问题描述
我正在尝试重建https://arxiv.org/abs/1709.04250中的模型。
作者将文本分解为发音（像句子一样思考），然后使用双向LSTM组合这些发音，然后再次使用双向LSTM，这一次是在一系列发音表示上，并通过使用CRF层来预测与每个话语相关的标签。
这是模型架构的可视化： enter image description here
这是我的尝试，在Keras中实现，并使用了https://github.com/keras-team/keras-contrib中的CRF层：
embedding_layer = Embedding(len(word2id) + 1,EMbedDING_DIM,weights=[embedding_matrix],input_length=max_nr_words,trainable=False)
crf = CRF(n_tags,sparse_target=True)
utterance_encoder = Sequential()
utterance_encoder.add(embedding_layer)
utterance_encoder.add(Bidirectional(LSTM(256,return_sequences=True)))
#This is not the pooling used in the paper but should only affect performance:
utterance_encoder.add(AveragePooling1D(max_nr_words))
utterance_encoder.add(Flatten())
utterance_encoder.summary()
model = Sequential()
model.add(Timedistributed(utterance_encoder,input_shape = (max_nr_utterances,max_nr_words)))
model.add(Bidirectional(LSTM(256,return_sequences = True)))
model.add(crf)
model.summary()
model.compile(optimizer="adam",loss='categorical_crossentropy',metrics = [crf_viterbi_accuracy])
model.fit(X,y,batch_size = 1)
# Here,X is of shape (51,3391,431) (51 documents featuring (max) 3391 utterances of (max) 431 words (represented by integer IDs)
# y is of shape (51,52) (51 documents featuring 3391 utterances each corresponding to one of 52 labels)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-9bb38cc64dfb> in <module>
22 #model.compile(optimizer="adam",metrics = ["acc"])
23
---> 24 model.fit(X,batch_size = 1)
~/.local/lib/python3.6/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.
~/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self,x,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()
~/.local/lib/python3.6/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()
~/.local/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _call(self,**kwds)
821 # This is the first call of __call__,so we have to initialize.
822 initializers = []
--> 823 self._initialize(args,kwds,add_initializers_to=initializers)
824 finally:
825 # At this point we kNow that the initialization is complete (or less
~/.local/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _initialize(self,args,add_initializers_to)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args,**kwds))
698
699 def invalid_creator_scope(*unused_args,**unused_kwds):
~/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
2853 args,kwargs = None,None
2854 with self._lock:
-> 2855 graph_function,_,_ = self._maybe_define_function(args,kwargs)
2856 return graph_function
2857
~/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self,kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args,kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function,kwargs
~/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self,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,~/.local/lib/python3.6/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,~/.local/lib/python3.6/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
~/.local/lib/python3.6/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
ValueError: in user code:
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self,iterator)
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step,args=(data,))
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn,kwargs)
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args,**kwargs)
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:757 train_step
self.trainable_variables)
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:2737 _minimize
trainable_variables))
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:562 _aggregate_gradients
filtered_grads_and_vars = _filter_grads(grads_and_vars)
/home/jonas/.local/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1271 _filter_grads
([v.name for _,v in grads_and_vars],))
ValueError: No gradients provided for any variable: ['bidirectional_2/forward_lstm_2/lstm_cell_7/kernel:0','bidirectional_2/forward_lstm_2/lstm_cell_7/recurrent_kernel:0','bidirectional_2/forward_lstm_2/lstm_cell_7/bias:0','bidirectional_2/backward_lstm_2/lstm_cell_8/kernel:0','bidirectional_2/backward_lstm_2/lstm_cell_8/recurrent_kernel:0','bidirectional_2/backward_lstm_2/lstm_cell_8/bias:0','bidirectional_3/forward_lstm_3/lstm_cell_10/kernel:0','bidirectional_3/forward_lstm_3/lstm_cell_10/recurrent_kernel:0','bidirectional_3/forward_lstm_3/lstm_cell_10/bias:0','bidirectional_3/backward_lstm_3/lstm_cell_11/kernel:0','bidirectional_3/backward_lstm_3/lstm_cell_11/recurrent_kernel:0','bidirectional_3/backward_lstm_3/lstm_cell_11/bias:0','crf_1/kernel:0','crf_1/chain_kernel:0','crf_1/bias:0','crf_1/left_boundary:0','crf_1/right_boundary:0'].
模型摘要在有帮助的情况下：
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None,431,300) 2867400
_________________________________________________________________
bidirectional_2 (Bidirection (None,512) 1140736
_________________________________________________________________
average_pooling1d_1 (Average (None,1,512) 0
_________________________________________________________________
flatten_1 (Flatten) (None,512) 0
=================================================================
Total params: 4,008,136
Trainable params: 1,140,736
Non-trainable params: 2,867,400
_________________________________________________________________
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
time_distributed_1 (Timedist (None,512) 4008136
_________________________________________________________________
bidirectional_3 (Bidirection (None,512) 1574912
_________________________________________________________________
crf_1 (CRF) (None,52) 29484
=================================================================
Total params: 5,612,532
Trainable params: 2,745,132
Non-trainable params: 2,400
________________________________________________________________
如果我将CRF层替换为密集层（仅用于测试），则最终会占用大量内存，并且我无法运行任何batch_sizes> 1（但这是一个单独的问题）。 >
任何建议/其他实现，甚至必要时在PyTorch / Tensorflow中也将受到赞赏。
谢谢
编辑： https://github.com/YanWenqiang/HBLSTM-CRF的特点是原始作者对此模型进行了张量流实现，但并未得到维护，对我来说很麻烦。
解决方法
我最终使用了https://github.com/xuxingya/tf2crf，它得到维护并可以与tf2一起使用。