refactored tensor.constant and tensor.value to include an ndim argument. also…

theano/tensor/basic.py

@@ -59,7 +59,7 @@ def __oplist_tag(thing, tag):
     thing.__oplist_tags = tags
 
 
-def as_tensor(x, name = None):
+def as_tensor(x, name = None, ndim=None):
     """Return `x`, transformed into a `Tensor`
 
     This function is often used by `make_node` methods of `Op` subclasses to
@@ -73,6 +73,8 @@ def as_tensor(x, name = None):
        to make an ndarray.
      - `name`: str or None
        If a new `Result` instance is created, it will be named with this string.
+     - `ndim`: None or integer
+       Return a Result with this many dimensions.  Raise TypeError if it's not possible.
 
     :Exceptions:
      - `ValueError`: raised if an `Apply` with no default output is fetched
@@ -88,12 +90,23 @@ def as_tensor(x, name = None):
             x = x.outputs[0]
     if isinstance(x, Result):
         if isinstance(x.type, scal.Scalar):
-            return tensor_from_scalar(x)
+            x = tensor_from_scalar(x)
+
         if not isinstance(x.type, Tensor):
             raise TypeError("Result type field must be a Tensor.", x, x.type)
-        return x
+
+        if ndim is None:
+            return x
+        else:
+            if (x.type.ndim > ndim):
+                #TODO: strip off leading broadcastable dimensions
+                raise ValueError('Tensor could not be cast to have %i dimensions' % ndim, x.type)
+            elif (x.type.ndim < ndim):
+                return shape_padleft(x, n_ones=(ndim - x.type.ndim))
+            else:
+                return x
     try:
-        return constant(x)
+        return constant(x, name=name, ndim=ndim)
     except TypeError:
         try:
             str_x = str(x)
@@ -105,43 +118,39 @@ def as_tensor(x, name = None):
 # to upcast their arguments... this internal-use function is a good place to put debugging stuff, better than the global astensor.
 _as_tensor = as_tensor
 
-
-def constant(x, name=None):
+def constant_or_value(x, rtype, name=None, ndim=None):
     """Return a symbolic `Constant` with value `x`
     
     :Exceptions:
      - `TypeError`: `x` could not be converted to a numpy.ndarray
-    """
-    if isinstance(x, numpy.ndarray):
-        x_ = x
-    else:
-        x_ = numpy.asarray(x)
-    try:
-        return TensorConstant(Tensor(dtype = x_.dtype,
-                                     broadcastable = [d == 1 for d in x_.shape]), x_, name=name)
-    except:
-        raise TypeError("Could not convert %s to Tensor" % x, type(x))
+     - `ValueError`: `x` could not be expanded to have ndim dimensions
 
-def value(x, name=None):
-    """Return a symbolic `Value` with default value `x`
-    
-    :Exceptions:
-     - `TypeError`: `x` could not be converted to a numpy.ndarray
     """
     if isinstance(x, numpy.ndarray):
         x_ = x
     else:
         x_ = numpy.asarray(x)
+
+    bcastable = [d == 1 for d in x_.shape]
+    if ndim is not None:
+        if len(bcastable) < ndim:
+            bcastable = [True] * (ndim - len(bcastable)) + bcastable
+        elif len(bcastable) > ndim:
+            #TODO: strip off dimensions of size 1
+            raise ValueError('ndarray could not be cast to constant with %i dimensions' % ndim)
+        assert len(bcastable) == ndim
+
     try:
-        if name is None:
-            return TensorValue(Tensor(dtype = x_.dtype,
-                                  broadcastable = [d == 1 for d in x_.shape]), x_)
-        else:
-            return TensorValue(Tensor(dtype = x_.dtype,
-                                  broadcastable = [d == 1 for d in x_.shape]), x_, name=name)
+        return rtype(Tensor(dtype = x_.dtype, broadcastable = bcastable), x_, name=name)
     except:
         raise TypeError("Could not convert %s to Tensor" % x, type(x))
 
+def constant(x, name=None, ndim=None):
+    return constant_or_value(x, rtype=TensorConstant, name=name, ndim=ndim)
+
+def value(x, name=None, ndim=None):
+    return constant_or_value(x, rtype=TensorValue, name=name, ndim=ndim)
+
 
 
 class Tensor(Type):