On Tue, Nov 08, 2016, Jiali Zhou wrote:
> 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.
What are you doing exactly that causes that 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!
It looks like self.inpc_c may be the output of the dimshuffle.
In that case, its shape would be (0, 40, 5), but the original variable would be
(5, 0, 40).
> 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]))
> > >
> > > --
> > >
> > > ---
> > > You received this message because you are subscribed to the Google
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> >
> >
> >
> > --
> > Pascal
> >
>
> --
>
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