I don't get what you do. Can you give codes that show the problem?
On Thu, Dec 8, 2016 at 6:32 AM, Lijun Wu <[email protected]> wrote:
> Now I got another problem,
>
> if now I have a matrix that is pre-defined as: mask = tensor.matrix(), and
> since I want to use one_hot function, so I have to convert it to lmatrix or
> imatrix, how can I do this?
> I tried: fake_matrix = tensor.cast(mask, 'int64'), but the
> fake_matrix.shape[0] still can't used to feed into one_hot.
>
>
> On Wednesday, December 7, 2016 at 8:53:37 PM UTC+8, Lijun Wu wrote:
>>
>> Thx, it's right.
>>
>> On Wednesday, December 7, 2016 at 3:55:29 PM UTC+8, Pascal Lamblin wrote:
>>>
>>> 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.
>>> > > >
>>> > > > --
>>> > > >
>>> > > > ---
>>> > > > You received this message because you are subscribed to the Google
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>>> send
>>> > > an email to [email protected] <javascript:>.
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>>> > >
>>> > >
>>> > > --
>>> > > Pascal
>>> > >
>>> >
>>> > --
>>> >
>>> > ---
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>>>
>>>
>>> --
>>> Pascal
>>>
>> --
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