df24b86d68db0a0f4f6947b806e7a0facc47c2b8,pymc3/variational/advi.py,,run_adagrad,#Any#Any#Any#Any#Any#Any#Any#,213

Before Change

            print("Iteration {0} [{1}%]: ELBO = {2}".format(i, 100*i//n, e.round(2)))
    
    if verbose:
        print("Finished [100%]: ELBO = {}".format(elbos[-1].round(2)))
    return uw_i, elbos

def variational_gradient_estimate(
    vars, model, minibatch_RVs=[], minibatch_tensors=[], total_size=None, 

After Change

        uw_i, g, e = f()
        elbos[i] = e
        if verbose and not i % (n//10):
            if not i:
                print("Iteration {0} [{1}%]: ELBO = {2}".format(i, 100*i//n, e.round(2)))
            else:
                avg_elbo = elbos[i-n//10:i].mean()
                print("Iteration {0} [{1}%]: Average ELBO = {2}".format(i, 100*i//n, avg_elbo.round(2)))
    
    if verbose:
        avg_elbo = elbos[-n//10:].mean()
        print("Finished [100%]: Average ELBO = {}".format(avg_elbo.round(2)))
    return uw_i, elbos

Instances