双向 LSTM 在 save_model() 上失败
Bidirectional LSTM fails on save_model()
我将不同的 LSTM 架构与 kerastuner 结合使用。
这个：
def tf_encode_decode_lstm_multivariate(
parameter_optimization: kt.HyperParameters,
) -> 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
工作正常，但对于具有双向层的那个：
def tf_bidirectional_lstm_multivariate(
parameter_optimization: kt.HyperParameters,
) -> 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
当我在训练后尝试将模型保存为 h5 数据时收到以下 ValueError：
/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
-> 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`.')
--> 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.`
---> 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 """
-> 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."""
-> 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.
--> 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.
-> 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`.
尽管如此，使用经过训练的模型不是问题并且仍然有效，只是 save_model() 步骤失败了。
我是 运行 tensorflow-gpu 2.4.1 和 kerastuner 1.0.4
我希望有人能告诉我使用双向层做错了什么？
非常感谢您的支持
您必须向第一层提供 input_shape。在您的情况下，这是 tf.keras.layers.Bidirectional() 而 而不是 tf.keras.layers.LSTM.
两个备选方案是：
- 首先用实际数据调用你的模型
- 在调用 model.compile()
之前先调用 model.build(input_shape)
我将不同的 LSTM 架构与 kerastuner 结合使用。
这个：
def tf_encode_decode_lstm_multivariate(
parameter_optimization: kt.HyperParameters,
) -> 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
工作正常，但对于具有双向层的那个：
def tf_bidirectional_lstm_multivariate(
parameter_optimization: kt.HyperParameters,
) -> 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
当我在训练后尝试将模型保存为 h5 数据时收到以下 ValueError：
/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
-> 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`.')
--> 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.`
---> 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 """
-> 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."""
-> 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.
--> 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.
-> 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`.
尽管如此，使用经过训练的模型不是问题并且仍然有效，只是 save_model() 步骤失败了。
我是 运行 tensorflow-gpu 2.4.1 和 kerastuner 1.0.4
我希望有人能告诉我使用双向层做错了什么？ 非常感谢您的支持
您必须向第一层提供 input_shape。在您的情况下，这是 tf.keras.layers.Bidirectional() 而 而不是 tf.keras.layers.LSTM.
两个备选方案是：
- 首先用实际数据调用你的模型
- 在调用 model.compile() 之前先调用 model.build(input_shape)