贝叶斯模型可以使用TensorFlow Probability和Keras进行学习。以下是一个使用TensorFlow Probability和Keras实现贝叶斯神经网络的示例代码：

import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow import keras
from tensorflow.keras import layers

# 构建贝叶斯神经网络模型
def build_bayesian_model(input_shape, num_classes):
    model = keras.Sequential([
        tfp.layers.DenseReparameterization(64, activation='relu', input_shape=input_shape),
        tfp.layers.DenseReparameterization(64, activation='relu'),
        tfp.layers.DenseReparameterization(num_classes, activation='softmax')
    ])
    return model

# 加载数据集
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# 扩展维度
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

# 定义模型参数
input_shape = x_train.shape[1:]
num_classes = 10

# 构建模型
model = build_bayesian_model(input_shape, num_classes)

# 定义损失函数和优化器
loss_fn = keras.losses.SparseCategoricalCrossentropy()
optimizer = keras.optimizers.Adam()

# 编译模型
model.compile(optimizer, loss=loss_fn, metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# 评估模型
model.evaluate(x_test, y_test)

在上述示例中，我们使用tfp.layers.DenseReparameterization替代了常规的Dense层，这使得贝叶斯模型能够在训练过程中估计参数的不确定性。通过使用TensorFlow Probability的相关层，我们可以在Keras模型中构建贝叶斯神经网络，并使用常规的损失函数和优化器进行训练和评估。