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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>> > >
>> > >
>> > > --
>> > > Pascal
>> > >
>> >
>> > --
>> >
>> > ---
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>>
>>
>> --
>> Pascal
>>
>
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