I was understanding the CGAN model given here.
The generative model has symmetric skip connections as it is explained in the paper here. Hence, I understand the lines such as:
d2 = {d2_,e4} - nn.CAddTable(true)
However, instead of doing the same thing after the last deconv layer d6, the following thing is done:
d6 = d61 - nn.Identity()
Can someone please help me understand why nn.Identity() is used here?
nn.Identity() is a module that forwards the input as such. It could be skipped in their code. Nevertheless it seems that they aren't implementing the model they described in figure 3 of their paper. Maybe it performs better without the third skip connection.
nn.Identity() is a placeholder identity operator.
Related
I'm using Z3 with the ml interface. I had created a formula
f(x_i)
that is satisfiable, according to the solver
Solver.mk_simple_solver ctxr.
The problem is: I can get a model, but he find me values only for some variables of the formula, and not all (some of my Model.get_const_interp_er end with a type None)
How can it be possible that the model can give me only a part of the x_ir? In my understanding, if the model work for one of the values, it means that the formula was satisfiable (in my case, it is) and so all the values can be given...
I don't understand something..
Thanks for reading me!
You should always post full examples so people can help with actual coding issues; without seeing your actual code, it's impossible to know what might be the actual reason.
Having said that, this sounds very much like the following question: Why Z3Py does not provide all possible solutions So, perhaps the answer given there will help you.
Long story short: Z3 models will only contain values for variables that matter for the model. For anything that is not explicitly assigned, any value will do. There are ways to get "full" models as explained in that answer of course; which I'm sure is also possible from the ML interface.
I saw how to do this from a DataSet object, and I saw a setLabel method, and I saw a getLabelMaskArrays, but none of these are what I'm looking for.
Am I just blind or is there not a way?
Thanks
Masking is for variable length time series in RNNs. Most of the time you don't need it. Our built in sequence dataset iterators also tend to handle these cases. For more details see our rnn page: https://deeplearning4j.org/usingrnns
Can anyone please explain the logic behind the functions like "tsclean" & "nnetar" of the package "forecast" written by Professor Rob J Hyndman.
I can able to use the tsclean & nnetar.
I hope these are used for the outlier treatment in time series.
tsclean is used for outlier treatment, i.e. it removes outliers & it fills the missing values.
Request:
What is the method or logic behind it, please let me know (any reference material)
Thanks in Advance!!
I'm have to code my own implementation of FREAK descriptor for a homework. I actually read the original paper but there isn't any explanation of how the build the pattern used.
In the OpenCV code is defined the buildPattern() function but it also lack of documentation on how the pattern itself is build.
So my question is, does anybody knows how the pattern is defined and how the parameters (radius, sigmas and coordinates) are selected?
It looks like the exact values aren't important, but Figure 4 shows the rough layout of the 43 receptive fields.
Their exact geometry is defined by the code here: https://github.com/kikohs/freak/blob/master/src/freak.cpp#L212
I have a complex problem, I am getting formulas form the database and I need to evaluate them. I choose to convert them to post fix...and evaluate them the problem is that..
my formulas are like
roundoff(vd,2);
udV=lookup(uv*dse,erd);
ude=if(er>es)?sr:ss;
Can anyone find a solution for these type of conversions and evaluations...
No, not without some more clarification from you. Perhaps you could tell us what sort of technology you are using and what some, at least, of your functions mean. As it stands I recommend that you use Mathematica because it's probably powerful enough to tackle this type of problem. If you don't have access to Mathematica, perhaps you could hook in to Wolfram Alpha for evaluations.