Python 错误：值传递给参数'；输入'；数据类型int64不在允许值列表中：float16、bfloat16、float32、float64？

我有这个代码，应用预测时出错

import pandas as pd
import numpy as np
import sklearn

import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.layers import Conv2D,Conv1D, MaxPooling2D,MaxPooling1D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dropout,BatchNormalization

dataset=pd.read_csv("C:/Users/User/Desktop/data.csv",encoding='cp1252')
dataset.shape
#output:(53480, 37)
array = dataset.values
X = array[:,0:36]
Y = array[:,36]
kf = KFold(n_splits=10)
kf.get_n_splits(X)
ACC_array = np.array([])
sensitivity_array = np.array([])
specificity_array = np.array([])
for trainindex, testindex in kf.split(X):
Xtrain, Xtest = X[trainindex], X[testindex]
Ytrain, Ytest = Y[trainindex], Y[testindex]
Xtrain = np.expand_dims(np.random.normal(size=(53480, 36)),axis=-1)

Ytrain = np.random.choice([0,1], size=(53480,10))
n_timesteps, n_features, n_outputs =Xtrain.shape[0], Xtrain.shape[1], Ytrain.shape[1]

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=1, 
activation='relu',input_shape=(n_features,1)))

model.add(Conv1D(filters=64, kernel_size=1, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit network
model.fit(Xtrain, Ytrain, epochs=10, batch_size=128, verbose=1)
model.summary()


#output:
Model: "sequential"
.
.

Predictions = model.predict(Xtest,batch_size =1024)
rounded = [round(x[0]) for x in Predictions]
Y_predection = pd.DataFrame(rounded)
Y_predection = Y_predection.iloc[:, 0]

错误消息：

TypeError                                 Traceback (most recent call last)
<ipython-input-16-67624699b454> in <module>
----> 1 Predictions = model.predict(Xtest,batch_size =1024)
  2 rounded = [round(x[0]) for x in Predictions]
  3 Y_predection = pd.DataFrame(rounded)
  4 Y_predection = Y_predection.iloc[:, 0]
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
907         max_queue_size=max_queue_size,
908         workers=workers,
--> 909         use_multiprocessing=use_multiprocessing)
910 
911   def reset_metrics(self):
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)
460     return self._model_iteration(
461         model, ModeKeys.PREDICT, x=x, batch_size=batch_size, 
verbose=verbose,
--> 462         steps=steps, callbacks=callbacks, **kwargs)
463 
464 
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, **kwargs)
442               mode=mode,
443               training_context=training_context,
--> 444               total_epochs=1)
445           cbks.make_logs(model, epoch_logs, result, mode)
446 
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121         step=step, mode=mode, size=current_batch_size) as batch_logs:
122       try:
--> 123         batch_outs = execution_function(iterator)
124       except (StopIteration, errors.OutOfRangeError):
125         # TODO(kaftan): File bug about tf function and 
errors.OutOfRangeError?
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
 84     # `numpy` translates Tensors to values in Eager mode.
 85     return nest.map_structure(_non_none_constant_value,
---> 86                               distributed_function(input_fn))
 87 
 88   return execution_function
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
455 
456     tracing_count = self._get_tracing_count()
--> 457     result = self._call(*args, **kwds)
458     if tracing_count == self._get_tracing_count():
459       self._call_counter.called_without_tracing()
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
501       # This is the first call of __call__, so we have to initialize.
502       initializer_map = object_identity.ObjectIdentityDictionary()
--> 503       self._initialize(args, kwds, add_initializers_to=initializer_map)
504     finally:
505       # At this point we know that the initialization is complete (or 
less

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
406     self._concrete_stateful_fn = (
407         
self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 408             *args, **kwds))
409 
410     def invalid_creator_scope(*unused_args, **unused_kwds):

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846     if self.input_signature:
1847       args, kwargs = None, None
-> 1848     graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849     return graph_function
1850 
~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\eager\function.py in 
_maybe_define_function(self, args, kwargs)
2148         graph_function = self._function_cache.primary.get(cache_key, 
None)
2149         if graph_function is None:
-> 2150           graph_function = self._create_graph_function(args, kwargs)
2151           self._function_cache.primary[cache_key] = graph_function
2152         return graph_function, args, kwargs
~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\eager\function.py in 
_create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039             arg_names=arg_names,
2040             override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041             capture_by_value=self._capture_by_value),
2042         self._function_attributes,
2043         # Tell the ConcreteFunction to clean up its graph once it goes 
out of

