I understand. I sincerely thank you. Sato
2017年8月10日木曜日 7時39分21秒 UTC+9 nouiz: > > This is a bug in one Theano optimization: local_dimshuffle_subtensor > > Thanks for the report. I made an issue so that we don't forget it: > > https://github.com/Theano/Theano/issues/6288 > > Frédéric > > On Wed, Aug 9, 2017 at 4:50 AM 佐藤優 <say...@gmail.com <javascript:>> wrote: > >> I wonder why bellow code is invalid.. >> >> from numpy import * >> import theano.tensor as T >> x = T.dmatrix("x") >> mx = x[...,None,:] >> a = T.ones((1,3)) >> T.grad(mx[...,0].dot(a).sum(), a).eval({x:ones((5,10)).astype(float32)}) >> >> bellow error is emerged. >> >> ---------------------------------------------------------------------------ValueError >> Traceback (most recent call >> last)/home/yu/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py >> in __call__(self, *args, **kwargs) 883 outputs =\--> 884 >> self.fn() if output_subset is None else\ 885 >> self.fn(output_subset=output_subset) >> ValueError: Shape mismatch: A.shape[1] != x.shape[0] >> >> During handling of the above exception, another exception occurred: >> ValueError Traceback (most recent call >> last)<ipython-input-74-52410617594a> in <module>() 3 mx = x[...,None,:] >> 4 a = T.ones((1,3))----> 5 T.grad(mx[...,0].dot(a).sum(), >> a).eval({x:ones((5,10)).astype(float32)}) >> /home/yu/anaconda3/lib/python3.5/site-packages/theano/gof/graph.py in >> eval(self, inputs_to_values) 517 args = [inputs_to_values[param] >> for param in inputs] 518 --> 519 rval = >> self._fn_cache[inputs](*args) 520 521 return rval >> /home/yu/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py >> in __call__(self, *args, **kwargs) 896 >> node=self.fn.nodes[self.fn.position_of_error], 897 >> thunk=thunk,--> 898 storage_map=getattr(self.fn, >> 'storage_map', None)) 899 else: 900 # >> old-style linkers raise their own exceptions >> /home/yu/anaconda3/lib/python3.5/site-packages/theano/gof/link.py in >> raise_with_op(node, thunk, exc_info, storage_map) 323 # extra >> long error message in that case. 324 pass--> 325 >> reraise(exc_type, exc_value, exc_trace) 326 327 >> /home/yu/anaconda3/lib/python3.5/site-packages/six.py in reraise(tp, value, >> tb) 683 value = tp() 684 if value.__traceback__ is >> not tb:--> 685 raise value.with_traceback(tb) 686 >> raise value 687 >> /home/yu/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py >> in __call__(self, *args, **kwargs) 882 try: 883 >> outputs =\--> 884 self.fn() if output_subset is None else\ >> 885 self.fn(output_subset=output_subset) 886 >> except Exception: >> ValueError: Shape mismatch: A.shape[1] != x.shape[0] >> Apply node that caused the error: >> CGemv{inplace}(AllocEmpty{dtype='float64'}.0, TensorConstant{1.0}, >> InplaceDimShuffle{1,0}.0, Rebroadcast{0}.0, TensorConstant{0.0}) >> Toposort index: 7 >> Inputs types: [TensorType(float64, vector), TensorType(float64, scalar), >> TensorType(float64, matrix), TensorType(float64, vector), >> TensorType(float64, scalar)] >> Inputs shapes: [(3,), (), (3, 5), (1,), ()] >> Inputs strides: [(8,), (), (8, 24), (80,), ()] >> Inputs values: [array([ 0.00000000e+000, 4.94065646e-324, >> 9.88131292e-324]), array(1.0), 'not shown', array([ 1.]), array(0.0)] >> Inputs type_num: [12, 12, 12, 12, 12] >> Outputs clients: [[InplaceDimShuffle{x,0}(CGemv{inplace}.0)]] >> >> Debugprint of the apply node: >> CGemv{inplace} [id A] <TensorType(float64, vector)> '' >> |AllocEmpty{dtype='float64'} [id