def main()

in examples/sparse_gamma_def.py [0:0]

• 34 lines of code
• 14 McCabe index (conditional complexity)

def main(args):
    # load data
    print('loading training data...')
    dataset_directory = get_data_directory(__file__)
    dataset_path = os.path.join(dataset_directory, 'faces_training.csv')
    if not os.path.exists(dataset_path):
        try:
            os.makedirs(dataset_directory)
        except OSError as e:
            if e.errno != errno.EEXIST:
                raise
            pass
        wget.download('https://d2hg8soec8ck9v.cloudfront.net/datasets/faces_training.csv', dataset_path)
    data = torch.tensor(np.loadtxt(dataset_path, delimiter=',')).float()

    sparse_gamma_def = SparseGammaDEF()

    # Due to the special logic in the custom guide (e.g. parameter clipping), the custom guide
    # seems to be more amenable to higher learning rates.
    # Nevertheless, the easy guide performs the best (presumably because of numerical instabilities
    # related to the gamma distribution in the custom guide).
    learning_rate = 0.2 if args.guide in ['auto', 'easy'] else 4.5
    momentum = 0.05 if args.guide in ['auto', 'easy'] else 0.1
    opt = optim.AdagradRMSProp({"eta": learning_rate, "t": momentum})

    # use one of our three different guide types
    if args.guide == 'auto':
        guide = AutoDiagonalNormal(sparse_gamma_def.model, init_loc_fn=init_to_feasible)
    elif args.guide == 'easy':
        guide = MyEasyGuide(sparse_gamma_def.model)
    else:
        guide = sparse_gamma_def.guide

    # this is the svi object we use during training; we use TraceMeanField_ELBO to
    # get analytic KL divergences
    svi = SVI(sparse_gamma_def.model, guide, opt, loss=TraceMeanField_ELBO())

    # we use svi_eval during evaluation; since we took care to write down our model in
    # a fully vectorized way, this computation can be done efficiently with large tensor ops
    svi_eval = SVI(sparse_gamma_def.model, guide, opt,
                   loss=TraceMeanField_ELBO(num_particles=args.eval_particles, vectorize_particles=True))

    print('\nbeginning training with %s guide...' % args.guide)

    # the training loop
    for k in range(args.num_epochs):
        loss = svi.step(data)
        # for the custom guide we clip parameters after each gradient step
        if args.guide == 'custom':
            clip_params()

        if k % args.eval_frequency == 0 and k > 0 or k == args.num_epochs - 1:
            loss = svi_eval.evaluate_loss(data)
            print("[epoch %04d] training elbo: %.4g" % (k, -loss))