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
913                                           converted_func)
914 
--> 915       func_outputs = python_func(*func_args, **func_kwargs)
916 
917       # invariant: `func_outputs` contains only Tensors, 
 CompositeTensors,

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\eager\def_function.py in wrapped_fn(*args, **kwds)
356         # __wrapped__ allows AutoGraph to swap in a converted function. We give
357         # the function a weak reference to itself to avoid a reference 
cycle.
--> 358         return weak_wrapped_fn().__wrapped__(*args, **kwds)
359     weak_wrapped_fn = weakref.ref(wrapped_fn)
360 

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in distributed_function(input_iterator)
 71     strategy = distribution_strategy_context.get_strategy()
 72     outputs = strategy.experimental_run_v2(
---> 73         per_replica_function, args=(model, x, y, sample_weights))
 74     # Out of PerReplica outputs reduce or pick values to return.
 75     all_outputs = dist_utils.unwrap_output_dict(

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
758       fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
759                                 convert_by_default=False)
--> 760       return self._extended.call_for_each_replica(fn, args=args, 
kwargs=kwargs)
761 
762   def reduce(self, reduce_op, value, axis):
~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\distribute\distribute_lib.py in 
call_for_each_replica(self, fn, args, kwargs)
   1785       kwargs = {}
   1786     with self._container_strategy().scope():
 -> 1787       return self._call_for_each_replica(fn, args, kwargs)
   1788 
   1789   def _call_for_each_replica(self, fn, args, kwargs):

    ~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in 
_call_for_each_replica(self, fn, args, kwargs)
2130         self._container_strategy(),
2131         replica_id_in_sync_group=constant_op.constant(0, 
dtypes.int32)):
-> 2132       return fn(*args, **kwargs)
   2133 
   2134   def _reduce_to(self, reduce_op, value, destinations):

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\autograph\impl\api.py in wrapper(*args, **kwargs)
290   def wrapper(*args, **kwargs):
291     with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292       return func(*args, **kwargs)
293 
294   if inspect.isfunction(func) or inspect.ismethod(func):

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in _predict_on_batch(***failed resolving arguments***)
160     def _predict_on_batch(model, x, y=None, sample_weights=None):
161       del y, sample_weights
--> 162       return predict_on_batch(model, x)
163 
164     func = _predict_on_batch

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in predict_on_batch(model, x)
368 
369   with backend.eager_learning_phase_scope(0):
--> 370     return model(inputs)  # pylint: disable=not-callable

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
845                     outputs = base_layer_utils.mark_as_return(outputs, acd)
846                 else:
--> 847                   outputs = call_fn(cast_inputs, *args, **kwargs)
848 
849             except errors.OperatorNotAllowedInGraphError as e:

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in call(self, inputs, training, mask)
254       if not self.built:
255         self._init_graph_network(self.inputs, self.outputs, name=self.name)
--> 256       return super(Sequential, self).call(inputs, training=training, mask=mask)
257 
258     outputs = inputs  # handle the corner case where self.layers is empty

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\network.py in call(self, inputs, training, mask)
706     return self._run_internal_graph(
707         inputs, training=training, mask=mask,
--> 708         convert_kwargs_to_constants=base_layer_utils.call_context().saving)
709 
710   def compute_output_shape(self, input_shape):

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\network.py in _run_internal_graph(self, inputs, training, mask, convert_kwargs_to_constants)
858 
859           # Compute outputs.
--> 860           output_tensors = layer(computed_tensors, **kwargs)
861 
862           # Update tensor_dict.