B] <TensorType(float64, vector)> '' >> | |TensorConstant{3} [id C] <TensorType(int64, scalar)> >> |TensorConstant{1.0} [id D] <TensorType(float64, scalar)> >> |InplaceDimShuffle{1,0} [id E] <TensorType(float64, matrix)> '' >> | |Alloc [id F] <TensorType(float64, matrix)> '' >> | |TensorConstant{(1, 1) of 1.0} [id G] <TensorType(float64, (True, >> True))> >> | |Shape_i{0} [id H] <TensorType(int64, scalar)> '' >> | | |x [id I] <TensorType(float64, matrix)> >> | |TensorConstant{3} [id C] <TensorType(int64, scalar)> >> |Rebroadcast{0} [id J] <TensorType(float64, vector)> '' >> | |Subtensor{int8, ::, int64} [id K] <TensorType(float64, (True,))> '' >> | |InplaceDimShuffle{0,x,1} [id L] <TensorType(float64, (False, True, >> False))> '' >> | | |x [id I] <TensorType(float64, matrix)> >> | |Constant{0} [id M] <int8> >> | |Constant{0} [id N] <int64> >> |TensorConstant{0.0} [id O] <TensorType(float64, scalar)> >> >> Storage map footprint: >> - x, Input, Shape: (5, 10), ElemSize: 8 Byte(s), TotalSize: 400 Byte(s) >> - InplaceDimShuffle{0,x,1}.0, Shape: (5, 1, 10), ElemSize: 8 Byte(s), >> TotalSize: 400 Byte(s) >> - Alloc.0, Shape: (5, 3), ElemSize: 8 Byte(s), TotalSize: 120 Byte(s) >> - InplaceDimShuffle{1,0}.0, Shape: (3, 5), ElemSize: 8 Byte(s), TotalSize: >> 120 Byte(s) >> - AllocEmpty{dtype='float64'}.0, Shape: (3,), ElemSize: 8 Byte(s), >> TotalSize: 24 Byte(s) >> - Subtensor{int8, ::, int64}.0, Shape: (1,), ElemSize: 8 Byte(s), >> TotalSize: 8 Byte(s) >> - Shape_i{0}.0, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s) >> - TensorConstant{1.0}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 >> Byte(s) >> - TensorConstant{0.0}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 >> Byte(s) >> - Constant{0}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s) >> - Rebroadcast{0}.0, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s) >> - TensorConstant{3}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s) >> - TensorConstant{(1, 1) of 1.0}, Shape: (1, 1), ElemSize: 8 Byte(s), >> TotalSize: 8 Byte(s) >> - Constant{0}, Shape: (), ElemSize: 1 Byte(s), TotalSize: 1.0 Byte(s) >> TotalSize: 593.0 Byte(s) 0.000 GB >> TotalSize inputs: 441.0 Byte(s) 0.000 GB >> >> HINT: Re-running with most Theano optimization disabled could give you a >> back-trace of when this node was created. This can be done with by setting >> the Theano flag 'optimizer=fast_compile'. If that does not work, Theano >> optimizations can be disabled with 'optimizer=None'. >> >> >> I thought above script includes broadcasted operation was wrong, >> So no broadcasting used before gradient operation as follows, >> >> x = T.tensor3("x") >> mx = x >> a = T.ones((1,3)) >> T.grad(mx[...,0].dot(a).sum(), a).eval({x:ones((5,1,10)).astype(float32)}) >> >> successfully performed and dumped bellow result. >> >> array([[ 5., 5., 5.]], dtype=float32) >> >> >> But why did the former case invalid? >> >> Is the gradient with broadcasting mathmatically invalid? >> >> Why does shape miss much happen on gradient? >> >> >> Could you taught me about above question? >> >> -- >> >> --- >> You received this message because you are subscribed to the Google Groups >> "theano-users" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to theano-users...@googlegroups.com <javascript:>. >> For more options, visit https://groups.google.com/d/optout. >> > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. 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