Added doc to Join, and fixed the mixed-input-type bug.

theano/tensor/basic.py

@@ -1574,21 +1574,25 @@ class Join(Op):
 
     def make_node(self, *axis_and_tensors):
         """
-        WRITEME
+        :param axis: an Int or integer-valued Result
+
+        :param tensors: a variable number (but not zero) of tensors to concatenate along the
+        specified axis.  These tensors must have the same shape along all dimensions other than this axis.
+
+        :returns: a symbolic Result.  It has the same ndim as the input tensors, and the most
+        inclusive dtype.
+
         """
         axis, tensors = axis_and_tensors[0], axis_and_tensors[1:]
+        if not tensors:
+            raise ValueError('Cannot join an empty list of tensors')
         as_tensor_args= [as_tensor(x) for x in tensors]
         dtypes = [x.type.dtype for x in as_tensor_args]
+        out_dtype = scal.upcast(*dtypes)
 
         if not all(targs.type.ndim for targs in as_tensor_args):
             raise TypeError('Join cannot handle arguments of dimension 0. For joining scalar values, see @stack');
 
-        if not all([dtypes[0] == dt for dt in dtypes[1:]]):
-            # Note that we could automatically find out the appropriate dtype
-            # able to store the concatenation of all tensors, but for now we
-            # just raise an error.
-            raise TypeError('All dtypes must match', tensors)
-
         # When the axis may vary, no dimension can be guaranteed to be
         # broadcastable.
         bcastable = [False] * len(as_tensor_args[0].type.broadcastable)
@@ -1613,16 +1617,14 @@ class Join(Op):
         if inputs[0].type not in int_types: 
             raise TypeError('Axis could not be cast to an integer type', axis, inputs[0].type, int_types)
 
-        outputs = [tensor(dtype = dtypes[0],
+        outputs = [tensor(dtype = out_dtype,
                           broadcastable = bcastable)]
         return Apply(self, inputs, outputs)
 
     def perform(self, node, axis_and_tensors, (out, )):
-        """
-        WRITEME
-        """
         axis, tensors = axis_and_tensors[0], axis_and_tensors[1:]
-        out[0] = numpy.concatenate(tensors, axis = axis)
+        out[0] = numpy.asarray(numpy.concatenate(tensors, axis = axis),
+                dtype=node.outputs[0].type.dtype)
 
     def grad(self, axis_and_tensors, (gz,)):
         """ The gradient wrt a join op is a `Split`, used to partition the gradient along the