replace all uses to reduce_log*exp

edward/__init__.py

@@ -21,6 +21,6 @@
     RandomVariable
 from edward.util import copy, dot, get_ancestors, get_children, \
     get_descendants, get_dims, get_parents, get_session, get_siblings, \
-    get_variables, hessian, log_sum_exp, logit, multivariate_rbf, \
-    placeholder, random_variables, rbf, set_seed, to_simplex
+    get_variables, hessian, logit, multivariate_rbf, placeholder, \
+    random_variables, rbf, reduce_logmeanexp, set_seed, to_simplex
 from edward.version import __version__

edward/inferences/klpq.py

@@ -7,7 +7,7 @@
 
 from edward.inferences.variational_inference import VariationalInference
 from edward.models import RandomVariable, Normal
-from edward.util import copy, log_sum_exp
+from edward.util import copy
 
 
 class KLpq(VariationalInference):
@@ -134,7 +134,7 @@ def build_loss_and_gradients(self, var_list):
     q_log_prob = tf.stack(q_log_prob)
 
     log_w = p_log_prob - q_log_prob
-    log_w_norm = log_w - log_sum_exp(log_w)
+    log_w_norm = log_w - tf.reduce_logsumexp(log_w)
     w_norm = tf.exp(log_w_norm)
 
     if var_list is None:

edward/util/tensorflow.py

@@ -110,57 +110,6 @@ def hessian(y, xs):
     return tf.stack(mat)
 
 
-def log_mean_exp(input_tensor, axis=None, keep_dims=False):
-  """Computes log(mean(exp(elements across dimensions of a tensor))).
-
-  Parameters
-  ----------
-  input_tensor : tf.Tensor
-    The tensor to reduce. Should have numeric type.
-  axis : int or list of int, optional
-    The dimensions to reduce. If `None` (the default), reduces all
-    dimensions.
-  keep_dims : bool, optional
-    If true, retains reduced dimensions with length 1.
-
-  Returns
-  -------
-  tf.Tensor
-    The reduced tensor.
-  """
-  logsumexp = tf.reduce_logsumexp(input_tensor, axis, keep_dims)
-  input_tensor = tf.convert_to_tensor(input_tensor)
-  n = input_tensor.get_shape().as_list()
-  if axis is None:
-    n = tf.cast(tf.reduce_prod(n), logsumexp.dtype)
-  else:
-    n = tf.cast(tf.reduce_prod(n[axis]), logsumexp.dtype)
-
-  return -tf.log(n) + logsumexp
-
-
-def log_sum_exp(input_tensor, axis=None, keep_dims=False, name=None):
-  """Compute the ``log_sum_exp`` of elements in a tensor, taking
-  the sum across axes given by ``axis``.
-
-  Parameters
-  ----------
-  input_tensor : tf.Tensor
-    The tensor to reduce. Should have numeric type.
-  axis : int or list of int, optional
-    The dimensions to reduce. If `None` (the default), reduces all
-    dimensions.
-  keep_dims : bool, optional
-    If true, retains reduced dimensions with length 1.
-
-  Returns
-  -------
-  tf.Tensor
-    The reduced tensor.
-  """
-  return tf.reduce_logsumexp(input_tensor, axis, keep_dims, name)
-
-
 def logit(x):
   """Evaluate :math:`\log(x / (1 - x))` elementwise.
 
@@ -290,6 +239,35 @@ def rbf(x, y=0.0, sigma=1.0, l=1.0):
       tf.exp(-1.0 / (2.0 * tf.pow(l, 2.0)) * tf.pow(x - y, 2.0))
 
