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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