I have a 2D array and I would like to apply the softmax function column wise. It try the following:

```
value = numpy.array([[1.0,2.0], [3.0,9.0], [7.0,1.0]], dtype=theano.config.floatX)
m = theano.shared(value, name='m', borrow=True)
y = theano.tensor.nnet.softmax(m)
print y.eval()

```

As a result I get the following output:

```
[[ 0.26894142  0.73105858]
 [ 0.00247262  0.99752738]
 [ 0.99752738  0.00247262]]

```

Which means that the operation has been applied row wise. Is there a way to force Theano to do softmax column wise?