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
845                     outputs = base_layer_utils.mark_as_return(outputs, acd)
846                 else:
--> 847                   outputs = call_fn(cast_inputs, *args, **kwargs)
848 
849             except errors.OperatorNotAllowedInGraphError as e:

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py in call(self, inputs)
385     if self.padding == 'causal':
386       inputs = array_ops.pad(inputs, self._compute_causal_padding())
--> 387     return super(Conv1D, self).call(inputs)
388 
389 

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py in call(self, inputs)
195 
196   def call(self, inputs):
--> 197     outputs = self._convolution_op(inputs, self.kernel)
198 
199     if self.use_bias:
~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\ops\nn_ops.py in __call__(self, inp, 
filter)
1132           call_from_convolution=False)
1133     else:
-> 1134       return self.conv_op(inp, filter)
1135     # copybara:strip_end
1136     # copybara:insert return self.conv_op(inp, filter)
~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in __call__(self, inp, filter)
637 
638   def __call__(self, inp, filter):  # pylint: disable=redefined- 
builtin
--> 639     return self.call(inp, filter)
640 
641  

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in __call__(self, inp, filter)
236         padding=self.padding,
237         data_format=self.data_format,
--> 238         name=self.name)
239 
240 

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in _conv1d(self, input, filter, strides, padding, data_format, name)
225         padding=padding,
226         data_format=data_format,
--> 227         name=name)
228 
229   # pylint: enable=redefined-builtin

~\.conda\envs\tensorflow\lib\site-packages\tensorflow_core\python\util\deprecation.py in new_func(*args, **kwargs)
572                   func.__module__, arg_name, arg_value, 'in a future version'
573                   if date is None else ('after %s' % date), instructions)
--> 574       return func(*args, **kwargs)
575 
576     doc = _add_deprecated_arg_value_notice_to_docstring(
~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\util\deprecation.py in new_func(*args, 
**kwargs)
572                   func.__module__, arg_name, arg_value, 'in a future 
version'
573                   if date is None else ('after %s' % date), 
instructions)
--> 574       return func(*args, **kwargs)
575 
576     doc = _add_deprecated_arg_value_notice_to_docstring(
~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\ops\nn_ops.py in conv1d(value, filters, 
stride, padding, use_cudnn_on_gpu, data_format, name, input, dilations)
1679         data_format=data_format,
1680         dilations=dilations,
-> 1681         name=name)
1682     return array_ops.squeeze(result, [spatial_start_dim])
1683 

~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\ops\gen_nn_ops.py in conv2d(input, filter, 
strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, 
dilations, name)
1068                   padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu,
1069                   explicit_paddings=explicit_paddings,
-> 1070                   data_format=data_format, dilations=dilations, 
name=name)
1071   _result = _op.outputs[:]
1072   _inputs_flat = _op.inputs

~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\framework\op_def_library.py in 
_apply_op_helper(self, op_type_name, name, **keywords)
629               _SatisfiesTypeConstraint(base_type,
630                                        _Attr(op_def, 
input_arg.type_attr),
--> 631                                        param_name=input_name)
632             attrs[input_arg.type_attr] = attr_value
633             inferred_from[input_arg.type_attr] = input_name

~\.conda\envs\tensorflow\lib\site- 
packages\tensorflow_core\python\framework\op_def_library.py in 
_SatisfiesTypeConstraint(dtype, attr_def, param_name)
 58           "allowed values: %s" %
 59           (param_name, dtypes.as_dtype(dtype).name,
---> 60            ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
 61 
 62 

TypeError: Value passed to parameter 'input' has DataType int64 not in list of allowed values: float16, bfloat16, float32, float64   

TypeError回溯（最近一次调用）
在里面
---->1预测=模型预测（Xtest，批量大小=1024）
2四舍五入=[预测中x的四舍五入（x[0]）]
3 Y_预测=局部数据帧（四舍五入）
4 Y_prediction=Y_prediction.iloc[：，0]
预测中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\keras\engine\training.py（self、x、批处理大小、详细程度、步骤、回调、最大队列大小、工人、使用多处理）
907最大队列大小=最大队列大小，
908名工人=工人，
-->909使用\多处理=使用\多处理）
910
911 def重置_指标（自我）：
predict中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\keras\engine\training\u v2.py（self、model、x、批大小、详细信息、步骤、回调、**kwargs）
460返回自。\模型\迭代(
461模型，ModeKeys.PREDICT，x=x，批量大小=批量大小，
冗长的，
-->462步骤=步骤，回调=回调，**kwargs）
463
464
~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\keras\engine\training\u v2.py in\u model\u迭代（self、model、mode、x、y、批大小、详细信息、样本权重、步骤、回调、**kwargs）
442模式=模式，
443培训上下文=培训上下文，
-->444个总历史记录=1）
445 cbks.生成日志（模型、历元日志、结果、模式）
446
运行一个历元中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\keras\engine\training\u v2.py（模型、迭代器、执行函数、数据集大小、批量大小、策略、每个历元的步骤、样本数、模式、训练上下文、总历元）
121步骤=步骤，模式=模式，大小=当前批次大小）作为批次日志：
请尝试：
-->123批处理输出=执行函数（迭代器）
124除外（StopIteration，errors.OutOfRangeError）：
125#TODO（kaftan）：关于tf函数和
错误。超出范围错误？
~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\keras\engine\training\u v2\u utils.py in execution\u函数（输入\u fn）
84#`numpy`以渴望模式将张量转换为值。
85返回nest.map\u结构（\u非\u无\u常量\u值，
--->86分布函数（输入函数）
87
88返回执行函数
调用中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\eager\def\u function.py（self，*args，**kwds）
455
456跟踪\u计数=自。\u获取\u跟踪\u计数（）
-->457结果=自调用（*args，**kwds）
458如果跟踪计数==self.\u获取跟踪计数（）：
459 self.\u调用\u计数器。调用\u而不跟踪（）
调用中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\eager\def\u function.py（self，*args，**kwds）
501#这是u call u的第一个调用，因此我们必须初始化。
502初始值设定项\u map=object\u identity.ObjectIdentityDictionary（）
-->503自我初始化（参数、KWD、添加初始值设定项到=初始值设定项映射）
504最后：
505#此时我们知道初始化已完成（或
较少的
初始化中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\eager\def\u function.py（self、args、kwds、add\u initializers\u to）
406自具体状态=(
407
self._stateful_fn._get_concrete_function_internal_garbage_collected（#pylint:disable=protected access
-->408*args，**科威特第纳尔）
409
410 def无效的创建者范围（*未使用的参数，**未使用的参数）：
收集中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\eager\function.py（self、*args、**kwargs）
1846如果self.input\u签名：
1847 args，kwargs=None，None
->1848图形函数，u，u=self._可能定义函数（args，kwargs）
1849返回图函数
1850
~\.conda\envs\tensorflow\lib\site-
中的packages\tensorflow\u core\python\eager\function.py
_可能定义函数（self、args、kwargs）
2148 graph_function=self._function_cache.primary.get（cache_key，
（无）
2149如果图形_函数为无：
->2150图形函数=自身。创建图形函数（args、kwargs）
2151 self.\u function\u cache.primary[cache\u key]=图形\u函数
2152返回图_函数，args，kwargs
~\.conda\envs\tensorflow\lib\site-
中的packages\tensorflow\u core\python\eager\function.py
_创建图形函数（self、args、kwargs、override\u flat\u arg\u shapes）
2039 arg_name=arg_name，
2040覆盖平面形状=覆盖平面形状，
->2041按值捕获=自身。_按值捕获），
2042自我功能属性，
2043#告诉concrete函数在它运行后清理它的图形
从…里面
来自func-py-func的func-graph中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\framework\func\u graph.py（名称、python\u func、args、kwargs、签名、func-graph、autograph、autograph\u选项、添加控制依赖项、arg\u名称、op\u返回值、集合、按值捕获、覆盖平面arg\u形状）
913（转换函数）
914
-->915 func_outputs=python_func（*func_args，**func_kwargs）
916
917#不变量：`func_outputs`只包含张量，
复合传感器，
包装中的~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\eager\def\u function.py（*args，**kwds）
356#uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu。我们给予
357#函数对自身进行弱引用以避免引用
周期
-->358返回弱_-wrapped_-fn（）
359弱包裹的=weakref.ref（包裹的）
360
~\.conda\envs\tensorflow\lib\site packages\tensorflow\u core\python\keras\engine\training\u v2
Predictions = model.predict(Xtest,batch_size =1024)

Xtrain, Xtest = X[trainindex], X[testindex]

Xtrain = np.expand_dims(np.random.normal(size=(213412, 36)),axis=-1)

model.add(Conv1D(filters=64, kernel_size=1, 
activation='relu',input_shape=(n_features,1)))

Xtest = np.expand_dims(Xtest, axis=-1)

Xtrain = np.expand_dims(np.random.normal(size=(53480, 36)),axis=-1)