Wrap exp2 from numpy, elemwise.

theano/scalar/basic.py

@@ -1944,6 +1944,25 @@ class Exp(UnaryScalarOp):
 exp = Exp(upgrade_to_float, name='exp')
 
 
+class Exp2(UnaryScalarOp):
+    def impl(self, x):
+        return numpy.exp2(x)
+
+    def grad(self, (x, ), (gz, )):
+        if x.type in complex_types:
+            raise NotImplementedError()
+        elif x.type in float_types:
+            return gz * exp2(x) * log(numpy.cast[x.type](2)),
+        else:
+            return None,
+
+    def c_code(self, node, name, (x, ), (z, ), sub):
+        if node.inputs[0].type in complex_types:
+            raise NotImplementedError('type not supported', type)
+        return "%(z)s = exp2(%(x)s);" % locals()
+exp2 = Exp2(upgrade_to_float, name='exp2')
+
+
 class Sqr(UnaryScalarOp):
     def impl(self, x):
         return x * x

theano/tensor/basic.py

@@ -2477,6 +2477,11 @@ def exp(a):
     """e^`a`"""
 
 
+@_scal_elemwise_with_nfunc('exp2', 1, 1)
+def exp2(a):
+    """2^`a`"""
+
+
 @_scal_elemwise_with_nfunc('negative', 1, 1)
 def neg(a):
     """-a"""

theano/tensor/inplace.py

@@ -87,6 +87,10 @@ def abs__inplace(a):
 def exp_inplace(a):
     """e^`a` (inplace on `a`)"""
 
+@_scal_inplace
+def exp2_inplace(a):
+    """2^`a` (inplace on `a`)"""
+
 @_scal_inplace
 def neg_inplace(a):
     """-a (inplace on a)"""

theano/tensor/tests/test_basic.py

@@ -951,6 +951,17 @@ ExpInplaceTester = makeBroadcastTester(op = inplace.exp_inplace,
                                          inplace = True)
 
 
+Exp2Tester = makeBroadcastTester(op = tensor.exp2,
+                                  expected = numpy.exp2,
+                                  good = _good_broadcast_unary_normal,
+                                  grad = _grad_broadcast_unary_normal)
+Exp2InplaceTester = makeBroadcastTester(op = inplace.exp2_inplace,
+                                         expected = numpy.exp2,
+                                         good = _good_broadcast_unary_normal,
+                                         grad = _grad_broadcast_unary_normal,
+                                         inplace = True)
+
+
 _good_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
                                       integers = (randint_ranged(1, 5, (2, 3)),),
                                       complex = (randc128_ranged(1, 5, (2,3)),),