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При использовании TF 2.0 Alpha и преобразовании рабочей модели TF Keras в TF estimator я столкнулся со странной ошибкой.

Приведенный ниже код взят из Tensorflow без прогнозирования временных рядов PHD — RNN без каких-либо изменений.

Когда я запускаю model.fit() для результата из model_fn_keras(), он работает абсолютно нормально, но при преобразовании его в TF estimator происходит сбой.

Это также поднимается на доске проблем с тензорным потоком, но они, похоже, не рассматривают это как ошибку — https://github.com/tensorflow/tensorflow/issues/27750

 def compile_keras_sequential_model(list_of_layers, msg):

    # a tf.keras.Sequential model is a sequence of layers
    model = tf.keras.Sequential(list_of_layers)

    # keras does not have a pre-defined metric for Root Mean Square Error. Let's define one.
    def rmse(y_true, y_pred): # Root Mean Squared Error
      return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))

    print('nModel ', msg)

    #Optimizer
    sgd = tf.keras.optimizers.SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)

    # to finalize the model, specify the loss, the optimizer and metrics
    model.compile(
       loss = 'mean_squared_error',
       optimizer = sgd,
#         optimizer=tf.keras.optimizers.SGD(lr=0.0001, momentum=0.9),
       metrics = [rmse])

    # this prints a description of the model
    model.summary()

    return model
#Create Keras model
def model_fn_keras():

    # RNN model (RMSE: 0.164 after 10 epochs)
    model_layers_RNN = [
        l.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for RNN model
        l.GRU(RNN_CELLSIZE, return_sequences=True),  # output shape [BATCHSIZE, SEQLEN, RNN_CELLSIZE]
        l.GRU(RNN_CELLSIZE), # keep only last output in sequence: output shape [BATCHSIZE, RNN_CELLSIZE]
        l.Dense(1) # output shape [BATCHSIZE, 1]
    ]

    model_RNN = compile_keras_sequential_model(model_layers_RNN, "RNN")

    return(model_RNN)

#Convert
estimator = tf.keras.estimator.model_to_estimator(keras_model=model_fn_keras())
  

Выдает ошибки как:

 Model  RNN
Model: "sequential_27"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
reshape_27 (Reshape)         (None, 16, 1)             0         
_________________________________________________________________
unified_gru_57 (UnifiedGRU)  (None, 16, 32)            3360      
_________________________________________________________________
unified_gru_58 (UnifiedGRU)  (None, 32)                6336      
_________________________________________________________________
dense_27 (Dense)             (None, 1)                 33        
=================================================================
Total params: 9,729
Trainable params: 9,729
Non-trainable params: 0
_________________________________________________________________
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-52-05ea50da2f1c> in <module>()
      5 #Convert Keras model to Estimator
      6 # tf.disable_eager_execution()
----> 7 estimator = tf.keras.estimator.model_to_estimator(keras_model=model_fn_keras())
      8 # estimator = model_fn_keras()
      9 

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflowpythonkerasestimator__init__.py in model_to_estimator(keras_model, keras_model_path, custom_objects, model_dir, config)
     71       custom_objects=custom_objects,
     72       model_dir=model_dir,
---> 73       config=config)
     74 
     75 # LINT.ThenChange(//tensorflow_estimator/python/estimator/keras.py)

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflow_estimatorpythonestimatorkeras.py in model_to_estimator(keras_model, keras_model_path, custom_objects, model_dir, config)
    488   if keras_model._is_graph_network:
    489     warm_start_path = _save_first_checkpoint(keras_model, custom_objects,
--> 490                                              config)
    491   elif keras_model.built:
    492     logging.warning('You are creating an Estimator from a Keras model manually '

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflow_estimatorpythonestimatorkeras.py in _save_first_checkpoint(keras_model, custom_objects, config)
    365           # pylint: disable=protected-access
    366           model._make_train_function()
--> 367           K._initialize_variables(sess)
    368           # pylint: enable=protected-access
    369         saver = saver_lib.Saver()

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflowpythonkerasbackend.py in _initialize_variables(session)
    760     # marked as initialized.
    761     is_initialized = session.run(
--> 762         [variables_module.is_variable_initialized(v) for v in candidate_vars])
    763     uninitialized_vars = []
    764     for flag, v in zip(is_initialized, candidate_vars):

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflowpythonclientsession.py in run(self, fetches, feed_dict, options, run_metadata)
    928     try:
    929       result = self._run(None, fetches, feed_dict, options_ptr,
--> 930                          run_metadata_ptr)
    931       if run_metadata:
    932         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflowpythonclientsession.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1151     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1152       results = self._do_run(handle, final_targets, final_fetches,
-> 1153                              feed_dict_tensor, options, run_metadata)
   1154     else:
   1155       results = []

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflowpythonclientsession.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1327     if handle is None:
   1328       return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1329                            run_metadata)
   1330     else:
   1331       return self._do_call(_prun_fn, handle, feeds, fetches)

c:usershrafiqappdatalocalprogramspythonpython35libsite-packagestensorflowpythonclientsession.py in _do_call(self, fn, *args)
   1347           pass
   1348       message = error_interpolation.interpolate(message, self._graph)
-> 1349       raise type(e)(node_def, op, message)
   1350 
   1351   def _extend_graph(self):

InvalidArgumentError: Node 'training/SGD/gradients/unified_gru_58/StatefulPartitionedCall_grad/StatefulPartitionedCall': Connecting to invalid output 4 of source node unified_gru_58/StatefulPartitionedCall which has 4 outputs

  

Как обсуждалось здесь , это действительно ошибка, вызванная комбинацией tf.function в keras layer и v1 tf.session, который используется model_to_estimator

Также вы можете перейти по следующей ссылке, где ошибка была исправлена. Спасибо!

Если вы чувствуете, что ответ был полезным, пожалуйста, поддержите его. Спасибо!