Python ValueError：函数的输入张量必须来自“tf.keras.Input”。接收到：0（缺少上一层元数据）

我正在尝试使用确定点过程进行主动学习，我使用的是包

model

。以下代码是确定点过程的代码：

from scipy.spatial.distance import pdist, squareform
from scipy.linalg import inv, norm, sqrtm
import scipy 

def sample(D, V, E=None, k=None, flag_gpu=False):
    N = D.shape[0]
    if k is None:
        # general dpp
        D = D / (1 + D)
        V = V[:,np.random.rand(N) < D]
        k = V.shape[1]
    else:
        # k-dpp
        v_idx = sample_k(D, E, k, flag_gpu=flag_gpu)
        V = V[:,v_idx]

    rst = list()

    for i in range(k-1,-1,-1):
        # choose indices

        P = np.sum(V**2, axis=1)

        row_idx = np.random.choice(range(N), p=P/np.sum(P))
        col_idx = np.nonzero(V[row_idx])[0][0]

        rst.append(row_idx)

        # update V
        V_j = np.copy(V[:,col_idx])
        V = V - np.outer(V_j, V[row_idx]/V_j[row_idx])
        V[:,col_idx] = V[:,i]
        V = V[:,:i]

        # reorthogonalize
        if i > 0:
            V = sym(V)

    rst = np.sort(rst)

    return rst

def sym(X):
    return X.dot(inv(np.real(sqrtm(X.T.dot(X)))))

def sample_k(D, E, k, flag_gpu=False):
    i = D.shape[0]
    remaining = k
    rst = list()

    while remaining > 0:
        if i == remaining:
            marg = 1.
        else:
            marg = D[i-1] * E[remaining-1, i-1] / E[remaining, i]

        if np.random.rand() < marg:
            rst.append(i-1)
            remaining -= 1
        i -= 1

    return np.array(rst)

def get_eig(L, flag_gpu=False):
    if flag_gpu:
        pass
    else:
        return scipy.linalg.eigh(L)

    
def get_sympoly(D, k, flag_gpu=False):
    N = D.shape[0]
    if flag_gpu:
        pass
    else:
        return np.zeros((k+1, N+1))

我收到的错误消息如下：

ValueError                                Traceback (most recent call last)
<ipython-input-124-de2b1c5b58fb> in <module>
      1 estimator = KerasClassifier(model)
----> 2 DPP_perf_hist_5 = active_learning_procedure(DPP_sampler, X_test, y_test,
      3                                               X_pool, y_pool,
      4                                               X_initial, y_initial, estimator, n_instances=5)

<ipython-input-119-0618cfd433fb> in active_learning_procedure(query_strategy, X_test, y_test, X_pool, y_pool, X_initial, y_initial, estimator, epochs, batch_size, n_queries, n_instances, verbose)
      6     perf_hist = [learner.score(X_test, y_test, verbose=verbose)]
      7     for index in range(n_queries):
----> 8         query_idx, query_instance = learner.query(X_pool, n_instances)
      9         learner.teach(X_pool[query_idx], y_pool[query_idx], epochs=epochs, batch_size=batch_size, verbose=verbose)
     10         X_pool = np.delete(X_pool, query_idx, axis=0)

~\anaconda3\lib\site-packages\modAL\models\base.py in query(self, X_pool, *query_args, **query_kwargs)
    259             labelled upon query synthesis.
    260         """
--> 261         query_result = self.query_strategy(self, X_pool, *query_args, **query_kwargs)
    262 
    263         if isinstance(query_result, tuple):

<ipython-input-117-67ae49971228> in DPP_sampler(learner, X, n_instances, T, nTrn, k)
     81 def DPP_sampler(learner, X, n_instances = 1, T = 100, nTrn = 4000, k=1):
     82     random_subset = np.random.choice(X.shape[0], 2000, replace=False)
---> 83     DPP_output = K.function([learner.estimator.model.layers[0].input, K.learning_phase()],
     84                            [learner.estimator.model.layers[-1].output[0]])
     85     DPP_output2 = K.function([learner.estimator.model.layers[0].input, K.learning_phase()],

~\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py in function(inputs, outputs, updates, name, **kwargs)
   3934     from tensorflow.python.keras import models  # pylint: disable=g-import-not-at-top
   3935     from tensorflow.python.keras.utils import tf_utils  # pylint: disable=g-import-not-at-top
-> 3936     model = models.Model(inputs=inputs, outputs=outputs)
   3937 
   3938     wrap_outputs = isinstance(outputs, list) and len(outputs) == 1

