shape=[units[2], units[1]], name="w2")
  w1 = ed.Gamma(0.1, 0.3, sample_shape=[units[1], units[0]], name="w1")
  w0 = ed.Gamma(0.1, 0.3, sample_shape=[units[0], feature_size], name="w0")

  z2 = ed.Gamma(0.1, 0.1, sample_shape=[data_size, units[2]], name="z2")
  z1 = ed.Gamma(shape, shape / tf.matmul(z2, w2), name="z1")
  z0 = ed.Gamma(shape, shape / tf.matmul(z1, w1), name="z0")
  x = ed.Poisson(tf.matmul(z0, w0), name="x")
  return x

Broadly, the function's outputs capture what the probabilistic program is over (the y in p(y | x)), and the function's inputs capture what the probabilistic program conditions on (the x in p(y | x)). Note it's best practice to write names to all random variables: this is useful for cleaner TensorFlow name scopes as well as for manipulating model computation.

TensorFlow Sessions

Edward. In graph mode, you fetch values from the TensorFlow graph using a built-in Edward session. Eager mode is not available.

# Generate from model: return np.ndarray of shape (data_size, feature_size).
with ed.get_session() as sess:

Edward2 / TensorFlow Probability. In graph mode, you fetch values from the TensorFlow graph using a TensorFlow session.

# Generate from model: return np.ndarray of shape (data_size, feature_size).
x = deep_exponential_family(data_size, feature_size, units, shape)

with tf.Session() as sess:  # or, e.g., tf.train.MonitoredSession()

You can also use Edward2 in eager mode (tf.enable_eager_execution()), where x already fetches the sampled NumPy array (obtainable as x.numpy()).

Probabilistic Inference

In Edward, there is a taxonomy of inference algorithms, with many built-in from the abstract classes of ed.MonteCarlo (sampling) and ed.VariationalInference (optimization). In TensorFlow Probability, inference algorithms are modularized so that they can depend on arbitrary TensorFlow ops; any associated abstractions do not strictly require Edward2. Below we outline variational inference, Markov chain Monte Carlo, and how to schedule training.

Variational Inference

Edward. You construct random variables with free parameters, representing the model's posterior approximation. You align these random variables together with the model's and construct an inference class.

def trainable_positive_pointmass(shape, name=None):
  """Learnable point mass distribution over positive reals."""
  with tf.variable_scope(None, default_name="trainable_positive_pointmass"):
    return PointMass(tf.nn.softplus(tf.get_variable("mean", shape)), name=name)

def trainable_gamma(shape, name=None):
  """Learnable Gamma via shape and scale parameterization."""
  with tf.variable_scope(None, default_name="trainable_gamma"):
    return Gamma(tf.nn.softplus(tf.get_variable("shape", shape)),
                 1.0 / tf.nn.softplus(tf.get_variable("scale", shape)),

qw2 = trainable_positive_pointmass(w2.shape)
qw1 = trainable_positive_pointmass(w1.shape)
qw0 = trainable_positive_pointmass(w0.shape)
qz2 = trainable_gamma(z2.shape)
qz1 = trainable_gamma(z1.shape)
qz0 = trainable_gamma(z0.shape)

inference = ed.KLqp({w0: qw0, w1: qw1, w2: qw2, z0: qz0, z1: qz1, z2: qz2},
                    data={x: bag_of_words})

Edward2 / TensorFlow Probability. We're in the process of making variational inference easier. For now, you set up variational inference manually (possibly using tfp.losses) and/or build your own abstractions.

