贝叶斯神经网络

Question

我正在研究贝叶斯神经网络，但有很多问题

我觉得这很难理解

在函数prior和posterior中，我不知道什么是内核大小和偏差大小，它们来自哪里。此外，我也不明白在create_bnn_model函数中如何调用它们。 有人能给我解释一下吗？非常感谢。prior and posterior function

def prior(kernel_size, bias_size, dtype=None):
    n = kernel_size + bias_size
    prior_model = keras.Sequential([
        tfp.layers.DistributionLambda(
            lambda t: tfp.distributions.MultivariateNormalDiag(
                loc=tf.zeros(n), scale_diag=tf.ones(n)
            ))
       ])
   return prior_model

def posterior(kernel_size, bias_size, dtype=None):
    n = kernel_size + bias_size
    posterior_model = keras.Sequential(
        [
            tfp.layers.VariableLayer(
                tfp.layers.MultivariateNormalTriL.params_size(n), dtype=dtype
            ),
            tfp.layers.MultivariateNormalTriL(n),
        ]
    )
    return posterior_model

def create_bnn_model(train_size):
    inputs = create_model_inputs()
    features = keras.layers.concatenate(list(inputs.values()))
    features = layers.BatchNormalization()(features)

    for units in hidden_units:
        features = tfp.layers.DenseVariational(
            units=units,
            make_prior_fn=prior,
            make_posterior_fn=posterior,
            kl_weight=1 / train_size,
            activation="sigmoid",
        )(features)

    outputs = layers.Dense(units=1)(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model