regularized

What is regularization loss in tensorflow?

由 余生长醉 提交于 2019-12-22 04:24:09
问题 When training an Object Detection DNN with Tensorflows Object Detection API it's Visualization Plattform Tensorboard plots a scalar named regularization_loss_1 What is this? I know what regularization is (to make the Network good at generalizing through various methods like dropout) But it is not clear to me what this displayed loss could be. Thanks! 回答1: TL;DR : it's just the additional loss generated by the regularization function. Add that to the network's loss and optimize over the sum of

Improving a badly conditioned matrix

由 瘦欲@ 提交于 2019-12-18 11:19:48
问题 I have a badly conditioned matrix, whose rcond() is close to zero, and therefore, the inverse of that matrix does not come out to be correct. I have tried using pinv() but that does not solve the problem. This is how I am taking the inverse: X = (A)\(b); I looked up for a solution to this problem and found this link (last solution) for improving the matrix. The solution there suggests to use this: A_new = A_old + c*eye(size(A_old)); Where c > 0 . So far employing this technique works in

RNN Regularization: Which Component to Regularize?

由 Deadly 提交于 2019-12-18 05:54:41
问题 I am building an RNN for classification (there is a softmax layer after the RNN). There are so many options for what to regularize and I am not sure if to just try all of them, would the effect be the same? which components do I regularize for what situation? The components being: Kernel weights (layer input) Recurrent weights Bias Activation function (layer output) 回答1: Regularizers that'll work best will depend on your specific architecture, data, and problem; as usual, there isn't a single

Reducing (Versus Delaying) Overfitting in Neural Network

由 别来无恙 提交于 2019-12-14 04:04:15
问题 In neural nets, regularization (e.g. L2, dropout) is commonly used to reduce overfitting. For example, the plot below shows typical loss vs epoch, with and without dropout. Solid lines = Train, dashed = Validation, blue = baseline (no dropout), orange = with dropout. Plot courtesy of Tensorflow tutorials. Weight regularization behaves similarly. Regularization delays the epoch at which validation loss starts to increase, but regularization apparently does not decrease the minimum value of

How to regularize the intercept with glmnet

由 南楼画角 提交于 2019-12-13 03:44:52
问题 I know that glmnet does not regularize the intercept by default, but I would like to do it anyway. I was taking a look at this question and tried to do what whuber suggested (adding a constant variable and turning the parameter intercept to FALSE ) , but as a result glmnet is not fitting the added constant as well. library(dplyr) library(glmnet) X <- mtcars %>% mutate(intercept = 1) %>% select(-c(mpg)) %>% as.matrix() y <- mtcars %>% select(mpg) %>% as.matrix() model <- glmnet(X, y, intercept

Orthogonal matching pursuit regression - am I using it wrong?

由 别等时光非礼了梦想. 提交于 2019-12-12 12:23:52
问题 I am trying out this method as a regularized regression, as an alternative to lasso and elastic net. I have 40k data points and 40 features. Lasso selects 5 features, and orthogonal matching pursuit selects only 1. What could be causing this? Am I using omp the wrong way? Perhaps it is not meant to be used as a regression. Please let me know if you can thing of anything else I may be doing wrong. 回答1: Orthogonal Matching Pursuit seems a bit broken, or at least very sensitive to input data, as

How to set intercept_scaling in scikit-learn LogisticRegression

由 十年热恋 提交于 2019-12-11 01:19:33
问题 I am using scikit-learn's LogisticRegression object for regularized binary classification. I've read the documentation on intercept_scaling but I don't understand how to choose this value intelligently. The datasets look like this: 10-20 features, 300-500 replicates Highly non-Gaussian, in fact most observations are zeros The output classes are not necessarily equally likely. In some cases they are almost 50/50, in other cases they are more like 90/10. Typically C=0.001 gives good cross

What is regularization loss in tensorflow?

由 半世苍凉 提交于 2019-12-05 02:06:00
When training an Object Detection DNN with Tensorflows Object Detection API it's Visualization Plattform Tensorboard plots a scalar named regularization_loss_1 What is this? I know what regularization is (to make the Network good at generalizing through various methods like dropout) But it is not clear to me what this displayed loss could be. Thanks! TL;DR : it's just the additional loss generated by the regularization function. Add that to the network's loss and optimize over the sum of the two . As you correctly state, regularization methods are used to help an optimization method to

Is it reasonable for l1/l2 regularization to cause all feature weights to be zero in vowpal wabbit?

由 江枫思渺然 提交于 2019-12-04 17:17:38
I got a weird result from vw , which uses online learning scheme for logistic regression. And when I add --l1 or --l2 regularization then I got all predictions at 0.5 (that means all features are 0) Here's my command: vw -d training_data.txt --loss_function logistic -f model_l1 --invert_hash model_readable_l1 --l1 0.05 --link logistic ...and here's learning process info: using l1 regularization = 0.05 final_regressor = model_l1 Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 using no cache Reading datafile = training_data.txt num sources = 1 average since example example

Python pandas time series interpolation and regularization

由 故事扮演 提交于 2019-12-03 08:52:21
问题 I am using Python Pandas for the first time. I have 5-min lag traffic data in csv format: ... 2015-01-04 08:29:05,271238 2015-01-04 08:34:05,329285 2015-01-04 08:39:05,-1 2015-01-04 08:44:05,260260 2015-01-04 08:49:05,263711 ... There are several issues: for some timestamps there's missing data (-1) missing entries (also 2/3 consecutive hours) the frequency of the observations is not exactly 5 minutes, but actually loses some seconds once in a while I would like to obtain a regular time
订阅 regularized