在 keras_tuner 中的 BayesianOptimization 调谐器中设置 objective 函数时遇到问题
Trouble with setting the objective function in BayesianOptimization tuner in keras_tuner
我正在尝试使用 Keras 调谐器的 BayesianOptimization 调谐器在 Keras 中调整 LSTM 模型的超参数。当我实例化调谐器时，我不断收到错误消息，这些消息似乎反对我在 objective 参数中输入的内容。这是我的代码：
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dropout, Dense
from kerastuner.tuners import BayesianOptimization
from kerastuner.engine.hyperparameters import HyperParameters
from kerastuner import Objective
def build_model(hp):
model = Sequential()
model.add(LSTM(hp.Int("input_unit", min_value=20, max_value=100, step=20),
batch_input_shape=(149,1,1), stateful=True, return_sequences=True))
for i in range(hp.Int("n_layers", 1, 2)):
model.add(LSTM(hp.Int(f"lstm_{i}_units", min_value=20, max_value=100, step=20),
stateful=True, return_sequences=True))
model.add(Dropout(hp.Float("Dropout_rate", min_value=0, max_value=0.5, step=0.1)))
model.add(Dense(1))
model.compile(loss="mean_squared_error",
optimizer=hp.Choice("optimizer", values=["rmsprop", "adam"]),
metrics=[keras.metrics.RootMeanSquaredError(name="rmse")])
return model
tuner = BayesianOptimization(build_model,
objective=Objective("root_mean_squared_error", direction="min"),
max_trials=5,
executions_per_trial=1)
tuner.search(X_train, y_train, epochs=30, batch_size=149, shuffle=False, validation_data=(X_test, y_test))
以下是我收到的消息。如您所见，代码似乎至少部分执行了，因为我收到了一条关于到目前为止哪些超参数表现最好的消息，并且我得到了历元的摘要。在那之后，一长串错误消息，最后一条似乎与 objective 参数有关。我在实例化调谐器时指定 objective 函数的方式有问题，还是有其他问题？
INFO:tensorflow:Reloading Oracle from existing project ./untitled_project/oracle.json
INFO:tensorflow:Reloading Tuner from ./untitled_project/tuner0.json
Search: Running Trial #3
Value |Best Value So Far |Hyperparameter
20 |40 |input_layer
1 |0 |middle_layers
0.1 |0.2 |dropout_rate
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.momentum
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.rho
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.bias
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
Epoch 1/30
29/29 [==============================] - 3s 25ms/step - loss: 0.2129 - rmse: 0.4614 - val_loss: 0.2266 - val_rmse: 0.4761
Epoch 2/30
29/29 [==============================] - 0s 8ms/step - loss: 0.1665 - rmse: 0.4080 - val_loss: 0.2074 - val_rmse: 0.4554
Epoch 3/30
29/29 [==============================] - 0s 7ms/step - loss: 0.1618 - rmse: 0.4022 - val_loss: 0.2002 - val_rmse: 0.4474
Epoch 4/30
29/29 [==============================] - 0s 7ms/step - loss: 0.1575 - rmse: 0.3969 - val_loss: 0.1906 - val_rmse: 0.4366
Epoch 5/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1513 - rmse: 0.3890 - val_loss: 0.1788 - val_rmse: 0.4228
Epoch 6/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1421 - rmse: 0.3770 - val_loss: 0.1638 - val_rmse: 0.4047
Epoch 7/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1283 - rmse: 0.3583 - val_loss: 0.1440 - val_rmse: 0.3794
Epoch 8/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1073 - rmse: 0.3275 - val_loss: 0.1161 - val_rmse: 0.3408
Epoch 9/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0773 - rmse: 0.2781 - val_loss: 0.0807 - val_rmse: 0.2841
Epoch 10/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0509 - rmse: 0.2256 - val_loss: 0.0613 - val_rmse: 0.2475
Epoch 11/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0449 - rmse: 0.2119 - val_loss: 0.0594 - val_rmse: 0.2438
Epoch 12/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0440 - rmse: 0.2098 - val_loss: 0.0587 - val_rmse: 0.2423
Epoch 13/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0434 - rmse: 0.2084 - val_loss: 0.0583 - val_rmse: 0.2414
Epoch 14/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0431 - rmse: 0.2076 - val_loss: 0.0580 - val_rmse: 0.2408
Epoch 15/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0429 - rmse: 0.2070 - val_loss: 0.0578 - val_rmse: 0.2405
Epoch 16/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0427 - rmse: 0.2066 - val_loss: 0.0577 - val_rmse: 0.2402
Epoch 17/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0426 - rmse: 0.2064 - val_loss: 0.0576 - val_rmse: 0.2400
Epoch 18/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0425 - rmse: 0.2062 - val_loss: 0.0575 - val_rmse: 0.2398
Epoch 19/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0424 - rmse: 0.2060 - val_loss: 0.0575 - val_rmse: 0.2397
