Rename the batched_dot22 function to be more generic and support other tensors as well.

theano/tensor/basic.py

@@ -4325,20 +4325,20 @@ def set_subtensor(x, y, inplace=False,
     return inc_subtensor(x, y, inplace, set_instead_of_inc=True,
             tolerate_inplace_aliasing=tolerate_inplace_aliasing)
 
-def batched_dot22(x, y):
+def batched_dot(x, y):
     """
-    :param x: A 3D Tensor with sizes (dim1, dim2, dim3)
-    :param y: A 3D Tensor with sizes (dim1, dim2, dim4)
-    This function computes the dot product between the two 3D tensors, by iterating
+    :param x: A Tensor with sizes e.g.: for  3D (dim1, dim3, dim2)
+    :param y: A Tensor with sizes e.g.: for 3D (dim1, dim2, dim4)
+    This function computes the dot product between the two tensors, by iterating
     over the first dimension using scan.
-    Returns a 3D tensor of size (dim1, dim3, dim4)
+    Returns a tensor of size e.g. if it is 3D: (dim1, dim3, dim4)
     Example:
     >>> first = T.tensor3('first')
     >>> second = T.tensor3('second')
-    >>> result = batched_dot22(first, second)
+    >>> result = batched_dot(first, second)
     """
     result, updates = theano.scan(fn=lambda x_mat, y_mat:
-            theano.tensor.dot(x_mat.T, y_mat),
+            theano.tensor.dot(x_mat, y_mat),
             outputs_info=None,
             sequences=[x, y],
             non_sequences=None)

theano/tensor/tests/test_basic.py

@@ -1898,12 +1898,12 @@ def test_batched_dot22():
     first = theano.tensor.tensor3("first")
     second = theano.tensor.tensor3("second")
     output = theano.tensor.basic.batched_dot22(first, second)
-    first_val = numpy.random.rand(10, 20, 10)
+    first_val = numpy.random.rand(10, 10, 20)
     second_val = numpy.random.rand(10, 20, 5)
     result_fn = theano.function([first, second], output)
     result = result_fn(first_val, second_val)
     assert result.shape[0] == first_val.shape[0]
-    assert result.shape[1] == first_val.shape[2]
+    assert result.shape[1] == first_val.shape[1]
     assert result.shape[2] == second_val.shape[2]
 
 def test_tensor_values_eq_approx():