Softmax accepts a vector argument

theano/tensor/nnet/nnet.py

@@ -317,12 +317,12 @@ class Softmax(gof.Op):
 
     def make_node(self, x):
         x = tensor.as_tensor_variable(x)
-        if x.type.ndim != 2 \
+        if x.type.ndim not in (1, 2) \
                 or x.type.dtype not in ['float32', 'float64']:
-            raise ValueError('x must be 2-d tensor of floats')
-
-        sm = x.type.make_variable()
-        return gof.Apply(self, [x], [sm])
+            raise ValueError('x must be 1-d or 2-d tensor of floats')
+        if x.ndim == 1:
+            x = tensor.shape_padleft(x, n_ones=1)
+        return gof.Apply(self, [x], [x.type()])
 
     def perform(self, node, input_storage, output_storage):
         x, = input_storage

theano/tensor/nnet/tests/test_nnet.py

@@ -42,6 +42,13 @@ class T_Softmax(unittest.TestCase):
             return softmax(a)[:,3]
         utt.verify_grad(f, [numpy.random.rand(3,4)])
 
+    def test_vector(self):
+        x = T.vector()
+        f = theano.function([x], softmax(x))
+
+        xv = numpy.random.randn(6)
+        assert numpy.allclose(f(xv), numpy.exp(xv) / numpy.exp(xv).sum())
+
 
 class T_SoftmaxWithBias(unittest.TestCase):
     def setUp(self):