[theano-users] why does this gradient is invalid?
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:
ValueErrorTraceback (most recent call
last) in () 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
value687
/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.e+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] ''
|AllocEmpty{dtype='float64'} [id B] ''
| |TensorConstant{3} [id C]
|TensorConstant{1.0} [id D]
|InplaceDimShuffle{1,0} [id E] ''
| |Alloc [id F] ''
| |TensorConstant{(1, 1) of 1.0} [id G]
| |Shape_i{0} [id H] ''
| | |x [id I]
| |TensorConstant{3} [id C]
|Rebroadcast{0} [id J] ''
| |Subtensor{int8, ::, int64} [id K] ''
| |InplaceDimShuffle{0,x,1} [id L] ''
| | |x [id I]
| |Constant{0} [id M]
| |Constant{0} [id N]
|TensorConstant{0.0} [id O]
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
Re: [theano-users] why does this gradient is invalid?
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 > 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:
>> ValueErrorTraceback (most recent call
>> last) in () 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 value687
>> /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.e+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
[theano-users] Differences between theano.shared and numpy.ndarray.
I saw follown defferences: import theano import theano.tensor as T import numpy as np o = np.ones((1,2,3)) o2= np.ones((2,1)) o2_shared = theano.shared(ones((2, 1))) print((o2 + o).shape) print((o2_shared + o).shape) result is (1, 2, 3) [1 2 1] Maybe, broadcasting result is difference. But changing the order of calculation: import theano import theano.tensor as T import numpy as np o = np.ones((1,2,3)) o2= np.ones((2,1)) o2_shared = theano.shared(ones((2, 1))) print((o + o2).shape) print((o + o2_shared).shape.eval()) result is same as follows: (1, 2, 3) [1 2 3] Is this theano.shared bug? -- --- 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+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