 
+def reduce_logmeanexp(input_tensor, axis=None, keep_dims=False):
+  """Computes log(mean(exp(elements across dimensions of a tensor))).
+
+  Parameters
+  ----------
+  input_tensor : tf.Tensor
+    The tensor to reduce. Should have numeric type.
+  axis : int or list of int, optional
+    The dimensions to reduce. If `None` (the default), reduces all
+    dimensions.
+  keep_dims : bool, optional
+    If true, retains reduced dimensions with length 1.
+
+  Returns
+  -------
+  tf.Tensor
+    The reduced tensor.
+  """
+  logsumexp = tf.reduce_logsumexp(input_tensor, axis, keep_dims)
+  input_tensor = tf.convert_to_tensor(input_tensor)
+  n = input_tensor.get_shape().as_list()
+  if axis is None:
+    n = tf.cast(tf.reduce_prod(n), logsumexp.dtype)
+  else:
+    n = tf.cast(tf.reduce_prod(n[axis]), logsumexp.dtype)
+
+  return -tf.log(n) + logsumexp
+
+
 def to_simplex(x):
   """Transform real vector of length ``(K-1)`` to a simplex of dimension ``K``
   using a backward stick breaking construction.

examples/iwvi.py

@@ -14,7 +14,7 @@
 
 from edward.inferences import VariationalInference
 from edward.models import Bernoulli, Normal, RandomVariable
-from edward.util import copy, log_mean_exp
+from edward.util import copy, reduce_logmeanexp
 from scipy.special import expit
 
 
@@ -78,7 +78,7 @@ def build_loss_and_gradients(self, var_list):
 
       log_w += [p_log_prob - q_log_prob]
 
-    loss = -log_mean_exp(log_w)
+    loss = -reduce_logmeanexp(log_w)
     grads = tf.gradients(loss, [v._ref() for v in var_list])
     grads_and_vars = list(zip(grads, var_list))
     return loss, grads_and_vars

examples/tf_mixture_gaussian.py

@@ -15,7 +15,7 @@
 
 from edward.models import Dirichlet, Normal, InverseGamma
 from edward.stats import dirichlet, invgamma, multivariate_normal_diag, norm
-from edward.util import get_dims, log_sum_exp
+from edward.util import get_dims
 
 plt.style.use('ggplot')
 
@@ -68,9 +68,9 @@ def log_prob(self, xs, zs):
                  sigmas[(k * self.D):((k + 1) * self.D)])]
 
     matrix = tf.stack(matrix)
-    # log_sum_exp() along the rows is a vector, whose nth
+    # log sum exp along the rows is a vector, whose nth
     # element is the log-likelihood of data point x_n.
-    vector = log_sum_exp(matrix, 0)
+    vector = tf.reduce_logsumexp(matrix, 0)
     # Sum over data points to get the full log-likelihood.
     log_lik = tf.reduce_sum(vector)
 

examples/tf_mixture_gaussian_laplace.py

@@ -13,7 +13,7 @@
 
 from edward.models import PointMass
 from edward.stats import dirichlet, invgamma, multivariate_normal_diag, norm
-from edward.util import get_dims, log_sum_exp
+from edward.util import get_dims
 
 
 class MixtureGaussian:
@@ -64,9 +64,9 @@ def log_prob(self, xs, zs):
                  sigmas[(k * self.D):((k + 1) * self.D)])]
 
     matrix = tf.stack(matrix)
-    # log_sum_exp() along the rows is a vector, whose nth
+    # log sum exp along the rows is a vector, whose nth
     # element is the log-likelihood of data point x_n.
-    vector = log_sum_exp(matrix, 0)
+    vector = tf.reduce_logsumexp(matrix, 0)
     # Sum over data points to get the full log-likelihood.
     log_lik = tf.reduce_sum(vector)
 

examples/tf_mixture_gaussian_map.py

@@ -13,7 +13,7 @@
 
 from edward.models import PointMass
 from edward.stats import dirichlet, invgamma, multivariate_normal_diag, norm
-from edward.util import get_dims, log_sum_exp
+from edward.util import get_dims
 
 
 class MixtureGaussian:
@@ -64,9 +64,9 @@ def log_prob(self, xs, zs):
                  sigmas[(k * self.D):((k + 1) * self.D)])]
 
     matrix = tf.stack(matrix)
-    # log_sum_exp() along the rows is a vector, whose nth
+    # log sum exp along the rows is a vector, whose nth
     # element is the log-likelihood of data point x_n.
-    vector = log_sum_exp(matrix, 0)
+    vector = tf.reduce_logsumexp(matrix, 0)
     # Sum over data points to get the full log-likelihood.
     log_lik = tf.reduce_sum(vector)