Блог

Я использую различные архитектуры LSTM в сочетании с kerastuner.

Этот:

 def tf_encode_decode_lstm_multivariate(  parameter_optimization: kt.HyperParameters, ) -gt; tf.keras.Model:  """  This LSTM model based on the work from this website:  https://machinelearningmastery.com/  how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/   Args:  parameter_optimization: is used within keras tuner for storing hyper-parameters   Returns:  A compiled LSTM keras Model  """  model = tf.keras.Sequential()   model.add(  tf.keras.layers.LSTM(  input_shape=(  parameter_optimization.get(TUNER_KEY_LSTM_SEQUENCE_LENGTH),  parameter_optimization.get(TUNER_KEY_INPUT_SHAPE),  ),  units=parameter_optimization.get(TUNER_KEY_FIRST_NEURON),  activation=parameter_optimization.get(TUNER_KEY_ACTIVATION_FUNCTION),  )  )  model.add(  tf.keras.layers.RepeatVector((parameter_optimization.get("output_shape")))  )  model.add(  tf.keras.layers.LSTM(  units=parameter_optimization.get(TUNER_KEY_SECOND_NEURON),  activation=parameter_optimization.get(TUNER_KEY_ACTIVATION_FUNCTION_2),  return_sequences=True,  )  )   model.add(  tf.keras.layers.TimeDistributed(  tf.keras.layers.Dense(  100,  activation=parameter_optimization.get(TUNER_KEY_ACTIVATION_FUNCTION),  )  )  )  model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(units=1)))   model.compile(  optimizer=TF_OPTIMIZER_DICT[parameter_optimization.get(TUNER_KEY_OPTIMIZER)](  parameter_optimization.get(TUNER_KEY_LEARNING_RATE)  ),  loss=TF_LOSS_FUNCTION_DICT[parameter_optimization.get(TUNER_KEY_LOSS)](),  metrics=TF_VALIDATION_METRICS_DICTS[  parameter_optimization.get(TUNER_KEY_VALIDATION_METRIC)  ](name=parameter_optimization.get(TUNER_KEY_VALIDATION_METRIC)),  )   return model   

Работает правильно, но для того, у кого есть двунаправленный слой:

 def tf_bidirectional_lstm_multivariate(  parameter_optimization: kt.HyperParameters, ) -gt; tf.keras.Model:  """  This function creates a bidirectional LSTM model to adjust hyper-parameters with kerastuner.  Hyper-parameter settings can be adjusted in model_config: first_neuron, second_neuron,  layer_activation_functions, loss_function, learning_rate  Args:  parameter_optimization: is used within keras tuner for storing hyper-parameters  Returns:  A compiled bidirectional LSTM neural net keras Model  """  model = tf.keras.Sequential()   model.add(  tf.keras.layers.Bidirectional(  tf.keras.layers.LSTM(  input_shape=(  parameter_optimization.get(TUNER_KEY_LSTM_SEQUENCE_LENGTH),  parameter_optimization.get(TUNER_KEY_INPUT_SHAPE),  ),  units=parameter_optimization.get(TUNER_KEY_FIRST_NEURON),  activation=parameter_optimization.get(TUNER_KEY_ACTIVATION_FUNCTION),  )  )  )   model.add(  tf.keras.layers.RepeatVector((parameter_optimization.get("output_shape")))  )  model.add(  tf.keras.layers.Bidirectional(  tf.keras.layers.LSTM(  units=parameter_optimization.get(TUNER_KEY_SECOND_NEURON),  activation=parameter_optimization.get(TUNER_KEY_ACTIVATION_FUNCTION_2),  return_sequences=True,  )  )  )   model.add(  tf.keras.layers.TimeDistributed(  tf.keras.layers.Dense(  100,  activation=parameter_optimization.get(TUNER_KEY_ACTIVATION_FUNCTION),  )  )  )  model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(units=1)))   model.compile(  optimizer=TF_OPTIMIZER_DICT[parameter_optimization.get(TUNER_KEY_OPTIMIZER)](  parameter_optimization.get(TUNER_KEY_LEARNING_RATE)  ),  loss=TF_LOSS_FUNCTION_DICT[parameter_optimization.get(TUNER_KEY_LOSS)](),  metrics=TF_VALIDATION_METRICS_DICTS[  parameter_optimization.get(TUNER_KEY_VALIDATION_METRIC)  ](name=parameter_optimization.get(TUNER_KEY_VALIDATION_METRIC)),  )   return model  

I receive the following ValueError when i try to save the model as h5 data after training:

 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options, save_traces)  1999 """  2000 # pylint: enable=line-too-long -gt; 2001 save.save_model(self, filepath, overwrite, include_optimizer, save_format,  2002 signatures, options, save_traces)  2003   /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options, save_traces)  151 'to the Tensorflow SavedModel format (by setting save_format="tf") '  152 'or using `save_weights`.') --gt; 153 hdf5_format.save_model_to_hdf5(  154 model, filepath, overwrite, include_optimizer)  155 else:  /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/hdf5_format.py in save_model_to_hdf5(model, filepath, overwrite, include_optimizer)  87 # entities like metrics added using `add_metric` and losses added using  88 # `add_loss.` ---gt; 89 if len(model.weights) != len(model._undeduplicated_weights):  90 logging.warning('Found duplicated `Variable`s in Model's `weights`. '  91 'This is usually caused by `Variable`s being shared by '  /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in weights(self)  2343 A list of variables.  2344 """ -gt; 2345 return self._dedup_weights(self._undeduplicated_weights)  2346   2347 @property  /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in _undeduplicated_weights(self)  2348 def _undeduplicated_weights(self):  2349 """Returns the undeduplicated list of all layer variables/weights.""" -gt; 2350 self._assert_weights_created()  2351 weights = []  2352 for layer in self._layers:  /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/sequential.py in _assert_weights_created(self)  525 # When the graph has not been initialized, use the Model's implementation to  526 # to check if the weights has been created. --gt; 527 super(functional.Functional, self)._assert_weights_created() # pylint: disable=bad-super-call  528   529   /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in _assert_weights_created(self)  2471 # been invoked yet, this will cover both sequential and subclass model.  2472 # Also make sure to exclude Model class itself which has build() defined. -gt; 2473 raise ValueError('Weights for model %s have not yet been created. '  2474 'Weights are created when the Model is first called on '  2475 'inputs or `build()` is called with an `input_shape`.' %  ValueError: Weights for model sequential have not yet been created. Weights are created when the Model is first called on inputs or `build()` is called with an `input_shape`.   

Тем не менее, работа с обученной моделью не является проблемой и все еще работает, просто save_model() шаг не выполняется.

Я использую tensorflow-gpu 2.4.1 и kerastuner 1.0.4

Я надеюсь, что кто-нибудь сможет сказать мне, что я делаю неправильно, используя двунаправленные слои? Большое спасибо за вашу поддержку