more selective warning about dropout being stochastic

DailyActie · Nov 14, 2014 · 53bf27d · 53bf27d
1 parent 07ee218
commit 53bf27d
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diff --git a/pylearn2/costs/cost.py b/pylearn2/costs/cost.py
@@ -222,6 +222,25 @@ def get_data_specs(self, model):
         raise NotImplementedError(str(type(self)) + " does not implement " +
                                   "get_data_specs.")
 
+    def is_stochastic(self):
+        """
+        Returns True if the cost is stochastic.
+
+        Stochastic costs are incompatible with some optimization algorithms
+        that make multiple updates per minibatch, such as algorithms that
+        use line searches. These optimizations should raise a TypeError if
+        given a stochastic Cost, or issue a warning if given a Cost whose
+        `is_stochastic` method raises NotImplementedError.
+
+        Returns
+        -------
+        is_stochastic : bool
+            Whether the cost is stochastic. For example, dropout is
+            stochastic.
+        """
+
+        raise NotImplementedError()
+
 
 class SumOfCosts(Cost):
     """

diff --git a/pylearn2/training_algorithms/bgd.py b/pylearn2/training_algorithms/bgd.py
@@ -12,6 +12,8 @@
 __email__ = "pylearn-dev@googlegroups"
 
 import logging
+import warnings
+
 import numpy as np
 from theano import config
 from theano.compat.python2x import OrderedDict
@@ -129,6 +131,15 @@ def setup(self, model, dataset):
         if self.cost is None:
             self.cost = model.get_default_cost()
 
+        try:
+            if self.cost.is_stochastic():
+                raise TypeError("BGD is not compatible with stochastic "
+                                "costs.")
+        except NotImplementedError:
+            warnings.warn("BGD is not compatible with stochastic costs "
+                          "and cannot determine whether the current cost is "
+                          "stochastic.")
+
         if self.batch_size is None:
             self.batch_size = model.force_batch_size
         else: