[theano-users] Constraints on convolutional filters
I would like to add a constraint on the filters of my convolution operator, such that they'll always be symmetric and positive semi definite along the two trailing (spatial) axes. There are many ways to achieve this, for instance taking the Gaussian Kernels, or building the symmetric Gram Matrices of the filters. To ensure that this conditions is always met throughout the network training, I'm applying this transformation to the filters before every convolution. The filters will then be modified between an update and a forward step ! I am not sure it's a good idea... *My questions :*Will the network be able to learn if the filters are passed through this transformation at each forward step ? Is there another way to constrain the filters of a layer on some condition? -- --- 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.
[theano-users] Meaning of this error message
I don't understand the following error message, any help would be
appreciated !!
*ERROR (theano.gof.opt): Optimization failure due to: constant_foldingERROR
(theano.gof.opt): node: Assert{msg='Theano Assert
failed!'}(TensorConstant{0.0}, TensorConstant{False})ERROR
(theano.gof.opt): TRACEBACK:ERROR (theano.gof.opt): Traceback (most recent
call last): File
"/Users/mcomin/anaconda/lib/python3.5/site-packages/theano/gof/opt.py",
line 1982, in process_nodereplacements = lopt.transform(node) File
"/Users/mcomin/anaconda/lib/python3.5/site-packages/theano/tensor/opt.py",
line 6452, in constant_foldingrequired = thunk() File
"/Users/mcomin/anaconda/lib/python3.5/site-packages/theano/gof/op.py", line
843, in rvalfill_storage() File
"/Users/mcomin/anaconda/lib/python3.5/site-packages/theano/gof/cc.py", line
1698, in __call__reraise(exc_type, exc_value, exc_trace) File
"/Users/mcomin/anaconda/lib/python3.5/site-packages/six.py", line 686, in
reraiseraise valueAssertionError: Theano Assert failed!*
Traceback (most recent call last):
[... I removed the traceback for brevity... ]
*AssertionError: ('The following error happened while compiling the node',
forall_inplace,cpu,scan_fn}(TensorConstant{2}, TensorConstant{[0 1]},
IncSubtensor{InplaceSet;:int64:}.0, IncSubtensor{InplaceSet;:int64:}.0,
IncSubtensor{InplaceSet;:int64:}.0, W, gamma, beta, W, b, W, b, W, b, W, b,
W, b, W, b, W, b, W, b, W, b, noise, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{0,1,x,2}.0, InplaceDimShuffle{x,0,x}.0,
InplaceDimShuffle{x,0,x}.0, Subtensor{::, ::, int64}.0, mean,
Elemwise{mul,no_inplace}.0, Elemwise{inv,no_inplace}.0,
Elemwise{mul,no_inplace}.0), '\n', 'The following error happened while
compiling the node', forall_inplace,cpu,scan_fn}(TensorConstant{2},
Elemwise{Composite{OR(EQ(i0, i1), EQ(i0, i1))}}.0, TensorConstant{[0 1]},
IncSubtensor{InplaceSet;:int64:}.0, IncSubtensor{InplaceSet;:int64:}.0,
IncSubtensor{InplaceSet;:int64:}.0, ScalarFromTensor.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{0,1,x,2}.0,
InplaceDimShuffle{x,0,x}.0, InplaceDimShuffle{x,0,x}.0, Subtensor{::, ::,
int64}.0, Elemwise{mul,no_inplace}.0, Elemwise{Mul}[(0, 1)].0,
Elemwise{Composite{inv(sqrt((i0 + (i1 * i2}}[(0, 2)].0,
Elemwise{mul,no_inplace}.0), '\n', 'Theano Assert failed!')*
Thanks !
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Re: [theano-users] And operator doesn't work with theano logical operators
I clearly haven't searched enough for theano.tensor.and_ ! Thank you for the correction, everything is clear now :) -- --- 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.
[theano-users] Re: Compiled cost function (with theano.function) ?
