regression_tutorial.py 文件源码

def nnet_bayesian(X, Y):
    """Bayesian neural net."""
    lambda_ = 1e-1  # Weight prior
    noise = tf.Variable(0.01)  # Likelihood st. dev. initialisation

    net = (
        ab.InputLayer(name="X", n_samples=n_samples_) >>
        ab.DenseVariational(output_dim=20, std=lambda_) >>
        ab.Activation(tf.nn.relu) >>
        ab.DenseVariational(output_dim=7, std=lambda_) >>
        ab.Activation(tf.nn.relu) >>
        ab.DenseVariational(output_dim=5, std=lambda_) >>
        ab.Activation(tf.tanh) >>
        ab.DenseVariational(output_dim=1, std=lambda_)
    )

    f, kl = net(X=X)
    lkhood = tf.distributions.Normal(loc=f, scale=ab.pos(noise))
    loss = ab.elbo(lkhood, Y, N, kl)
    return f, loss