AttributeError：使用CRF，“ Tensor”对象没有属性“ _keras_history”

如何解决AttributeError：使用CRF，“ Tensor”对象没有属性“ _keras_history”

我知道关于这个问题有很多问题，我已经阅读了其中一些，但是没有一个对我有用。

我正在尝试使用以下架构构建模型：

代码如下：

token_inputs = Input((32,),dtype=tf.int32,name='input_ids')
mask_inputs = Input((32,name='attention_mask')
seg_inputs = Input((32,name='token_type_ids')

seq_out,_ = bert_model([token_inputs,mask_inputs,seg_inputs])
bd = Bidirectional(LSTM(units=50,return_sequences=True,recurrent_dropout=0.1))(seq_out)
td = TimeDistributed(Dense(50,activation="relu"))(bd)
crf = CRF(n_tags)
out = crf(td)

model = Model([token_inputs,seg_inputs],out)
model.compile(optimizer='rmsprop',loss=crf_loss,metrics=[crf_viterbi_accuracy])

每当我尝试拟合模型时，都会出现以下错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-158-46bb02fcb4e2> in <module>
----> 1 history = model.fit(train_ds,epochs = 3,validation_data = val_ds)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self,*args,**kwargs)
    106   def _method_wrapper(self,**kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self,**kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self,x,y,batch_size,epochs,verbose,callbacks,validation_split,validation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,validation_steps,validation_batch_size,validation_freq,max_queue_size,workers,use_multiprocessing)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self,**kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args,**kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self,**kwds)
    812       # In this case we have not created variables on the first call. So we can
    813       # run the first trace but we should fail if variables are created.
--> 814       results = self._stateful_fn(*args,**kwds)
    815       if self._created_variables:
    816         raise ValueError("Creating variables on a non-first call to a function"

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self,**kwargs)
   2826     """Calls a graph function specialized to the inputs."""
   2827     with self._lock:
-> 2828       graph_function,args,kwargs = self._maybe_define_function(args,kwargs)
   2829     return graph_function._filtered_call(args,kwargs)  # pylint: disable=protected-access
   2830 

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self,kwargs)
   3208           and self.input_signature is None
   3209           and call_context_key in self._function_cache.missed):
-> 3210         return self._define_function_with_shape_relaxation(args,kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _define_function_with_shape_relaxation(self,kwargs)
   3140 
   3141     graph_function = self._create_graph_function(
-> 3142         args,kwargs,override_flat_arg_shapes=relaxed_arg_shapes)
   3143     self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
   3144 

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self,override_flat_arg_shapes)
   3073             arg_names=arg_names,3074             override_flat_arg_shapes=override_flat_arg_shapes,-> 3075             capture_by_value=self._capture_by_value),3076         self._function_attributes,3077         function_spec=self.function_spec,/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name,python_func,signature,func_graph,autograph,autograph_options,add_control_dependencies,arg_names,op_return_value,collections,capture_by_value,override_flat_arg_shapes)
    984         _,original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args,**func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors,CompositeTensors,/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args,**kwds)
    598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args,**kwds)
    601     weak_wrapped_fn = weakref.ref(wrapped_fn)
    602 

/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args,**kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e,"ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

AttributeError: in user code:

    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self,iterator)
    /opt/conda/lib/python3.7/site-packages/keras_contrib/losses/crf_losses.py:54 crf_loss  *
        crf,idx = y_pred._keras_history[:2]

    AttributeError: 'Tensor' object has no attribute '_keras_history'

我所有的进口商品如下：

import numpy as np # linear algebra
import pandas as pd 
import tensorflow as tf
import matplotlib.pyplot as plt

from transformers import BertTokenizer,TFBertModel,BertConfig

# for building the model
import tensorflow as tf
from keras.layers import Dense,Input,Dropout,GlobalAveragePooling1D,LSTM,TimeDistributed,Bidirectional
from keras.models import Model
from keras.callbacks import EarlyStopping

from keras.utils import to_categorical
from sklearn.model_selection import train_test_split

from keras_contrib.layers import CRF
from keras_contrib.losses import crf_loss
from keras_contrib.metrics import crf_viterbi_accuracy

你能帮我吗？