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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