added log(sigmoid) -> softplus optimizations

theano/gof/graph.py

@@ -348,7 +348,7 @@ class Constant(Value):
         Value.__init__(self, type, data, name)
     def equals(self, other):
         # this does what __eq__ should do, but Result and Apply should always be hashable by id
-        return type(other) == type(self) and self.signature() == other.signature()
+        return isinstance(other, Constant) and self.signature() == other.signature()
     def signature(self):
         return (self.type, self.data)
     def __str__(self):

theano/tensor/nnet.py

@@ -9,6 +9,7 @@ from ..printing import pprint
 import basic as tensor
 import elemwise
 import numpy
+import opt
 
 ############
 #
@@ -733,3 +734,16 @@ class solve(gof.Op):
         raise NotImplementedError()
 
 
+
+
+logsigm_to_softplus = gof.PatternSub(
+    (tensor.log, (sigmoid, 'x')),
+    (tensor.neg, (softplus, (tensor.neg, 'x'))),
+    allow_multiple_clients = True)
+log1msigm_to_softplus = gof.PatternSub(
+    (tensor.log, (tensor.sub, tensor.constant([[1.0]]), (sigmoid, 'x'))),
+    (tensor.neg, (softplus, 'x')),
+    allow_multiple_clients = True)
+
+opt.register_specialize(logsigm_to_softplus, name = 'logsigm_to_softplus')
+opt.register_specialize(log1msigm_to_softplus, name = 'log1msigm_to_softplus')

theano/tensor/opt.py

@@ -98,10 +98,12 @@ compile.optdb.register('inplace', inplace_optimizer, 99, 'fast_run')
 
 
 def register_canonicalize(lopt, *tags, **kwargs):
-    compile.optdb['canonicalize'].register((kwargs and kwargs.pop('name')) or lopt.__name__, lopt, 'fast_run', *tags)
+    name = (kwargs and kwargs.pop('name')) or lopt.__name__
+    compile.optdb['canonicalize'].register(name, lopt, 'fast_run', *tags)
 
 def register_specialize(lopt, *tags, **kwargs):
-    compile.optdb['specialize'].register((kwargs and kwargs.pop('name')) or lopt.__name__, lopt, 'fast_run', *tags)
+    name = (kwargs and kwargs.pop('name')) or lopt.__name__
+    compile.optdb['specialize'].register(name, lopt, 'fast_run', *tags)
 
 ######################
 # DimShuffle lifters #