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in __new__(cls, *args, **kwargs)
    240       # Functional model
    241       from tensorflow.python.keras.engine import functional  # pylint: disable=g-import-not-at-top
--> 242       return functional.Functional(*args, **kwargs)
    243     else:
    244       return super(Model, cls).__new__(cls, *args, **kwargs)

~\anaconda3\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\lib\site-packages\tensorflow\python\keras\engine\functional.py in __init__(self, inputs, outputs, name, trainable)
    113     #     'arguments during initialization. Got an unexpected argument:')
    114     super(Functional, self).__init__(name=name, trainable=trainable)
--> 115     self._init_graph_network(inputs, outputs)
    116 
    117   @trackable.no_automatic_dependency_tracking

~\anaconda3\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\lib\site-packages\tensorflow\python\keras\engine\functional.py in _init_graph_network(self, inputs, outputs)
    142       base_layer_utils.create_keras_history(self._nested_outputs)
    143 
--> 144     self._validate_graph_inputs_and_outputs()
    145 
    146     # A Network does not create weights of its own, thus it is already
 ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in _validate_graph_inputs_and_outputs(self)
    634       if not hasattr(x, '_keras_history'):
    635         cls_name = self.__class__.__name__
 raise ValueError('Input tensors to a ' + cls_name + ' ' + 'must come from `tf.keras.Input`. ' 'Received: ' + str(x) +

ValueError: Input tensors to a Functional must come from `tf.keras.Input`. Received: 0 (missing previous layer metadata).

ValueError回溯（最近一次调用）
在里面
1估计器=KerasClassifier（模型）
---->2 DPP_perf_hist_5=主动学习程序（DPP_采样器、X_测试、y_测试、，
3 X_池，y_池，
4 X_初始值，y_初始值，估计量，n_实例=5）
在主动学习过程中（查询策略、X\u测试、y\u测试、X\u池、y\u池、X\u初始、y\u初始、估计器、年代、批量大小、n\u查询、n\u实例、详细）
6 perf_hist=[学习者分数（X_测试，y_测试，verbose=verbose）]
7对于范围内的索引（n_查询）：
---->8 query\u idx，query\u instance=learner.query（X\u池，n\u实例）
9.教学（X_pool[query_idx]，y_pool[query_idx]，epochs=epochs，batch_size=batch_size，verbose=verbose）
10 X_pool=np.delete（X_pool，query_idx，axis=0）
查询中的~\anaconda3\lib\site packages\modAL\models\base.py（self、X\u pool、*query\u args、**query\u kwargs）
259在查询合成时标记。
260         """
-->261 query\u result=self.query\u策略（self、X\u pool、*query\u args、**query\u kwargs）
262
263如果isinstance（查询结果，元组）：
在DPP_采样器中（学习者、X、n_实例、T、nTrn、k）
81 def DPP_采样器（学习者，X，n_实例=1，T=100，nTrn=4000，k=1）：
82 random_subset=np.random.choice（X.shape[0]，2000，replace=False）
--->83 DPP_输出=K.函数（[learner.estimator.model.layers[0]。输入，K.学习阶段（），
84[学习者.估计器.模型.层[-1].输出[0]]
85 DPP_输出2=K.函数（[learner.estimator.model.layers[0]。输入，K.学习阶段（），
函数中的~\anaconda3\lib\site packages\tensorflow\python\keras\backend.py（输入、输出、更新、名称，**kwargs）
3934来自tensorflow.python.keras导入模型#pylint:disable=g-import-not-at-top
3935从tensorflow.python.keras.utils导入tf_utils#pylint:disable=g-import-not-at-top
->3936模型=模型。模型（输入=输入，输出=输出）
3937
3938 wrap_输出=isinstance（输出，列表）和len（输出）=1
~\anaconda3\lib\site packages\tensorflow\python\keras\engine\training.py在新的（cls，*args，**kwargs）
240#功能模型
241来自tensorflow.python.keras.engine import functional#pylint:disable=g-import-not-at-top
-->242返回functional.functional（*args，**kwargs）
243其他：
244返回超级（型号，cls）。\uuuuu新的\uuuuuu（cls，*args，**kwargs）
~\anaconda3\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\lib\site packages\tensorflow\python\keras\engine\functional.py in\uuuuuuu init\uuuuu（self、输入、输出、名称、可培训）
113#'初始化期间的参数。获取意外参数：'）
114超级（功能，自我）。\uuuuu初始（名称=名称，可培训=可培训）
-->115自初始化图网络（输入、输出）
116
117@trackable.no\自动\依赖\跟踪
~\anaconda3\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
_init_graph_网络中的~\anaconda3\lib\site packages\tensorflow\python\keras\engine\functional.py（自身、输入、输出）
142基础层实用程序。创建历史记录（自嵌套输出）
143
-->144自我验证图输入和输出（）
145
146#一个网络本身并不会产生权重，因此它已经存在
~\anaconda3\lib\site packages\tensorflow\python\keras\engine\functional.py在\u validate\u graph\u input\u和\u output（self）中
634如果不是hasattr（x，“\u keras\u history”）：
635 cls_name=self.\u类\u.\u名称__
raise VALUERROR（“+cls_名称+”+”的输入张量必须来自'tf.keras.Input`.”接收：'+str（x）+
ValueError:函数的输入张量必须来自`tf.keras.Input`。收到：0（缺少上一层元数据）。

