Model Based Machine Learning Book Chapter 2 Skills example in Pyro- tensor dimension issue

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Hi @jeffmax, I was able to get your snippet running by:

  • applying @jpchen’s suggestion of adding .independent(1)
  • replacing the .apply_() with an affine sum (I’m not sure how the .apply_() worked)
  • constraining the beta parameters to be positive and initializing with smaller values
def complete_model_tensor():
    alpha0 = torch.ones(48) * 2.5
    beta0 = torch.ones(48) * 7.5

    guess_probs = pyro.sample('guess_prob',
                              dist.Beta(alpha0, beta0).independent(1))

    with pyro.plate("participants", 22):
        skills = []
        for i in pyro.plate("skills", 7):
           skills.append(pyro.sample("skill_{}".format(i),
                                     dist.Bernoulli(0.5),
                                     infer={"enumerate": "parallel"}))

        for q in pyro.plate("questions", 48):
            has_skills = reduce(operator.mul,
                                [skills[i] for i in skills_needed[q]]).float()
            prob_correct = has_skills * (1 - prob_mistake) + (1 - has_skills) * guess_probs[q]
            pyro.sample("isCorrect{}".format(q),
                        dist.Bernoulli(prob_correct))

def complete_model_tensor_guide():
    guess_prob_a = pyro.param('guess_prob_a', torch.ones(48) * 4,
                              constraint=constraints.positive)
    guess_prob_b = pyro.param('guess_prob_b', torch.ones(48) * 4,
                              constraint=constraints.positive)
    guess_probs = pyro.sample('guess_prob',
                              dist.Beta(guess_prob_a , guess_prob_b).independent(1))

Does that look correct to you?

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