added join function to avoid concat with only one input

theano/tensor/basic.py

@@ -4162,7 +4162,11 @@ class Join(Op):
         return [tuple(out_shapes)]
 
 
-"""
+join_ = Join()
+
+
+def join(axis, *tensors_list):
+    """
     Convenience function to concatenate `TensorType`s along the given axis.
 
     Parameters
@@ -4181,12 +4185,11 @@ class Join(Op):
         former case, the axis is fixed at construction, while in the
         latter it may vary over time depending on the value of the
         `axis` variable.
-
-"""
-
-join = Join()
-
-pprint.assign(Join, printing.FunctionPrinter('join'))
+    """
+    if len(tensors_list) == 1:
+        return tensors_list[0]
+    else:
+        return join_(axis, *tensors_list)
 
 
 def roll(x, shift, axis=None):

theano/tensor/tests/test_basic.py

@@ -4372,6 +4372,29 @@ def test_join_inplace():
     assert numpy.allclose(f(data, 0), [3, 4, 5])
 
 
+def test_join_oneInput():
+    """Test join when only 1 input is given.
+
+    This functions tests the case when concatenate is called
+    on an array of tensors but the array has only one element.
+    In this case, we would like to avoid the computational
+    overhead of concatenation of one element.
+    """
+    x_0 = theano.tensor.fmatrix()
+    x_1 = theano.tensor.fmatrix()
+    x_2 = theano.tensor.fvector()
+    join_0 = theano.tensor.concatenate([x_0], axis=1)
+    join_1 = theano.tensor.concatenate([x_0, x_1, theano.tensor.shape_padright(x_2)],
+                                       axis=1)
+    f = theano.gof.FunctionGraph([x_0], [join_0])
+    g = theano.gof.FunctionGraph([x_0, x_1, x_2], [join_1])
+
+    assert isinstance(g.toposort()[1].op, Join)
+    assert not f.toposort()
+    assert join_0.ndim is 2
+    assert join_1.ndim is 2
+
+
 class test_comparison(unittest.TestCase):
     """Test <, >, <=, >=, == and !=