Epoch 20/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0424 - rmse: 0.2059 - val_loss: 0.0574 - val_rmse: 0.2396
Epoch 21/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0423 - rmse: 0.2058 - val_loss: 0.0573 - val_rmse: 0.2395
Epoch 22/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0423 - rmse: 0.2057 - val_loss: 0.0573 - val_rmse: 0.2394
Epoch 23/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0423 - rmse: 0.2056 - val_loss: 0.0572 - val_rmse: 0.2393
Epoch 24/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2055 - val_loss: 0.0572 - val_rmse: 0.2392
Epoch 25/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2055 - val_loss: 0.0572 - val_rmse: 0.2391
Epoch 26/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2054 - val_loss: 0.0571 - val_rmse: 0.2390
Epoch 27/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2054 - val_loss: 0.0571 - val_rmse: 0.2390
Epoch 28/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2053 - val_loss: 0.0571 - val_rmse: 0.2389
Epoch 29/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0421 - rmse: 0.2053 - val_loss: 0.0570 - val_rmse: 0.2388
Epoch 30/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0421 - rmse: 0.2052 - val_loss: 0.0570 - val_rmse: 0.2388
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/tmp/ipykernel_371/3389478107.py in <module>
26 executions_per_trial=1)
27
---> 28 tuner.search(X_train, y_train, epochs=30, batch_size=149, shuffle=False, validation_data=(X_test, y_test))
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
177
178 self.on_trial_begin(trial)
--> 179 results = self.run_trial(trial, *fit_args, **fit_kwargs)
180 # `results` is None indicates user updated oracle in `run_trial()`.
181 if results is None:
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/tuner.py in run_trial(self, trial, *args, **kwargs)
292 callbacks.append(model_checkpoint)
293 copied_kwargs["callbacks"] = callbacks
--> 294 obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
295
296 histories.append(obj_value)
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/tuner.py in _build_and_fit_model(self, trial, *args, **kwargs)
221 model = self._try_build(hp)
222 results = self.hypermodel.fit(hp, model, *args, **kwargs)
--> 223 return tuner_utils.convert_to_metrics_dict(
224 results, self.oracle.objective, "HyperModel.fit()"
225 )
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/tuner_utils.py in convert_to_metrics_dict(results, objective, func_name)
266 best_epoch = 0
267 for epoch, metrics in epoch_metrics.items():
--> 268 objective_value = objective.get_value(metrics)
269 # Support multi-objective.
270 if objective.name not in metrics:
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/objective.py in get_value(self, logs)
53 The objective value.
54 """
---> 55 return logs[self.name]
56
57 def better_than(self, a, b):
KeyError: 'root_mean_squared_error'
如果：
objective=Objective("rmse", direction="min")
我不知道为什么它不起作用。刚才试了一下，修改自己的一个笔记本测试，'rmse'可以
(p.s。不过我更喜欢 Optuna，对于大型数据集，速度要快得多)
以下是一些片段：
self.compile_metrics = ['AUC', 'binary_accuracy', 'Precision', 'Recall',
tf.keras.metrics.RootMeanSquaredError(
name='rmse', dtype=None
)]
self.model.compile(
optimizer = opt,
#loss = 'binary_crossentropy',
loss = 'mean_squared_error',
metrics = self.compile_metrics,
steps_per_execution = self.steps_per_execution,
)
# above are snippets from my_keras_tuner_class
dl = my_keras_tuner_class()
tuner = kt.BayesianOptimization(
dl.model_builder, # the hypermodel, found in custom class keras_tuning
#objective = 'val_binary_accuracy', # objective to optimize
objective = kt.Objective("rmse", direction="min"),
max_trials = nn_trials, # number of trials
distribution_strategy = tpu_strategy,
# this project name (a dir) is created in 'directory', and is used to
# resume tuning if the search is run again
directory = f'{workdir}',
project_name = 'kbayesian',
# if True, overwrite above directory if search is run again - i.e. don't resume
overwrite = True
Trial 2 Complete [00h 00m 16s]
rmse: 0.42514675855636597
Best rmse So Far: 0.42514675855636597
display(model.metrics_names)
-output:
['loss', 'auc', 'binary_accuracy', 'precision', 'recall', 'rmse']
# example trial
9s 453ms/step - loss: 2.5937 - auc: 0.6732 - binary_accuracy: 0.6353 - precision: 0.6174 - recall: 0.6237 - rmse: 0.5435 - val_loss: 1.1624 - val_auc: 0.7197 - val_binary_accuracy: 0.6970 - val_precision: 0.7083 - val_recall: 0.8500 - val_rmse: 0.5045
我正在尝试使用 Keras 调谐器的 BayesianOptimization 调谐器在 Keras 中调整 LSTM 模型的超参数。当我实例化调谐器时，我不断收到错误消息，这些消息似乎反对我在 objective 参数中输入的内容。这是我的代码：
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dropout, Dense
from kerastuner.tuners import BayesianOptimization
from kerastuner.engine.hyperparameters import HyperParameters
from kerastuner import Objective
def build_model(hp):
model = Sequential()
model.add(LSTM(hp.Int("input_unit", min_value=20, max_value=100, step=20),
batch_input_shape=(149,1,1), stateful=True, return_sequences=True))
for i in range(hp.Int("n_layers", 1, 2)):
model.add(LSTM(hp.Int(f"lstm_{i}_units", min_value=20, max_value=100, step=20),
stateful=True, return_sequences=True))
model.add(Dropout(hp.Float("Dropout_rate", min_value=0, max_value=0.5, step=0.1)))
model.add(Dense(1))
model.compile(loss="mean_squared_error",
optimizer=hp.Choice("optimizer", values=["rmsprop", "adam"]),
metrics=[keras.metrics.RootMeanSquaredError(name="rmse")])
return model
tuner = BayesianOptimization(build_model,
objective=Objective("root_mean_squared_error", direction="min"),
max_trials=5,
executions_per_trial=1)
tuner.search(X_train, y_train, epochs=30, batch_size=149, shuffle=False, validation_data=(X_test, y_test))
以下是我收到的消息。如您所见，代码似乎至少部分执行了，因为我收到了一条关于到目前为止哪些超参数表现最好的消息，并且我得到了历元的摘要。在那之后，一长串错误消息，最后一条似乎与 objective 参数有关。我在实例化调谐器时指定 objective 函数的方式有问题，还是有其他问题？
INFO:tensorflow:Reloading Oracle from existing project ./untitled_project/oracle.json
INFO:tensorflow:Reloading Tuner from ./untitled_project/tuner0.json
Search: Running Trial #3
Value |Best Value So Far |Hyperparameter
20 |40 |input_layer
1 |0 |middle_layers
0.1 |0.2 |dropout_rate
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).layer_with_weights-1.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.momentum
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.rho
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.bias
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
Epoch 1/30
29/29 [==============================] - 3s 25ms/step - loss: 0.2129 - rmse: 0.4614 - val_loss: 0.2266 - val_rmse: 0.4761
Epoch 2/30
29/29 [==============================] - 0s 8ms/step - loss: 0.1665 - rmse: 0.4080 - val_loss: 0.2074 - val_rmse: 0.4554
Epoch 3/30
29/29 [==============================] - 0s 7ms/step - loss: 0.1618 - rmse: 0.4022 - val_loss: 0.2002 - val_rmse: 0.4474
Epoch 4/30
29/29 [==============================] - 0s 7ms/step - loss: 0.1575 - rmse: 0.3969 - val_loss: 0.1906 - val_rmse: 0.4366
Epoch 5/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1513 - rmse: 0.3890 - val_loss: 0.1788 - val_rmse: 0.4228
Epoch 6/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1421 - rmse: 0.3770 - val_loss: 0.1638 - val_rmse: 0.4047
Epoch 7/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1283 - rmse: 0.3583 - val_loss: 0.1440 - val_rmse: 0.3794
Epoch 8/30
29/29 [==============================] - 0s 6ms/step - loss: 0.1073 - rmse: 0.3275 - val_loss: 0.1161 - val_rmse: 0.3408
Epoch 9/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0773 - rmse: 0.2781 - val_loss: 0.0807 - val_rmse: 0.2841
Epoch 10/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0509 - rmse: 0.2256 - val_loss: 0.0613 - val_rmse: 0.2475
Epoch 11/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0449 - rmse: 0.2119 - val_loss: 0.0594 - val_rmse: 0.2438
Epoch 12/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0440 - rmse: 0.2098 - val_loss: 0.0587 - val_rmse: 0.2423
Epoch 13/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0434 - rmse: 0.2084 - val_loss: 0.0583 - val_rmse: 0.2414
Epoch 14/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0431 - rmse: 0.2076 - val_loss: 0.0580 - val_rmse: 0.2408
Epoch 15/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0429 - rmse: 0.2070 - val_loss: 0.0578 - val_rmse: 0.2405
Epoch 16/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0427 - rmse: 0.2066 - val_loss: 0.0577 - val_rmse: 0.2402
Epoch 17/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0426 - rmse: 0.2064 - val_loss: 0.0576 - val_rmse: 0.2400
Epoch 18/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0425 - rmse: 0.2062 - val_loss: 0.0575 - val_rmse: 0.2398
Epoch 19/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0424 - rmse: 0.2060 - val_loss: 0.0575 - val_rmse: 0.2397
Epoch 20/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0424 - rmse: 0.2059 - val_loss: 0.0574 - val_rmse: 0.2396
