Merge

theano/tensor/elemwise.py

@@ -941,12 +941,16 @@ class CAReduce(Op):
                 # If it's a zero-size array, use scalar_op.identity if available
                 if variable.shape[dimension] == 0:
                     if hasattr(self.scalar_op, 'identity'):
-                        variable = self.scalar_op.identity
+                        variable = numpy.array(self.scalar_op.identity)
                         break
                     else:
                         raise ValueError("Input (%s) has zero-size on axis %s, but self.scalar_op (%s) has no attribute 'identity'" % (variable, dimension, self.scalar_op))
                 else:
                     variable = self.ufunc.reduce(variable, dimension)
+            variable = numpy.asarray(variable)
+            if numpy.may_share_memory(variable, input):
+                # perhaps numpy is clever for reductions of size 1?  We don't want this.
+                variable = variable.copy()
             output[0] = theano._asarray(variable, dtype = node.outputs[0].type.dtype)
         else:
             output[0] = numpy.copy(variable)

theano/tensor/opt.py

@@ -764,10 +764,12 @@ def local_subtensor_make_vector(node):
 @gof.local_optimizer([T.Elemwise])
 def local_useless_elemwise(node):
     """
-eq(x,x) -> 1
-neq(x,x) -> 0
-mul(x) -> x
-add(x) -> x
+
+    eq(x,x) -> 1
+    neq(x,x) -> 0
+    mul(x) -> x
+    add(x) -> x
+    identity(x) -> x
 
     """
     if isinstance(node.op, T.Elemwise):
@@ -783,6 +785,8 @@ add(x) -> x
             return [node.inputs[0]]
         if node.op.scalar_op == theano.scalar.add and len(node.inputs)==1:
             return [node.inputs[0]]
+        if node.op.scalar_op == theano.scalar.identity and len(node.inputs)==1:
+            return [node.inputs[0]]
 
 
 @register_specialize