added a transpose method for TensorVariables and a transpose test

theano/tensor/basic.py

@@ -1236,7 +1236,31 @@ class _tensor_py_operators:
 
     #TRANSPOSE
     T = property(lambda self: transpose(self))
+
+    def transpose(self, *axes):
+        """
+        Return `tensor.transpose(self, axes)`
+        or `tensor.transpose(self, axes[0])`
+
+        If only one `axes` argument is provided and it is iterable, then it is
+        assumed to be the entire axes tuple, and passed intact to
+        tensor.transpose.
+
+        """
+        if len(axes) == 0:
+            return transpose(self)
+        try:
+            iter(axes[0])
+            iterable = True
+        except TypeError:
+            iterable = False
+        if len(axes) == 1 and iterable:
+            return transpose(self, axes[0])
+        else:
+            return transpose(self, axes)
+
     shape = property(lambda self: shape(self))
+
     size = property(lambda self: prod(self.shape))
 
     # We can't implement __len__ to provide a better error message.
@@ -1346,18 +1370,23 @@ class _tensor_py_operators:
     # CONVENIENT ACCESS TO TYPE PROPERTIES
     ndim = property(lambda self: self.type.ndim)
     """The rank of this tensor."""
+
     broadcastable = property(lambda self: self.type.broadcastable)
     """The broadcastable signature of this tensor.
 
     See :doc:`broadcasting` for details.
 
     """
+
     dtype = property(lambda self: self.type.dtype)
     """ The dtype of this tensor.  """
 
     #extra pseudo-operator symbols
-    def __dot__(left, right): return dot(left, right)
-    def __rdot__(right, left): return dot(left, right)
+    def __dot__(left, right):
+        return dot(left, right)
+
+    def __rdot__(right, left):
+        return dot(left, right)
 
     def sum(self, axis=None):
         return elemwise.Sum(axis)(self)
@@ -1389,7 +1418,6 @@ class _tensor_py_operators:
     #TO TRUMP NUMPY OPERATORS
     __array_priority__ = 1000
 
-
     def get_constant_value(self):
         return get_constant_value(self)
 
@@ -2933,10 +2961,17 @@ def get_canonical_form_slice(theslice, length):
         return value, 1
 
 
-def transpose(x, **kwargs):
-    dims = range(x.ndim-1, -1, -1)
+def transpose(x, axes=None):
+    """
+    Reorder the dimensions of x. (Default: reverse them)
+
+    This is a macro around dimshuffle that matches the numpy.transpose
+    function.
 
-    return DimShuffle(x.broadcastable, dims, inplace=False)(x)
+    """
+    if axes is None:
+        axes = range(x.ndim-1, -1, -1)
+    return DimShuffle(x.broadcastable, axes, inplace=False)(x)
 
 
 class AdvancedIndexingError(TypeError):

theano/tensor/tests/test_basic.py

@@ -5083,6 +5083,7 @@ def test_mod():
                 ):
         assert fn(a,b) == a%b, (a,)
 
+
 def test_mod_compile():
     """
     This test generate an Elemwise of Composite as:
@@ -5106,6 +5107,7 @@ def test_mod_compile():
 
     f = theano.function([x,y],out)
 
+
 def test_unalign():
     if config.floatX == 'float64':
         dtype="b1,f8"
@@ -5137,6 +5139,7 @@ def test_unalign():
         if not should_raise:
             raise Exception("Theano raised an exception when none was expected")
 
+
 def test_dimshuffle_duplicate():
     x = tensor.vector()
 
@@ -5151,7 +5154,6 @@ def test_dimshuffle_duplicate():
     assert success
 
 class T_get_constant_value(unittest.TestCase):
-
     def test_get_constant_value(self):
         a = tensor.stack(1,2,3)
         assert get_constant_value(a[0])==1
@@ -5186,6 +5188,7 @@ class T_get_constant_value(unittest.TestCase):
             for j in range(c.value.shape[1]):
                 assert get_constant_value(c[i,j]) == c.value[i,j]
 
+
 class T_as_tensor_variable(unittest.TestCase):
     """
     We test that ticket #649 stay fixed.
@@ -5215,7 +5218,6 @@ class test_complex_mod(unittest.TestCase):
 
 
 class test_size(unittest.TestCase):
-
     """
     Ensure the `size` attribute of tensors behaves as in numpy.
     """
@@ -5243,7 +5245,6 @@ class test_size(unittest.TestCase):
 
 
 class test_numpy_assumptions(unittest.TestCase):
-
     """
     Verify that some assumptions Theano makes on Numpy's behavior still hold.
     """
@@ -5274,6 +5275,43 @@ class test_numpy_assumptions(unittest.TestCase):
                 assert (dtype1 == dtype2) == (str(dtype1) == str(dtype2))
 
 
+def test_transpose():
+    x1 = tensor.dvector()
+    x2 = tensor.dmatrix()
+    x3 = tensor.dtensor3()
+
+    x1v = numpy.arange(24)
+    x2v = numpy.arange(24).reshape(2, 12)
+    x3v = numpy.arange(24).reshape(2, 3, 4)
+
+    f = theano.function([x1, x2, x3], [
+        tensor.transpose(x1),
+        tensor.transpose(x2),
+        tensor.transpose(x3),
+        x1.transpose(),
+        x2.transpose(),
+        x3.transpose(),
+        x2.transpose(0, 1),
+        x3.transpose((0, 2, 1)),
+        ])
+
+    t1, t2, t3, t1b, t2b, t3b, t2c, t3c = f(x1v, x2v, x3v)
+    assert t1.shape == numpy.transpose(x1v).shape
+    assert t2.shape == numpy.transpose(x2v).shape
+    assert t3.shape == numpy.transpose(x3v).shape
+    assert numpy.all(t1 == numpy.transpose(x1v))
+    assert numpy.all(t2 == numpy.transpose(x2v))
+    assert numpy.all(t3 == numpy.transpose(x3v))
+    assert numpy.all(t1b == x1v.transpose())
+    assert numpy.all(t2b == x2v.transpose())
+    assert numpy.all(t3b == x3v.transpose())
+    assert t2c.shape == (2, 12)
+    assert t3c.shape == (2, 4, 3)
+    assert numpy.all(t2c == x2v.transpose([0, 1]))
+    assert numpy.all(t3c == x3v.transpose([0, 2, 1]))
+
+
+
 if __name__ == '__main__':
     if 0:
         unittest.main()