Python 3.x 在Tensorflow概率回报中具有密度变化层的简单线性回归模型：TypeError:'；非类型'；对象不可调用

这是一次尝试使用Tensforflow概率，更具体地说是密度变化层，但由于某些原因失败了。如何更正代码

x_train = np.linspace(-1, 1, 100)[:, np.newaxis]
y_train = x_train + 0.3*np.random.randn(100)[:, np.newaxis]

def prior(kernel_size, bias_size, dtype = None):
    
    n = kernel_size + bias_size
    
    prior_model = Sequential([
        
        tfpl.DistributionLambda(
        
            lambda t: tfd.MultivariateNormalDiag(loc = tf.zeros(n)  ,  scale_diag = tf.ones(n)
                                                
                                                ))
        
    ])

def posterior(kernel_size, bias_size, dtype = None):
    
    n = kernel_size + bias_size
    
    posterior_model = Sequential([
        
        tfpl.VariableLayer(tfpl.MultivariateNormalTriL.params_size(n)  , dtype = dtype),   # The parameters of the model are declared Variables that are trainable
        
        tfpl.MultivariateNormalTriL(n)  # The posterior function will return to the Variational layer that will call it a MultivariateNormalTril object that will have as many dimensions
                                        # as the parameters of the Variational Dense Layer.  That means that each parameter will be generated by a distinct Normal Gaussian shifted and scaled
                                        # by a mu and sigma learned from the data, independently of all the other weights.  The output of this Variablelayer will become the input to the
                                        # MultivariateNormalTriL object.
                                        # The shape of the VariableLayer object will be defined by the number of parameters needed to create the MultivariateNormalTriL object given
                                        # that it will live in a Space of n dimensions (event_size = n).  This number is returned by the tfpl.MultivariateNormalTriL.params_size(n)
        
        
    ])
    
    return(posterior_model)

model = Sequential([
    
    tfpl.DenseVariational(
    
        input_shape = (1, ),  # The input is of dimensionality 1, a series
        
        units = 1,  # A linear regression is represented by a Dense layer with one single unit
        
        make_prior_fn = prior,  # We pass the function we have defined which returns the prior distribution on the weights
        
        make_posterior_fn = posterior,   # We pass the function we have defined which returns the variational approximation of the posterior distribution on the weights
        
        kl_weight = 1/ x_train.shape[0],  # Tensorflow scales the likelihood loss calculated using the mini-batch to become an unbiased estimator of the true loss but does not do the
                                          # same for the DL divergence loss.  Here we instruct it to do the necessary scaling.
        
        kl_use_exact = True   # Unless there is a closed form equation for the KL divergence in the library of Tensorflow setting True will return error.  By setting False instead
                              # the KL Divergence will be approxiated using Sampling
    
    )
    
])

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-14-e7cf0bfd5902> in <module>
     17                                           # same for the DL divergence loss.  Here we instruct it to do the necessary scaling.
     18 
---> 19         kl_use_exact = True   # Unless there is a closed form equation for the KL divergence in the library of Tensorflow setting True will return error.  By setting False instead
     20                               # the KL Divergence will be approxiated using Sampling
     21 

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\sequential.py in __init__(self, layers, name)
    140         layers = [layers]
    141       for layer in layers:
--> 142         self.add(layer)
    143 
    144   @property

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\sequential.py in add(self, layer)
    204           # and create the node connecting the current layer
    205           # to the input layer we just created.
--> 206           layer(x)
    207           set_inputs = True
    208 

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
    924     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    925       return self._functional_construction_call(inputs, args, kwargs,
--> 926                                                 input_list)
    927 
    928     # Maintains info about the `Layer.call` stack.

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1115           try:
   1116             with ops.enable_auto_cast_variables(self._compute_dtype_object):
-> 1117               outputs = call_fn(cast_inputs, *args, **kwargs)
   1118 
   1119           except errors.OperatorNotAllowedInGraphError as e:

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\autograph\impl\api.py in wrapper(*args, **kwargs)
    253       try:
    254         with conversion_ctx:
--> 255           return converted_call(f, args, kwargs, options=options)
    256       except Exception as e:  # pylint:disable=broad-except
    257         if hasattr(e, 'ag_error_metadata'):

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\autograph\impl\api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
    455   if conversion.is_in_whitelist_cache(f, options):
    456     logging.log(2, 'Whitelisted %s: from cache', f)
--> 457     return _call_unconverted(f, args, kwargs, options, False)
    458 
    459   if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED:

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\autograph\impl\api.py in _call_unconverted(f, args, kwargs, options, update_cache)
    337 
    338   if kwargs is not None:
--> 339     return f(*args, **kwargs)
    340   return f(*args)
    341 

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_probability\python\layers\dense_variational_v2.py in call(self, inputs)
    120 
    121     q = self._posterior(inputs)
--> 122     r = self._prior(inputs)
    123     self.add_loss(self._kl_divergence_fn(q, r))
    124 

