bayesian_regression.py 文件源码

def model(data):
    # Create unit normal priors over the parameters
    mu = Variable(torch.zeros(p, 1)).type_as(data)
    sigma = Variable(torch.ones(p, 1)).type_as(data)
    bias_mu = Variable(torch.zeros(1)).type_as(data)
    bias_sigma = Variable(torch.ones(1)).type_as(data)
    w_prior, b_prior = Normal(mu, sigma), Normal(bias_mu, bias_sigma)
    priors = {'linear.weight': w_prior, 'linear.bias': b_prior}
    # lift module parameters to random variables sampled from the priors
    lifted_module = pyro.random_module("module", regression_model, priors)
    # sample a regressor (which also samples w and b)
    lifted_reg_model = lifted_module()

    with pyro.iarange("map", N, subsample=data):
        x_data = data[:, :-1]
        y_data = data[:, -1]
        # run the regressor forward conditioned on inputs
        prediction_mean = lifted_reg_model(x_data).squeeze()
        pyro.observe("obs", Normal(prediction_mean, Variable(torch.ones(data.size(0))).type_as(data)), y_data.squeeze())