Below we use Edward2's interceptors in order to manipulate model computation. We define the variational approximation—another Edward2 program—and apply interceptors to write the evidence lower bound (Hinton & Camp, 1993; Jordan, Ghahramani, Jaakkola, & Saul, 1999; Waterhouse, MacKay, & Robinson, 1996).

def deep_exponential_family_variational():
  """Posterior approx. for deep exponential family p(w{0,1,2}, z{1,2,3} | x)."""
  qw2 = trainable_positive_pointmass(w2.shape, name="qw2")  # same func as above but with ed2 rv's
  qw1 = trainable_positive_pointmass(w1.shape, name="qw1")
  qw0 = trainable_positive_pointmass(w0.shape, name="qw0")
  qz2 = trainable_gamma(z2.shape, name="qz2")  # same func as above but with ed2 rv's
  qz1 = trainable_gamma(z1.shape, name="qz1")
  qz0 = trainable_gamma(z0.shape, name="qz0")
  return qw2, qw1, qw0, qz2, qz1, qz0

# Compute expected log-likelihood. First, sample from the variational
# distribution; second, compute the log-likelihood given the sample.
qw2, qw1, qw0, qz2, qz1, qz0 = deep_exponential_family_variational()

with ed.tape() as model_tape:
  with ed.interception(ed.make_value_setter(w2=qw2, w1=qw1, w0=qw0,
                                            z2=qz2, z1=qz1, z0=qz0)):
    posterior_predictive = deep_exponential_family(data_size, feature_size, units, shape)

log_likelihood = posterior_predictive.distribution.log_prob(bag_of_words)

# Compute analytic KL-divergence between variational and prior distributions.
kl = 0.
for rv_name, variational_rv in [("z0", qz0), ("z1", qz1), ("z2", qz2),
                                ("w0", qw0), ("w1", qw1), ("w2", qw2)]:
  kl += tf.reduce_sum(variational_rv.distribution.kl_divergence(

elbo = tf.reduce_mean(log_likelihood - kl)
tf.summary.scalar("elbo", elbo)
optimizer = tf.train.AdamOptimizer(1e-3)
train_op = optimizer.minimize(-elbo)

Markov chain Monte Carlo

Edward. Similar to variational inference, you construct random variables with free parameters, representing the model's posterior approximation. You align these random variables together with the model's and construct an inference class.

num_samples = 10000  # number of events to approximate posterior

qw2 = Empirical(tf.get_variable("qw2/params", [num_samples, units[2], units[1]]))
qw1 = Empirical(tf.get_variable("qw1/params", [num_samples, units[1], units[0]]))
qw0 = Empirical(tf.get_variable("qw0/params", [num_samples, units[0], feature_size]))
qz2 = Empirical(tf.get_variable("qz2/params", [num_samples, data_size, units[2]]))
qz1 = Empirical(tf.get_variable("qz1/params", [num_samples, data_size, units[1]]))
qz0 = Empirical(tf.get_variable("qz0/params", [num_samples, data_size, units[0]]))

inference = ed.HMC({w0: qw0, w1: qw1, w2: qw2, z0: qz0, z1: qz1, z2: qz2},
                   data={x: bag_of_words})

Edward2 / TensorFlow Probability. Use the tfp.mcmc module. Operating with tfp.mcmc comprises two stages: set up a transition kernel which determines how one state propagates to the next; and apply the transition kernel over multiple iterations until convergence.

Below we first rewrite the Edward2 model in terms of its target log-probability as a function of latent variables. Namely, it is the model's log-joint probability function with fixed hyperparameters and observations anchored at the data. We then apply the higher-level tfp.mcmc.sample_chain which applies a Hamiltonian Monte Carlo transition kernel to return a collection of state transitions.

num_samples = 10000  # number of events to approximate posterior
qw2 = tf.nn.softplus(tf.random_normal([units[2], units[1]]))  # initial state
qw1 = tf.nn.softplus(tf.random_normal([units[1], units[0]]))
qw0 = tf.nn.softplus(tf.random_normal([units[0], feature_size]))
qz2 = tf.nn.softplus(tf.random_normal([data_size, units[2]]))
qz1 = tf.nn.softplus(tf.random_normal([data_size, units[1]]))
qz0 = tf.nn.softplus(tf.random_normal([data_size, units[0]]))

log_joint = ed.make_log_joint_fn(deep_exponential_family)

def target_log_prob_fn(w2, w1, w0, z2, z1, z0):
  """Target log-probability as a function of states."""
  return log_joint(data_size, feature_size, units, shape,
                   w2=w2, w1=w1, w0=w0, z2=z2, z1=z1, z0=z0, x=bag_of_words)

hmc_kernel = tfp.mcmc.HamiltonianMonteCarlo(
states, kernels_results = tfp.mcmc.sample_chain(
    current_state=[qw2, qw1, qw0, qz2, qz1, qz0],