TypeError: 'NoneType' object is not callable

x_train=np.linspace（-1,1100）[：，np.newaxis]
y_-train=x_-train+0.3*np.random.randn（100）[：，np.newaxis]
def prior（内核大小、偏差大小、数据类型=无）：
n=内核大小+偏差大小
先验模型=序贯模型([
tfpl.DistributionLambda(
lambda t:tfd.多变量非线性诊断（loc=tf.零（n），scale_diag=tf.一（n）
))
])
def后验（内核大小、偏差大小、数据类型=无）：
n=内核大小+偏差大小
后验模型=序贯模型([
tfpl.VariableLayer（tfpl.multivarianenormaltril.params_size（n），dtype=dtype），模型的参数是可训练的声明变量
tfpl.multivarianentormaltril（n）#后验函数将返回变分层，变分层将称之为具有相同维数的multivarianentormaltril对象
#作为变分稠密层的参数，这意味着每个参数将由一个不同的正态高斯位移和标度产生
#通过从数据中学习mu和sigma，独立于所有其他权重。此可变层的输出将成为
#多变量对象。
#VariableLayer对象的形状将由创建给定多变量对象所需的参数数量定义
#它将存在于n维空间中（event_size=n）。该数字由tfpl.multivarianentormaltril.params_size（n）返回
])
返回（后置模型）
模型=顺序([
去变异(
输入_shape=（1，），#输入为维度1，一个系列
单位=1，#线性回归用一个单位的密集层表示
使_prior_fn=prior，#我们传递我们定义的函数，该函数返回权重的先验分布
使_posterior_fn=posterior，#我们传递我们定义的函数，该函数返回权重上后验分布的变分近似值
kl_weight=1/x_train.shape[0]，#Tensorflow对使用小批量计算的可能性损失进行缩放，以成为真实损失的无偏估计器，但不执行以下操作：
#DL发散损失也是如此。这里我们指示它进行必要的缩放。
kl_使用_exact=True#除非Tensorflow库中存在kl散度的封闭式方程，否则设置True将返回错误。改为设置False
#KL散度将通过采样进行近似
)
])
---------------------------------------------------------------------------
TypeError回溯（最近一次调用上次）
在里面
17#DL发散损失相同。在这里，我们指示它进行必要的缩放。
18
--->19 kl_使用_exact=True#除非Tensorflow库中存在kl散度的封闭式方程，否则设置True将返回错误。改为设置为False
20#KL散度将通过抽样进行近似
21
~\Anaconda3\envs\tf2\lib\site packages\tensorflow\python\training\tracking\base.py in\u method\u包装（self，*args，**kwargs）
455 self._self_setattr_tracking=False#pylint:disable=protected access
456试试：
-->457结果=方法（自身、*args、**kwargs）
458最后：
459 self._self_setattr_tracking=上一个值#pylint:disable=受保护访问
~\Anaconda3\envs\tf2\lib\site packages\tensorflow\python\keras\engine\sequential.py in\uuuuuu init\uuuu（self，layers，name）
140层=[层]
141对于分层：
-->142.添加（层）
143
144@property
~\Anaconda3\envs\tf2\lib\site packages\tensorflow\python\training\tracking\base.py in\u method\u包装（self，*args，**kwargs）
455 self._self_setattr_tracking=False#pylint:disable=protected access
456试试：
-->457结果=方法（自身、*args、**kwargs）
458最后：
459 self._self_setattr_tracking=上一个值#pylint:disable=受保护访问
添加中的~\Anaconda3\envs\tf2\lib\site packages\tensorflow\python\keras\engine\sequential.py（self，layer）
204#并创建连接当前层的节点
205#到我们刚刚创建的输入层。
-->206层（x）
207设置_输入=真
208
~\Anaconda3\envs\tf2\lib\site packages\tensorflow\python\keras\engine\base\u layer.py in\uuuuu调用（self，*args，**kwargs）
924如果处于功能构建模式（自身、输入、参数、kwargs、输入列表）：
925返回自功能构造调用（输入、参数、kwargs、，
-->926输入（U列表）
927
928#维护有关“Layer.call”堆栈的信息。
~\Anaconda3\envs\tf2\lib\site packages\tensorflow\python\keras\engine\base\u layer.py in\u functional\u construction\u call（self、input、args、kwargs、input\u list）
1115尝试：
1116带操作。启用自动转换变量（自计算类型对象）：
->1117输出=呼叫（强制转换）
def prior(kernel_size, bias_size, dtype=None):
    n = kernel_size + bias_size

    prior_model = tf.keras.Sequential([

        tfp.layers.DistributionLambda(

            lambda t: tfd.MultivariateNormalDiag(loc=tf.zeros(n), scale_diag=tf.ones(n)

                                                 ))

    ])
    return (prior_model)