Read this page, there is many tips to help debug that type of error: http://deeplearning.net/software/theano/tutorial/debug_faq.html?highlight=mismatch
Fred On Wed, Jul 20, 2016 at 1:34 AM, Hongxin Shao <[email protected]> wrote: > Hi， I met the same problem. > Have you figured out how to solve it? > > > 在 2015年12月23日星期三 UTC+8上午3:39:52，Hamid Reza Hassanzadeh写道： > >> Hello, >> Thanks, >> No I'm doing regression. >> >> On Monday, December 21, 2015 at 3:12:05 PM UTC-5, nouiz wrote: >>> >>> Hi, >>> >>> you can use the HINT from the error message to find the exact line >>> number where the problem come from. >>> >>> mse is useful when doing regression. so your target should be a vector, >>> with only one value per example. Then the 2 vector will broadcast correctly >>> together. >>> >>> Are you doing classification? If so, I should change your cost. If the >>> class are ordered, then you can use them as a regression and use the mse >>> cost, but you seem to use them as different not ordered class. >>> >>> Fred >>> >>> On Sun, Dec 20, 2015 at 8:41 PM, Hamid Reza Hassanzadeh < >>> [email protected]> wrote: >>> >>>> Hi everyone, >>>> I'm trying to find the weights of a neural network with mean square >>>> error cost using gradient descent. Here is the theano function I create: >>>> train_fn = theano.function( >>>> inputs=[train_index], >>>> outputs=self.finetune_cost, >>>> updates=updates, >>>> givens={ >>>> self.x: dataset_x[train_index], >>>> self.y: dataset_y[train_index] >>>> } >>>> ) >>>> >>>> with self.x = T.matrix('x') and self.y = T.dvector('y') and the >>>> finetune_cost is defined as: >>>> self.finetune_cost = self.regLayer.mean_sq_error(self.y) >>>> >>>> def mean_sq_error(self,y): >>>> return T.mean((self.y_pred-y)**2) >>>> >>>> Now the problem is that when I compile I get the following error: >>>> ValueError: Input dimension mis-match. (input[0].shape[1] = 1, >>>> input[2].shape[1] = 46) >>>> Apply node that caused the error: Elemwise{Composite{((i0 + i1) - >>>> i2)}}[(0, 0)](Dot22.0, InplaceDimShuffle{x,0}.0, InplaceDimShuffle{x,0}.0) >>>> Toposort index: 30 >>>> Inputs types: [TensorType(float64, matrix), TensorType(float64, row), >>>> TensorType(float64, row)] >>>> Inputs shapes: [(46L, 1L), (1L, 1L), (1L, 46L)] >>>> Inputs strides: [(8L, 8L), (8L, 8L), (368L, 8L)] >>>> Inputs values: ['not shown', array([[ 0.]]), 'not shown'] >>>> Outputs clients: [[Elemwise{sqr,no_inplace}(Elemwise{Composite{((i0 + >>>> i1) - i2)}}[(0, 0)].0), Elemwise{Composite{((i0 * i1) / i2)}}[(0, >>>> 1)](TensorConstant{(1L, 1L) of 2.0}, Elemwise{Composite{((i0 + i1) - >>>> i2)}}[(0, 0)].0, Elemwise{mul,no_inplace}.0)]] >>>> >>>> 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'. >>>> HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint >>>> and storage map footprint of this apply node. >>>> >>>> >>>> I guess the problem is that self.y_pred is a column matrix whereas the >>>> y is a dvector which is treated as a row matrix. What should I do? >>>> >>>> >>>> >>>> -- >>>> >>>> --- >>>> 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. > 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. 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.