The Training Loop

Edward. To schedule training, you call which automatically handles the schedule. Alternatively, you manually schedule training with inference's class methods.


for _ in range(inference.n_iter):
  info_dict = inference.update()


Edward2 / TensorFlow Probability. To schedule training, you use TensorFlow ops. For an equivalent API, see TensorFlow Estimator (example). For finetuning variational inference, below is one example.

max_steps = 10000  # number of training iterations
model_dir = None  # directory for model checkpoints

sess = tf.Session()
summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(model_dir, sess.graph)
start_time = time.time()
for step in range(max_steps):
  start_time = time.time()
  _, elbo_value =[train_op, elbo])
  if step % 500 == 0:
    duration = time.time() - start_time
    print("Step: {:>3d} Loss: {:.3f} ({:.3f} sec)".format(
        step, elbo_value, duration))
    summary_str =
    summary_writer.add_summary(summary_str, step)

See the deep exponential family example for more details.

Finetuning Markov chain Monte Carlo is similar. Instead of tracking a loss function, one uses, for example, a counter for the number of accepted samples. This lets us monitor a running statistic of MCMC's acceptance rate by accumulating kernel_results.is_accepted over session runs.

Model & Inference Criticism

Edward. You typically use two functions: ed.evaluate for assessing how model predictions match the true data; and ed.ppc for assessing how data generated from the model matches the true data.

# Build posterior predictive: it is parameterized by a variational posterior sample.
posterior_predictive = ed.copy(
    x, {w0: qw0, w1: qw1, w2: qw2, z0: qz0, z1: qz1, z2: qz2})

# Evaluate average log-likelihood of data.
ed.evaluate('log_likelihood', data={posterior_predictive: bag_of_words})
## np.ndarray of shape ()

# Compare TF-IDF on real vs generated data.
def tfidf(bag_of_words):
  """Computes term-frequency inverse-document-frequency."""
  num_documents = bag_of_words.shape[0]
  idf = tf.log(num_documents) - tf.log(tf.count_nonzero(bag_of_words, axis=0))
  return bag_of_words * idf

observed_statistics, replicated_statistics = ed.ppc(
    lambda data, latent_vars: tf_idx(data[posterior_predictive]),
    {posterior_predictive: bag_of_words},

Edward2 / TensorFlow Probability. Build the metric manually or use TensorFlow abstractions such as tf.metrics.

# See posterior_predictive built in Variational Inference section.
log_likelihood = tf.reduce_mean(posterior_predictive.log_prob(bag_of_words))
## tf.Tensor of shape ()

# Simple version: Compare statistics by sampling from model in a for loop.
observed_statistic =
replicated_statistic = tfidf(posterior_predictive)
replicated_statistics = [ for _ in range(100)]

See Bayesian neural networks for training with tf.metrics.accuracy and Eight Schools for visualizing manually written predictive checks.


  1. George Edward Pelham Box. Science and statistics. Journal of the American Statistical Association, 71(356), 791–799, 1976.
  2. Hinton, G. E., & Camp, D. van. (1993). Keeping the neural networks simple by minimizing the description length of the weights. In Conference on learning theory. ACM.
  3. Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.
  4. Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei. Deep exponential families. In Artificial Intelligence and Statistics, 2015.
  5. Waterhouse, S., MacKay, D., & Robinson, T. (1996). Bayesian methods for mixtures of experts. Advances in Neural Information Processing Systems, 351–357.
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