regression_tutorial.py 文件源码

def nnet_dropout(X, Y):
    """Neural net with dropout."""
    lambda_ = 1e-3  # Weight prior
    noise = .5  # Likelihood st. dev.

    net = (
        ab.InputLayer(name="X", n_samples=n_samples_) >>
        ab.DenseMAP(output_dim=40, l2_reg=lambda_, l1_reg=0.) >>
        ab.Activation(tf.tanh) >>
        ab.DropOut(keep_prob=0.9) >>
        ab.DenseMAP(output_dim=20, l2_reg=lambda_, l1_reg=0.) >>
        ab.Activation(tf.tanh) >>
        ab.DropOut(keep_prob=0.95) >>
        ab.DenseMAP(output_dim=10, l2_reg=lambda_, l1_reg=0.) >>
        ab.Activation(tf.tanh) >>
        ab.DenseMAP(output_dim=1, l2_reg=lambda_, l1_reg=0.)
    )

    f, reg = net(X=X)
    lkhood = tf.distributions.Normal(loc=f, scale=noise)
    loss = ab.max_posterior(lkhood, Y, reg)
    return f, loss