问题
I know there are a bunch of questions on this problem and I have read some of those but none of them worked for me.
I am trying to build a model with the following architecture:
The code is as follows:
token_inputs = Input((32,), dtype=tf.int32, name='input_ids')
mask_inputs = Input((32,), dtype=tf.int32, name='attention_mask')
seg_inputs = Input((32,), dtype=tf.int32, 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, mask_inputs, seg_inputs], out)
model.compile(optimizer='rmsprop', loss=crf_loss, metrics=[crf_viterbi_accuracy])
Whenever I am trying to fit the model, I am getting the following error:
---------------------------------------------------------------------------
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, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **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, *args, **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, *args, **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, *args, **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, args, 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, args, 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, args, kwargs, 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, args, kwargs, 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'
All of my imports are as follows:
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
Can you please help me?
来源：https://stackoverflow.com/questions/64320212/attributeerror-tensor-object-has-no-attribute-keras-history-using-crf