PyMC3 supports various Variational Inference techniques,the main entry point is pymc3.fit().but I don’t know how to apply it effectively,and when I tried to use it ,there were the following error: Average Loss = 4.2499e+08: 0%| | 19/10000 [00:02<22:09, 7.51it/s] Traceback (most recent c…

obj_n_mc=100 is probably too many - the default was 1. My suggestion is try with 5 or 10, and also increase the learning_rate in pm.adagrad(learning_rate=0.02). It all feels a bit like “change something and see what happen”, but that is the reality as all the solutions are domain specific, when yo…

Did you have a look at the related discussion here? [image] Frequently Asked Questions Questions Getting NaN or Inf during NUTS or VI (e.g., 'Bad initial energy: inf. The model might be misspecified., NaN occurred in optimization.) [image] NaN occurred…

Thank you,I saw the links you gave,to get the logp of each RV,I used for RV in model.basic_RVs: print(RV.name, RV.logp(model.test_point)) which gives: sigma_w_log__ -1.3068528194400542 rho 3.6862316527834187 omega 23.716498079566385 mu1 3.6862316527834187 xi_log__ 0.2240229904027684 b 276.729…

FYI, you can check the logp easier now if you are on master branch: model.check_test_point() Usually, if you get NaN occurred in optimization after the optimization started, there are some invalid parameters in your model, typically things like negative sigma for Normal distribution, negative alph…

Thank you very much for your advice! my code is as follows: import numpy as np import pymc3 as pm from theano import tensor as tt N = 200 # the number of observed individuals n= 11 # each individual was observed 5 times e=np.ones(n) mu0 = np.linspace(0.0, 0.0, num=n) # simulate obs…

The problem is rho, as it must be bounded within [0, 1] for the approximation to valid. You can try: BoundNormal = pm.Bound(pm.Normal, lower=0., upper=1.) with pm.Model() as model: sigma_w = pm.InverseGamma('sigma_w', 1.0, 0.1) rho = BoundNormal('rho', mu=0.4, sd=0.01) ... Which bounde…

That’s great :tada: , NaN no longer occurs in optimization,your advice has enabled my code to work properly.I uesd with model: mean_field=pm.fit(method='advi',callbacks=[pm.callbacks.CheckParametersConvergence(diff='absolute',tolerance=2e-4)]) which gives: Average Loss = 1.492e+06: 100%|█████…

Check the ELBO history: plt.plot(mean_field.hist) The convergent checks we currently have are only heuristics - you should train your model much longer and check your result extensively. For example, have a look at this discussion here: Poor Accuracy of ADVI for Linear Regression

Thank you,according to your suggestion, I increased the training time and checked the ELBO history,and the result as follows: with model: mean_field=pm.fit(n=50000,method='advi',callbacks=[pm.callbacks.CheckParametersConvergence(diff='absolute',tolerance=2e-4)]) plt.plot(mean_field.hist) g…

The scale of the yaxis of the ELBO plot is rather big. Try plotting the [10000:] iteration - if it looks rather flat then further training probably wont help.

According to your suggestion, I modified the code： with model: mean_field=pm.fit(n=20000,method='advi',callbacks=[pm.callbacks.CheckParametersConvergence(diff='absolute',tolerance=2e-4)]) plt.figure(figsize=(10, 6)) plt.subplot(221) plt.plot(mean_field.hist) plt.subplot(222) …

How to use pymc3.fit() method

Questions

junpenglao May 3, 2018, 4:52am 2

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How to use pymc3.fit() method

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