That's not really possible unfortunately, in particular because each
sequence and intermediate output needs to be a tensor (so the
concatenation is a tensor with 1 more dimension).
On 2018-04-11 08:17 AM, Caroline Etienne wrote:
Hello,
I would like to have one variable representing the huge list of
TensorVariable provided with dic_in and dic_out. But I don't manage to
do that with the inner function of theano scan.
|
||def|inner_function(dic_in, dic_out, *args)|||:|
| ||||||||||||||||input_t0, input_t1, input_t2 = ||||||dic_in||||
|||||||||||||| cell_tm1, cell_tm2, cell_tm3 =
||||||dic_out||||||||||||||
|
||||||||
|and not
|
|||def|inner_function(input_t0, input_t1, input_t2, cell_tm1, cell_tm2,
cell_tm3, *args):||||
|
|
|
||||||||nb_time_steps =3
jump_step =1
step_list_in =[i fori inrange(0,nb_time_steps,jump_step)]
step_list_out =[i fori inrange(-nb_time_steps,0,jump_step)]
dic_in ={'input':sequences,'taps':step_list_in}
dic_out ={'initial':cell_init,'taps':step_list_out}
|non_seqs| =
results, updates=theano.scan(fn=inner_function,sequences
=[dic_in], outputs_info =[dic_out],
go_backwards=self.backwards,truncate_gradient=self.gradient_steps,
non_sequences =non_seqs,strict=True)[0]
|
Would someone have some ideas ?
Thank you !
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