Epoch 21/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0423 - rmse: 0.2058 - val_loss: 0.0573 - val_rmse: 0.2395
Epoch 22/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0423 - rmse: 0.2057 - val_loss: 0.0573 - val_rmse: 0.2394
Epoch 23/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0423 - rmse: 0.2056 - val_loss: 0.0572 - val_rmse: 0.2393
Epoch 24/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2055 - val_loss: 0.0572 - val_rmse: 0.2392
Epoch 25/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2055 - val_loss: 0.0572 - val_rmse: 0.2391
Epoch 26/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2054 - val_loss: 0.0571 - val_rmse: 0.2390
Epoch 27/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2054 - val_loss: 0.0571 - val_rmse: 0.2390
Epoch 28/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0422 - rmse: 0.2053 - val_loss: 0.0571 - val_rmse: 0.2389
Epoch 29/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0421 - rmse: 0.2053 - val_loss: 0.0570 - val_rmse: 0.2388
Epoch 30/30
29/29 [==============================] - 0s 6ms/step - loss: 0.0421 - rmse: 0.2052 - val_loss: 0.0570 - val_rmse: 0.2388
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/tmp/ipykernel_371/3389478107.py in <module>
26 executions_per_trial=1)
27
---> 28 tuner.search(X_train, y_train, epochs=30, batch_size=149, shuffle=False, validation_data=(X_test, y_test))
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
177
178 self.on_trial_begin(trial)
--> 179 results = self.run_trial(trial, *fit_args, **fit_kwargs)
180 # `results` is None indicates user updated oracle in `run_trial()`.
181 if results is None:
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/tuner.py in run_trial(self, trial, *args, **kwargs)
292 callbacks.append(model_checkpoint)
293 copied_kwargs["callbacks"] = callbacks
--> 294 obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
295
296 histories.append(obj_value)
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/tuner.py in _build_and_fit_model(self, trial, *args, **kwargs)
221 model = self._try_build(hp)
222 results = self.hypermodel.fit(hp, model, *args, **kwargs)
--> 223 return tuner_utils.convert_to_metrics_dict(
224 results, self.oracle.objective, "HyperModel.fit()"
225 )
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/tuner_utils.py in convert_to_metrics_dict(results, objective, func_name)
266 best_epoch = 0
267 for epoch, metrics in epoch_metrics.items():
--> 268 objective_value = objective.get_value(metrics)
269 # Support multi-objective.
270 if objective.name not in metrics:
~/comp_4447/dst2_env/lib/python3.9/site-packages/keras_tuner/engine/objective.py in get_value(self, logs)
53 The objective value.
54 """
---> 55 return logs[self.name]
56
57 def better_than(self, a, b):
KeyError: 'root_mean_squared_error'
如果：
objective=Objective("rmse", direction="min")
我不知道为什么它不起作用。刚才试了一下，修改自己的一个笔记本测试，'rmse'可以
(p.s。不过我更喜欢 Optuna，对于大型数据集，速度要快得多)
以下是一些片段：
self.compile_metrics = ['AUC', 'binary_accuracy', 'Precision', 'Recall',
tf.keras.metrics.RootMeanSquaredError(
name='rmse', dtype=None
)]
self.model.compile(
optimizer = opt,
#loss = 'binary_crossentropy',
loss = 'mean_squared_error',
metrics = self.compile_metrics,
steps_per_execution = self.steps_per_execution,
)
# above are snippets from my_keras_tuner_class
dl = my_keras_tuner_class()
tuner = kt.BayesianOptimization(
dl.model_builder, # the hypermodel, found in custom class keras_tuning
#objective = 'val_binary_accuracy', # objective to optimize
objective = kt.Objective("rmse", direction="min"),
max_trials = nn_trials, # number of trials
distribution_strategy = tpu_strategy,
# this project name (a dir) is created in 'directory', and is used to
# resume tuning if the search is run again
directory = f'{workdir}',
project_name = 'kbayesian',
# if True, overwrite above directory if search is run again - i.e. don't resume
overwrite = True
Trial 2 Complete [00h 00m 16s]
rmse: 0.42514675855636597
Best rmse So Far: 0.42514675855636597
display(model.metrics_names)
-output:
['loss', 'auc', 'binary_accuracy', 'precision', 'recall', 'rmse']
# example trial
9s 453ms/step - loss: 2.5937 - auc: 0.6732 - binary_accuracy: 0.6353 - precision: 0.6174 - recall: 0.6237 - rmse: 0.5435 - val_loss: 1.1624 - val_auc: 0.7197 - val_binary_accuracy: 0.6970 - val_precision: 0.7083 - val_recall: 0.8500 - val_rmse: 0.5045