What is the use for average_loss? Would someone please give an example of it or explain it in layman's terms?
You can check in the caffe.proto file. Line 151 in the present version gives the following comment for average_loss:
Display the loss averaged over the last average_loss iterations
Related
I am trying to understand Generalized Simulated Annealing (https://arxiv.org/pdf/cond-mat/9501047.pdf) and Dual Annealing (Dual Annealing) for my own problem. It is calculating (through a graph) some values in the form of a 64 bit number, and my cost/error function is XOR of that calculated 64 bit number and another 64 bit integer. I am trying to apply Generalized SA to optimize my graph structure to get the best results. Any help is highly appreciated. Thank you in advance.
I tried to read the paper mentioned in the question and read ML mastery blot also mentioned in the question.
I have done a multiple regression analysis in lmer.
So I wanted to calculate the confidence interval of the partial regression coefficient using confint(), and when I enter the following code, I get an error "
> confint(H2_FULL, method="Wald")
***Error in diag(vcov(object)) :
long vectors not supported yet: array.c:2192***
" and cannot run it.
Does anyone know how to resolve this error? Please help.
I am a beginner in R. I would appreciate it if you could help me to understand it clearly.
I only need to be able to calculate 95% confidence intervals for the partial regression coefficients of multiple regression analysis(multi-model).
I assume four explanatory variables, which is why I think this error was made.
This error did not occur in the single regression analysis.
My question is related to plotting amplitude spectrum.
Problem 1: (I have solved it) I have to represent the following function as a discrete set of N=100 numbers separated by time increment of 1/N:
e(t) = 3sin2.1t + 2sin1.9t
I did it using stem function in matlab and plotted it.
Problem 2: (I have question about it) The next thing was to repeat the same above all, using dataset of 200 points with time increment of 1/N and 1/2N.
My question is a bit basic but I just want to clear if I am following the right path to solve my problem.
I want to ask that for problem 2, for both 1/N and 1/2N, should I use N=200 (as I believe it is separate problem)?
A few of my mates have suggested using N=100 for 1/N and N=200 for 1/2N.
which one is the right thing?
Any help will be highly appreciated. Thanks
Recently, I have worked on quantization aware training on tf1.x to push the model to Coral Dev Board. However, when I finished training the model, why is my min max of my 2 outputs fake quantization is the same?
Should it be different when one's maximum target is 95 and one is 2pi?
I have figured out the problem. It is the problem when that part of the model is not really trained QAT. This happens for the output node that somehow forgets to QAT when training. The -6 and 6 values come from the default source of the quantization of tf1.x as mention here
To overcome the problem, we should provide some op to trigger the QAT for the output nodes. In my regression case, I add a dummy op: tf.maximum(output,0) in the model to make the node QAT. If your output is strictly between 0-1, applying "sigmoid" activation at output instead of relu can also solve the problems.
I have Two Tensors
I am trying to gather one from each row with the column being specified by these indices. So I am trying to get:
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1]
This is my code for this:
self.manDistMat.gather(1, state.unsqueeze(-1)))
self.manDistMat
being the 16x16 matrix and state.unsqueeze(-1) being the other matrix.
When I try this I get this error.
RuntimeError: index 578437695752307201 is out of bounds for dimension 1 with size 16
What am I doing wrong?
I actually figured out it was cause I was indexing with a uint8 tensor. When I switched it to with .long() it worked. Can anyone explain why it has to be a long tensor?
I encountered the similar problem. It appears to be a bug in pytorch.