Changed grad to L_op in basic_scipy.py

theano/scalar/basic_scipy.py

@@ -30,12 +30,12 @@ class Erf(UnaryScalarOp):
         else:
             super(Erf, self).impl(x)
 
-    def grad(self, inp, grads):
-        x, = inp
+    def L_op(self, inputs, outputs, grads):
+        x, = inputs
         gz, = grads
         if x.type in complex_types:
             raise NotImplementedError()
-        if self(x).type in discrete_types:
+        if outputs[0].type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
             else:
@@ -61,12 +61,12 @@ class Erfc(UnaryScalarOp):
         else:
             super(Erfc, self).impl(x)
 
-    def grad(self, inp, grads):
-        x, = inp
+    def L_op(self, inputs, outputs, grads):
+        x, = inputs
         gz, = grads
         if x.type in complex_types:
             raise NotImplementedError()
-        if self(x).type in discrete_types:
+        if outputs[0].type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
             else:
@@ -108,12 +108,12 @@ class Erfcx(UnaryScalarOp):
         else:
             super(Erfcx, self).impl(x)
 
-    def grad(self, inp, grads):
-        x, = inp
+    def L_op(self, inputs, outputs, grads):
+        x, = inputs
         gz, = grads
         if x.type in complex_types:
             raise NotImplementedError()
-        if self(x).type in discrete_types:
+        if outputs[0].type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
             else:
@@ -144,12 +144,12 @@ class Erfinv(UnaryScalarOp):
         else:
             super(Erfinv, self).impl(x)
 
-    def grad(self, inp, grads):
-        x, = inp
+    def L_op(self, inputs, outputs, grads):
+        x, = inputs
         gz, = grads
         if x.type in complex_types:
             raise NotImplementedError()
-        if self(x).type in discrete_types:
+        if outputs[0].type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
             else:
@@ -177,12 +177,12 @@ class Erfcinv(UnaryScalarOp):
         else:
             super(Erfcinv, self).impl(x)
 
-    def grad(self, inp, grads):
-        x, = inp
+    def L_op(self, inputs, outputs, grads):
+        x, = inputs
         gz, = grads
         if x.type in complex_types:
             raise NotImplementedError()
-        if self(x).type in discrete_types:
+        if outputs[0].type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
             else:
@@ -214,12 +214,12 @@ class Gamma(UnaryScalarOp):
         else:
             super(Gamma, self).impl(x)
 
-    def grad(self, inputs, gout):
+    def L_op(self, inputs, outputs, gout):
         (x,) = inputs
         (gz,) = gout
         if x.type in complex_types:
             raise NotImplementedError()
-        if self(x).type in discrete_types:
+        if outputs[0].type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
             else:
@@ -251,12 +251,12 @@ class GammaLn(UnaryScalarOp):
         else:
             super(GammaLn, self).impl(x)
 
-    def grad(self, inp, grads):
-        x, = inp
+    def L_op(self, inputs, outputs, grads):
+        x, = inputs
         gz, = grads
         if x.type in complex_types:
             raise NotImplementedError()
-        if self(x).type in discrete_types:
+        if outputs[0].type in discrete_types:
             if x.type in discrete_types:
                 return [x.zeros_like(dtype=theano.config.floatX)]
             else: