fix test: now zeros_like and ones_like return the same type as the model

theano/sandbox/conv.py

@@ -33,7 +33,8 @@ class ConvOp(Op):
             unroll_kern=4,
             imshp_logical=None,
             kshp_logical=None,
-            kshp_logical_top_aligned=True):
+            kshp_logical_top_aligned=True,
+            version=-1):
         """
         
 
@@ -46,7 +47,7 @@ class ConvOp(Op):
         out_mode - 'valid', 'full'
         unroll_batch - c code generation option
         unroll_kern - c code generation option
-
+        version - passed to GpuConv.
 
         The reason that this op does the summation over convolutions within the 'stack' is that
         it allows us to be memory-efficient about how gradients are calculated.  If, for
@@ -77,6 +78,7 @@ class ConvOp(Op):
         self.bsize=bsize
         self.dx=dx
         self.dy=dy
+        self.version=version
         # a triple
         self.imshp_logical = self.imshp if imshp_logical is None else tuple(imshp_logical)
         assert len(self.imshp) == len(self.imshp_logical)

theano/tensor/basic.py

@@ -133,7 +133,7 @@ _as_tensor_variable = as_tensor_variable
 as_tensor = as_tensor_variable
 
 
-def constant_or_value(x, rtype, name=None, ndim=None):
+def constant_or_value(x, rtype, name=None, ndim=None, dtype=None):
     """Return a symbolic `Constant` with value `x`
     
     :Exceptions:
@@ -141,6 +141,9 @@ def constant_or_value(x, rtype, name=None, ndim=None):
      - `ValueError`: `x` could not be expanded to have ndim dimensions
 
     """
+    if dtype is not None:
+        x_ = numpy.asarray(x, dtype=dtype)
+    else:
         x_ = None
         if rtype is TensorConstant and isinstance(x, int):
             for dtype in ['int8', 'int16', 'int32', 'int64']:
@@ -175,11 +178,11 @@ def constant_or_value(x, rtype, name=None, ndim=None):
     except:
         raise TypeError("Could not convert %s to TensorType" % x, type(x))
 
-def constant(x, name=None, ndim=None):
-    return constant_or_value(x, rtype=TensorConstant, name=name, ndim=ndim)
+def constant(x, name=None, ndim=None, dtype=None):
+    return constant_or_value(x, rtype=TensorConstant, name=name, ndim=ndim, dtype=dtype)
 
-def value(x, name=None, ndim=None):
-    return constant_or_value(x, rtype=TensorValue, name=name, ndim=ndim)
+def value(x, name=None, ndim=None, dtype=None):
+    return constant_or_value(x, rtype=TensorValue, name=name, ndim=ndim, dtype=dtype)
 
 def _obj_is_wrappable_as_tensor(x):
     try:
@@ -1234,13 +1237,14 @@ pprint.assign(fill, printing.FunctionPrinter('fill'))
 def ones_like(model):
     """WRITEME"""
     #return Ones(model.type.ndim)(shape(model))
-    return fill(model, 1.0)
+    ret= fill(model, constant(1.0, dtype=model.type.dtype))
+    return ret
 
 @constructor
 def zeros_like(model):
     """WRITEME"""
     #return Zeros(model.type.ndim)(shape(model))
-    return fill(model, 0.0)
+    return fill(model, constant(0.0, dtype=model.type.dtype))
 
 class Filler(gof.Op):
     """WRITEME"""