added log10

theano/scalar/basic.py

@@ -710,6 +710,15 @@ class Log2(UnaryScalarOp):
         return "%(z)s = log2(%(x)s);" % locals()
 log2 = Log2(upgrade_to_float, name = 'log2')
 
+class Log10(UnaryScalarOp):
+    def impl(self, x):
+        return numpy.log10(x)
+    def grad(self, (x, ), (gz, )):
+        return gz / (x * math.log(10.0)),
+    def c_code(self, node, name, (x, ), (z, ), sub):
+        return "%(z)s = log10(%(x)s);" % locals()
+log10 = Log10(upgrade_to_float, name = 'log10')
+
 class Exp(UnaryScalarOp):
     def impl(self, x):
         return math.exp(x)

theano/tensor/basic.py

@@ -907,7 +907,8 @@ class MaxAndArgmax(Op):
         g_x = eq(xmax_pad, x) * g_max_pad
         return g_x, None
 
-@_redefine_asRoutine(MaxAndArgmax())
+_max_and_argmax = MaxAndArgmax()
+@_redefine_asRoutine(_max_and_argmax)
 def max_and_argmax(a):
     pass
 
@@ -1054,6 +1055,10 @@ def log(a):
 def log2(a):
     """base 2 logarithm of a"""
 
+@_scal_elemwise
+def log10(a):
+    """base 10 logarithm of a"""
+
 @_scal_elemwise
 def sgn(a):
     """sign of a"""

theano/tensor/tests/test_basic.py

@@ -434,6 +434,16 @@ Log2InplaceTester = makeBroadcastTester(op = inplace.log2_inplace,
                                           grad = _grad_broadcast_unary_positive,
                                           inplace = True)
 
+Log10Tester = makeBroadcastTester(op = log10,
+                                   expected = numpy.log10,
+                                   good = _good_broadcast_unary_positive,
+                                   grad = _grad_broadcast_unary_positive)
+Log10InplaceTester = makeBroadcastTester(op = inplace.log10_inplace,
+                                          expected = numpy.log10,
+                                          good = _good_broadcast_unary_positive,
+                                          grad = _grad_broadcast_unary_positive,
+                                          inplace = True)
+
 SqrtTester = makeBroadcastTester(op = sqrt,
                                    expected = numpy.sqrt,
                                    good = _good_broadcast_unary_positive,