add informative message to all raise errors (#563)

edward/inferences/gan_inference.py

@@ -51,8 +51,8 @@ def __init__(self, data, discriminator):
     >>>
     >>> inference = ed.GANInference({x: x_data}, discriminator)
     """
-    if discriminator is None:
-      raise NotImplementedError()
+    if not callable(discriminator):
+      raise TypeError("discriminator must be a callable function.")
 
     self.discriminator = discriminator
     super(GANInference, self).__init__(None, data)
@@ -175,7 +175,7 @@ def update(self, feed_dict=None, variables=None):
           [self.train_d, self.increment_t, self.loss_d], feed_dict)
       loss = 0.0
     else:
-      raise NotImplementedError()
+      raise NotImplementedError("variables must be None, 'Gen', or 'Disc'.")
 
     if self.debug:
       sess.run(self.op_check)
@@ -225,6 +225,6 @@ def _build_optimizer(optimizer, global_step):
     else:
       raise ValueError('Optimizer class not found:', optimizer)
   elif not isinstance(optimizer, tf.train.Optimizer):
-    raise TypeError()
+    raise TypeError("Optimizer must be a tf.train.Optimizer object.")
 
   return optimizer, global_step

edward/inferences/implicit_klqp.py

@@ -7,7 +7,7 @@
 
 from edward.inferences.gan_inference import GANInference
 from edward.models import RandomVariable
-from edward.util import copy, get_session
+from edward.util import check_latent_vars, copy, get_session
 
 
 class ImplicitKLqp(GANInference):
@@ -70,15 +70,14 @@ def __init__(self, latent_vars, data=None, discriminator=None,
     its corresponding output, ``discriminator`` must output a
     dictionary of same size and keys as ``scale``.
     """
-    if discriminator is None:
-      raise NotImplementedError()
+    if not callable(discriminator):
+      raise TypeError("discriminator must be a callable function.")
 
+    self.discriminator = discriminator
     if global_vars is None:
       global_vars = {}
-    elif not isinstance(latent_vars, dict):
-      raise TypeError()
 
-    self.discriminator = discriminator
+    check_latent_vars(global_vars)
     self.global_vars = global_vars
     # call grandparent's method; avoid parent (GANInference)
     super(GANInference, self).__init__(latent_vars, data)

edward/inferences/inference.py

@@ -167,7 +167,7 @@ def initialize(self, n_iter=1000, n_print=None, scale=None, logdir=None,
     if scale is None:
       scale = {}
     elif not isinstance(scale, dict):
-      raise TypeError()
+      raise TypeError("scale must be a dict object.")
 
     self.scale = scale
 

edward/util/random_variables.py

@@ -553,7 +553,7 @@ def get_dims(x):
   elif isinstance(x, RandomVariable):
     return x.get_batch_shape().as_list()
   else:
-    raise NotImplementedError()
+    raise TypeError("Input has invalid type: {}".format(type(x)))
 
 
 def get_parents(x, collection=None):