ValueError                                Traceback (most recent call last)
<ipython-input-124-de2b1c5b58fb> in <module>
      1 estimator = KerasClassifier(model)
----> 2 DPP_perf_hist_5 = active_learning_procedure(DPP_sampler, X_test, y_test,
      3                                               X_pool, y_pool,
      4                                               X_initial, y_initial, estimator, n_instances=5)

<ipython-input-119-0618cfd433fb> in active_learning_procedure(query_strategy, X_test, y_test, X_pool, y_pool, X_initial, y_initial, estimator, epochs, batch_size, n_queries, n_instances, verbose)
      6     perf_hist = [learner.score(X_test, y_test, verbose=verbose)]
      7     for index in range(n_queries):
----> 8         query_idx, query_instance = learner.query(X_pool, n_instances)
      9         learner.teach(X_pool[query_idx], y_pool[query_idx], epochs=epochs, batch_size=batch_size, verbose=verbose)
     10         X_pool = np.delete(X_pool, query_idx, axis=0)

~\anaconda3\lib\site-packages\modAL\models\base.py in query(self, X_pool, *query_args, **query_kwargs)
    259             labelled upon query synthesis.
    260         """
--> 261         query_result = self.query_strategy(self, X_pool, *query_args, **query_kwargs)
    262 
    263         if isinstance(query_result, tuple):

<ipython-input-117-67ae49971228> in DPP_sampler(learner, X, n_instances, T, nTrn, k)
     81 def DPP_sampler(learner, X, n_instances = 1, T = 100, nTrn = 4000, k=1):
     82     random_subset = np.random.choice(X.shape[0], 2000, replace=False)
---> 83     DPP_output = K.function([learner.estimator.model.layers[0].input, K.learning_phase()],
     84                            [learner.estimator.model.layers[-1].output[0]])
     85     DPP_output2 = K.function([learner.estimator.model.layers[0].input, K.learning_phase()],

~\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py in function(inputs, outputs, updates, name, **kwargs)
   3934     from tensorflow.python.keras import models  # pylint: disable=g-import-not-at-top
   3935     from tensorflow.python.keras.utils import tf_utils  # pylint: disable=g-import-not-at-top
-> 3936     model = models.Model(inputs=inputs, outputs=outputs)
   3937 
   3938     wrap_outputs = isinstance(outputs, list) and len(outputs) == 1

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in __new__(cls, *args, **kwargs)
    240       # Functional model
    241       from tensorflow.python.keras.engine import functional  # pylint: disable=g-import-not-at-top
--> 242       return functional.Functional(*args, **kwargs)
    243     else:
    244       return super(Model, cls).__new__(cls, *args, **kwargs)

~\anaconda3\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\lib\site-packages\tensorflow\python\keras\engine\functional.py in __init__(self, inputs, outputs, name, trainable)
    113     #     'arguments during initialization. Got an unexpected argument:')
    114     super(Functional, self).__init__(name=name, trainable=trainable)
--> 115     self._init_graph_network(inputs, outputs)
    116 
    117   @trackable.no_automatic_dependency_tracking

~\anaconda3\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\lib\site-packages\tensorflow\python\keras\engine\functional.py in _init_graph_network(self, inputs, outputs)
    142       base_layer_utils.create_keras_history(self._nested_outputs)
    143 
--> 144     self._validate_graph_inputs_and_outputs()
    145 
    146     # A Network does not create weights of its own, thus it is already
 ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in _validate_graph_inputs_and_outputs(self)
    634       if not hasattr(x, '_keras_history'):
    635         cls_name = self.__class__.__name__
 raise ValueError('Input tensors to a ' + cls_name + ' ' + 'must come from `tf.keras.Input`. ' 'Received: ' + str(x) +

ValueError: Input tensors to a Functional must come from `tf.keras.Input`. Received: 0 (missing previous layer metadata).