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 <[email protected]> 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 [email protected]. > 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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