Dear Pascal,
Thanks for your reply. I've tried to look into the self.inp_c, but it gives
error like this:
theano.gof.fg.MissingInputError: An input of the graph, used to compute
Subtensor{int64::}(input_var, Constant{0}), was not provided and not given
a value.Use the Theano flag exception_verbosity='high',for more information
on this error.
And plus, InplaceDimShuffle{1,2,0}.0 is supposed to get the first dimension
right? Since it shuffles the dimension inplace first. So actually it should
gives 40 instead of 0.
Am I on the wrong way? Thanks!
On Monday, November 7, 2016 at 10:57:22 PM UTC-5, Pascal Lamblin wrote:
>
> The error message is telling you that the corresponding computation in
> the symbolic graph is at the line:
>
> > outputs_info=T.zeros_like(self.inp_c[0][0]))
>
> Since the corresponding node is
>
> > Subtensor{int64}(InplaceDimShuffle{1,2,0}.0, Constant{0})
>
> It is likely that the problem occurred when computing "self.inp_c[0]".
>
> The error message also gives you the shape of the indexed variable
> (self.inp_c, probably), which is (0, 40, 5).
>
> So the issue is that you are trying to take the first element of an
> empty array, which is out of bounds.
>
> On Mon, Nov 07, 2016, Jiali Zhou wrote:
> >
> > Dear all,
> > I am new to theano and trying to reproduce the result from YerevaNN of
> the
> > dmn_qa_draft.py using who did what dataset.
> > However, the codes give following errors.
> > if mode == 'train':
> > gradient_value = self.get_gradient_fn(inp, q, ans, ca, cb,
> cc,
> > cd, ce, input_mask)
> > '''
> > if self.mode == 'train':
> > print "==> computing gradients (for debugging)"
> > gradient = T.grad(self.loss, self.params)
> > self.get_gradient_fn = theano.function(inputs=[self.inp_var,
> > self.q_var, self.ans_var,
> > self.ca_var,
> > self.cb_var, self.cc_var, self.cd_var, self.ce_var,
> >
> self.input_mask_var],
> > outputs=gradient)
> >
> > '''
> > I googled online and found that the error is due to index out of bounds
> of
> > outputs if I am right. But here theano.function will give out a
> gradient.
> > So what is the exact problem here?
> > Any suggestions will be appreciated.
> >
> > File
> >
> "/Users/baymax/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.py",
>
>
> > line 873, in __call__
> >
> > self.fn() if output_subset is None else\
> >
> > IndexError: index out of bounds
> >
> > Apply node that caused the error:
> > Subtensor{int64}(InplaceDimShuffle{1,2,0}.0, Constant{0})
> >
> > Toposort index: 1057
> >
> > Inputs types: [TensorType(float32, 3D), Scalar(int64)]
> >
> > Inputs shapes: [(0, 40, 5), ()]
> >
> > Inputs strides: [(160, 4, 4), ()]
> >
> > Inputs values: [array([], shape=(0, 40, 5), dtype=float32), 0]
> >
> > Inputs type_num: [11, 7]
> >
> > Outputs clients: [[Subtensor{int64}(Subtensor{int64}.0, Constant{0})]]
> >
> >
> >
> >
> > Backtrace when the node is created(use Theano flag traceback.limit=N to
> > make it longer):
> >
> > File "main.py", line 64, in <module>
> >
> > dmn = dmn_qa.DMN_qa(**args_dict)
> >
> > File "/Users/baymax/Desktop/nlp/proj/dmn/dmn_qa.py", line 114, in
> __init__
> >
> > current_episode = self.new_episode(memory[iter - 1])
> >
> > File "/Users/baymax/Desktop/nlp/proj/dmn/dmn_qa.py", line 248, in
> > new_episode
> >
> > outputs_info=T.zeros_like(self.inp_c[0][0]))
> >
> > --
> >
> > ---
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>
>
>
> --
> Pascal
>
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