I would like to add that the cost function is looping over the input tensors. I need these loops to be differentiable, so I've implemented them with scan/map. But in this answer <https://groups.google.com/forum/#!msg/theano-users/uqYtNlpwiHw/k-3QCro0SbkJ> I saw that it is possible to compile a theano function and call it with a Python for loop. So again I don't know what's best.. Should I compile a step of the loop in a theano function, and then loop with Python's for op ? Or should I compile a theano function with the whole cost (where the loops are now done with scan) ? Or should I just compile one theano function, with the network and the cost ? Thank you for your help ! Le mardi 4 juillet 2017 10:20:17 UTC-4, Sym a écrit : > > Hi, > > I have a fairly complex cost function, composed of various pieces that > are looping over the data. > > Should I compile the total cost as a theano function or not ? > Would the network still be able to compute gradients if I do this ? > Would it be faster / slower than compiling everything (network + cost > functions) in one theano function ? > > I am not sure about what would be the benefits of doing this, if there are > any. > -- --- 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.
Re: [theano-users] And operator doesn't work with theano logical operators
I found a solution : The and operator should be implemented with * and the or operator with +, as the python operators do no work properly with theano (as opposed to what they say in the docs). For further readers, the corrected code is : import theano import theano.tensor as T import numpy as np import matplotlib.pyplot as plt r = T.scalar() gate = T.switch( T.ge(r,2.) * T.le(r,3.) , 1., 0.) f = theano.function([r],gate) x = np.arange(0.,4.,0.05,dtype='float32') y = [f(i) for i in x] plt.plot(x,y) Which indeed gives the correct output. -- --- 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.
Re: [theano-users] And operator doesn't work with theano logical operators
I am using theano 0.9.0 and this function does not exist.. I can't find it
in the documentation either !
And in the theano docs it says that :
__{abs,neg,lt,le,gt,ge,invert,and,or,add,sub,mul,div,truediv,floordiv}__
Those elemwise operation are supported via Python syntax.
Le mercredi 28 juin 2017 21:15:37 UTC-4, nouiz a écrit :
>
> Don't use the Python "and" operation. Use theano.tensor.and(a,b) instead.
> I think it will fix your problem.
>
> Le mer. 28 juin 2017 10:26, Sym <mass.c...@gmail.com > a
> écrit :
>
>>
>> I want to build a piecewise function with theano, for instance a function
>> that is nonzero only in the interval [2,3].
>>
>> Here is the minimal code reproducing the error :
>>
>>
>> import theano
>> import theano.tensor as T
>> import numpy as np
>> import matplotlib.pyplot as plt
>>
>> r = T.scalar()
>> gate = T.switch( T.ge(r,2.) and T.le(r,3.) , 1., 0.)
>> f = theano.function([r],gate)
>> x = np.arange(0.,4.,0.05,dtype='float32')
>> y = [f(i) for i in x]
>> plt.plot(x,y)
>>
>>
>>
>> The result is the following : https://i.stack.imgur.com/XMQme.png
>>
>> Which is clearly not correct : only one condition is satisfied here.
>>
>>
>> If I replace T.switch by theano.ifelse.ifelse the result is the same...
>>
>> Is it a known bug, or am I missing something here?
>>
>>
>> Thanks a lot !
>>
>> --
>>
>> ---
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>> "theano-users" group.
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>> email to theano-users...@googlegroups.com .
>> For more options, visit https://groups.google.com/d/optout.
>>
>
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[theano-users] Re: How can I calculate the size of output of convolutional operation in theano?
You should check this out http://deeplearning.net/software/theano_versions/dev/tutorial/conv_arithmetic.html The output size is in general o = (i - r + 2p)/s + 1 where i is the input size, o the output size, r the filter size, p the padding and s the stride of the convolution. This formula holds for every dimension (so for a 2D convolution, if the strides, padding, filters, etc.. are different you can apply this formula separately) Le lundi 26 juin 2017 15:36:21 UTC-4, Sunjeet Jena a écrit : > > Is there any way I can calculate the size of the output after the > Convolution Operation? > -- --- 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.
