fixed grad for Minimum, Sub, Trunc, Sum, and ConvTransp3D

theano/scalar/basic.py

@@ -1124,11 +1124,13 @@ class Minimum(BinaryScalarOp):
     def grad(self, (x, y), (gz, )):
         assert gz.type not in complex_types
         # max is not defined for complex_types
-        gx, gy = None, None
-        if x.type in float_types:
-            gx = cast(eq(minimum(x, y), x) * gz, x.type.dtype)
-        if y.type in float_types:
-            gy = cast(eq(minimum(x, y), y) * gz, y.type.dtype)
+
+        output = minimum(x,y)
+        if output.type in discrete_types:
+            return [x.zeros_like().astype(theano.config.floatX),
+                    y.zeros_like().astype(theano.config.floatX)]
+        gx = eq(output, x) * gz
+        gy = eq(output, y) * gz
         return (gx, gy)
 
 minimum = Minimum(upcast_out, name='minimum')
@@ -1225,15 +1227,13 @@ class Sub(BinaryScalarOp):
         if gz.type in complex_types:
             raise NotImplementedError()
 
-        if x.type in float_types:
-            first_part = cast(gz, x.type.dtype)
-        else:
-            first_part = None
+        if (x-y).type in discrete_types:
+            return [x.zeros_like().astype(theano.config.floatX),
+                    y.zeros_like().astype(theano.config.floatX)]
+
+        first_part = gz
+        second_part = -gz
 
-        if y.type in float_types:
-            second_part = cast(-gz, y.type.dtype)
-        else:
-            second_part = None
         return first_part, second_part
 sub = Sub(upcast_out, name='sub')
 
@@ -1767,7 +1767,7 @@ class Trunc(UnaryScalarOp):
         return numpy.trunc(x)
 
     def grad(self, (x,), (gz,)):
-        return None,
+        return x.zeros_like().astype(theano.config.floatX)
 
     def c_code(self, node, name, (x,), (z,), sub):
         return "%(z)s = %(x)s >= 0? floor(%(x)s): -floor(-%(x)s);" % locals()

theano/tensor/elemwise.py

@@ -1612,7 +1612,7 @@ class Sum(CAReduceDtype):
                 new_dims.append(i)
                 i += 1
         ds_op = DimShuffle(gz.type.broadcastable, new_dims)
-        gx = Elemwise(scalar.second)(x, ds_op(gz).astype(x.dtype))
+        gx = Elemwise(scalar.second)(x, ds_op(gz))
         return [gx]
 
     def R_op(self, inputs, eval_points):

theano/tensor/nnet/ConvTransp3D.py

@@ -2,6 +2,8 @@ import numpy as N
 from theano.tensor import basic as T
 from theano.misc import strutil
 import theano
+from theano.gradient import grad_undefined
+from theano.gradient import DisconnectedType
 
 class ConvTransp3D(theano.Op):
     """ "Transpose" of Conv3D (Conv3D implements multiplication by an implicitly defined matrix W. This implements multiplication by its transpose) """
@@ -42,6 +44,9 @@ class ConvTransp3D(theano.Op):
         W_shape, b_shape, d_shape, H_shape, RShape_shape = input_shapes
         return [(H_shape[0],  RShape[0], RShape[1], RShape[2], W_shape[4])]
 
+    def connection_pattern(self, node):
+        return [[True], [True], [True], [True], [False]]
+
     def grad(self,inputs, output_gradients):
         W,b,d,H, RShape = inputs
         dCdR ,= output_gradients
@@ -49,8 +54,10 @@ class ConvTransp3D(theano.Op):
         WShape = W.shape
         dCdW = convGrad3D(dCdR,d,WShape,H)
         dCdb = T.sum(dCdR,axis=(0,1,2,3))
-        dCdd = None #not differentiable, since d is not continuous
-        dCdRShape = None #not differentiable, since RShape is not continuous
+        # not differentiable, since d affects the output elements
+        dCdd = grad_undefined(self,2,d)
+        # disconnected, since RShape just determines the output shape
+        dCdRShape = DisconnectedType()()
 
 
         if 'name' in dir(dCdR) and dCdR.name is not None: