The error message indicates that the index variable (x_index_true) has
to have an integer dtype.
The issue in that case is that its dtype is float64, since mask has been
defined as a dmatrix(). If you define it as imatrix() or lmatrix(), then
it should work.
On Tue, Dec 06, 2016, Lijun Wu wrote:
> But when I try to feed in with M.shape[0], it failed, my code is:
>
> x = tensor.dmatrix('x')
> mask = tensor.dmatrix('m')
> mask_sum = mask.sum(axis=0)
> mask_sum_gt_1 = tensor.gt(mask_sum, 1)
> x_index= mask.sum - 2
> x_index_true = x_index * mask_sum_gt_1
> one_hot_matrix = tensor.extra_ops.to_one_hot(x_index_true, mask.shape[0])
>
> then it posted error:
> raise TypeError('index must be integers')
>
> am I doing anything wrong?
>
>
> On Wednesday, December 7, 2016 at 6:47:34 AM UTC+8, Pascal Lamblin wrote:
> >
> > Theano definitely accepts 'nb_class' as a symbolic scalar in to_one_hot().
> >
> > >>> a = tensor.ivector()
> > >>> i = tensor.iscalar()
> > >>> b = to_one_hot(a, i)
> > >>> b.eval{a: [3], i: 5})
> > array([[ 0., 0., 0., 1., 0.]])
> > >>> b.eval({a: [3], i: 4})
> > array([[ 0., 0., 0., 1.]])
> >
> >
> > On Tue, Dec 06, 2016, Lijun Wu wrote:
> > > Hi All,
> > >
> > > I want to implement the need of one_hot with variable length, so I want
> > to
> > > feed in the nb_class with a tensorVariable, but how to do this? Is there
> > > any other way?
> > >
> > > What my need is following:
> > > I have matrix A, example:
> > > [[0.1, 0.2, 0.3]
> > > [0.2, 0.1, 0.1]
> > > [0.1, 0.2, 0.2]]
> > >
> > > and one mask matrix M:
> > > [[1, 1, 1]
> > > [1, 0, 1]
> > > [0, 0, 0]]
> > >
> > > and I want to get the last one in each column of M, and get the
> > > corresponding value in A. e.g, here is
> > > [[0, 0.2, 0]
> > > [0.2, 0, 0.1]
> > > [0, 0, 0]]
> > >
> > > My solution is first get y=M.sum(axis=0), then feed y to create one_hot
> > > matrix using extra_ops.to_one_hot(), but since my M.shape[0] will be
> > > different, so I want to feed in np_class as M.shape[0], but I don't know
> > > how to do this, one_hot() can not feed in 'nb_class' as tensorvariable.
> > >
> > > Can anyone help me work on this? Thanks pretty much.
> > >
> > > --
> > >
> > > ---
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> >
> >
> > --
> > Pascal
> >
>
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>
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