Hay muchas preguntas y respuestas sobre el mismo problema. Desafortunadamente, no funcionaron para mí.

Mi modelo que tiene como objetivo extraer términos de aspecto de las revisiones tiene la siguiente arquitectura:

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 75)]              0         
_________________________________________________________________
embedding (Embedding)        (None, 75, 300)           2635800   
_________________________________________________________________
bidirectional (Bidirectional (None, 75, 512)           1140736   
_________________________________________________________________
time_distributed (TimeDistri (None, 75, 50)            25650     
_________________________________________________________________
crf (CRF)                    (None, 75, 4)             228       
=================================================================
Total params: 3,802,414
Trainable params: 3,802,414
Non-trainable params: 0

El código es el siguiente:

#==============Bi-LSTM CRF=============
input = Input(shape=(max_len,))
model = Embedding(input_dim=n_words, 
                  output_dim=300,
                  weights=[embedding_matrix],
                  input_length=max_len, 
                  mask_zero=True)(input)  # 20-dim embedding
model = Bidirectional(LSTM(units=256, return_sequences=True,
                           recurrent_dropout=0.1))(model)  # variational biLSTM
model = TimeDistributed(Dense(50, activation="tanh"))(model)  # a dense layer as suggested by neuralNer
crf = CRF(n_tags)  # CRF layer
out = crf(model)  # output

model = Model(input, out)
model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])
model.summary()

Tengo el siguiente problema al entrenar el modelo:

Epoch 1/8
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-45-95fc1a21681d> in <module>()
      1 history = model.fit(X_train, np.array(y_train), batch_size=32, epochs=8,
----> 2                     validation_split=0.1, verbose=1)

9 frames
/usr/local/lib/python3.6/dist-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)
   1098                 _r=1):
   1099               callbacks.on_train_batch_begin(step)
-> 1100               tmp_logs = self.train_function(iterator)
   1101               if data_handler.should_sync:
   1102                 context.async_wait()

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    826     tracing_count = self.experimental_get_tracing_count()
    827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
    829       compiler = "xla" if self._experimental_compile else "nonXla"
    830       new_tracing_count = self.experimental_get_tracing_count()

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    869       # This is the first call of __call__, so we have to initialize.
    870       initializers = []
--> 871       self._initialize(args, kwds, add_initializers_to=initializers)
    872     finally:
    873       # At this point we know that the initialization is complete (or less

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    724     self._concrete_stateful_fn = (
    725         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 726             *args, **kwds))
    727 
    728     def invalid_creator_scope(*unused_args, **unused_kwds):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2967       args, kwargs = None, None
   2968     with self._lock:
-> 2969       graph_function, _ = self._maybe_define_function(args, kwargs)
   2970     return graph_function
   2971 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3359 
   3360           self._function_cache.missed.add(call_context_key)
-> 3361           graph_function = self._create_graph_function(args, kwargs)
   3362           self._function_cache.primary[cache_key] = graph_function
   3363 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3204             arg_names=arg_names,
   3205             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206             capture_by_value=self._capture_by_value),
   3207         self._function_attributes,
   3208         function_spec=self.function_spec,

/usr/local/lib/python3.6/dist-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)
    988         _, original_func = tf_decorator.unwrap(python_func)
    989 
--> 990       func_outputs = python_func(*func_args, **func_kwargs)
    991 
    992       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    632             xla_context.Exit()
    633         else:
--> 634           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    635         return out
    636 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    975           except Exception as e:  # pylint:disable=broad-except
    976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
    978             else:
    979               raise

AttributeError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-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'

Estoy usando google colab. tensorflow versión 2.4.0

¿Podría ayudarme, por favor?

0
Ali F 23 ene. 2021 a las 07:56

1 respuesta

La mejor respuesta

Resolví este problema instalando tensorflow versión 2.2 y keras versión 2.3.1. Está sucediendo porque keras ahora no es compatible con keras_contrib en la versión más reciente.

0
Pushya shah 5 feb. 2021 a las 04:30