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Source code for an ACL2016 paper of Chinese word segmentation
I'm trying to train the model with my own corpus, but it failed with this error:
I highlight the important lines, which apparently some matrices' dimension is not compatible to do matrix multiple: numpy.dot(A,x)
。But all I did is just replace the corpus with my own one, really don't know why this is happening. Is there any requirement for corpus or whatever other reasons that i can not figure out.
形如这样的结果:
\ x e 4 \ x b 8
这是啥啊......
当我用dynet运行代码时,出现错误
word = dy.sum_cols(dy.cmult(update_gate,dy.reshape(comb,(self.options['ndims'],wlen+1))))
AttributeError: 'module' object has no attribute 'sum_cols'
Thanks for you paper and code.
But I'm confused with some code.
In src/model.py
function get_score()
-> function inner_one_step()
update_gate = T.exp(T.dot(ugW[:ln+nhiddens,an+ln-nhiddens:an+ln+ln],com)+ugb[an+ln-nhiddens:an+ln+ln]).reshape((len+1,nhiddens))
and in src/tools.py
function get_word()
update_gate = np.exp(np.dot(ugW[:ln+ndims,an+ln-ndims:an+ln+ln],com)+ugb[an+ln-ndims:an+ln+ln]).reshape((len+1,ndims))
I think they should be
update_gate = T.exp(T.dot(ugW[:ln+nhiddens,an+ln-nhiddens:an+ln+ln],com)+ugb[an+ln-nhiddens:an+ln+ln]).reshape((nhiddens, len+1)).transpose()
and
update_gate = np.exp(np.dot(ugW[:ln+ndims,an+ln-ndims:an+ln+ln],com)+ugb[an+ln-ndims:an+ln+ln]).reshape((ndims, len+1)).transpose()
Since the code
np.exp(np.dot(ugW[:ln+ndims,an+ln-ndims:an+ln+ln],com)+ugb[an+ln-ndims:an+ln+ln])
represent a vector
[e1_1, e1_2, ..., e1_ndims, e2_1, e2_2, ...,eln_1, eln_2, ..., eln_ndim]
where ei_j derived from the jth elem of ith character.
So the reshape((nhiddens, len+1)) reshape the vector to
[e1_1, e1_2, ..., e1_ln,
e1_ln+1, e1_ln+2,...e1_ln+ln
...
eln_ndim-ln+1, ..., eln_ndim-1, eln_ndim]
which may supposed to be
[e1_1, e1_2, ..., e1_ndim,
e2_1, e2_2, ..., e2_ndim,
...
eln_1, eln_2, ..., eln_ndim]
In order to see the difference, I revised the code and ran it on CPU. But The memory occupation raised from 3GB(epoch 1) to 30GB(epoch 8). And it took 8000s per epoch.
The original code took 7000s per epoch and 2.6GB.
Did I misunderstand something?
Thanks!
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