3

keras-tuner で調整された深層学習モデルを初めて取得しようとしています。私のチューニングコードは以下のようになります:

def build_model_test(hp):
    model = models.Sequential()
    model.add(layers.InputLayer(input_shape=(100,28)))
    model.add(layers.Dense(28,activation = 'relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Conv1D(filters=hp.Int(
    'num_filters',
    16, 128,
    step=16
),kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Conv1D(filters=hp.Int(
    'num_filters',
    16, 128,
    step=16
),kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Conv1D(filters=hp.Int(
    'num_filters',
    16, 128,
    step=16
),kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Dense(units=hp.Int('units',min_value=16,max_value=512,step=32,default=128),activation = 'relu'))
    model.add(Dropout(hp.Float('dropout', 0, 0.5, step=0.1, default=0.5)))
    model.add(layers.Dense(1, activation = 'linear'))

    model.compile(
        optimizer='adam',
        loss=['mean_squared_error'],
        metrics=[tf.keras.metrics.RootMeanSquaredError()]
    )
    return model

tuner = RandomSearch(
    build_model_test,
    objective='mean_squared_error',
    max_trials=20,
    executions_per_trial=3,
    directory='my_dir',
    project_name='helloworld')


x_train,x_test=dataframes[0:734,:,:],dataframes[734:1100,:,:]
y_train,y_test=target_fx[0:734,:,:],target_fx[734:1100,:,:]


tuner.search(x_train, y_train,
             epochs=20,
             validation_data=(x_test, y_test))

models = tuner.get_best_models(num_models=1)

しかし、20 番目のエポックが到着するとすぐに、このエラーが出力されます

ValueError                                Traceback (most recent call last)
<ipython-input-59-997de3dfa9e5> in <module>
     52 tuner.search(x_train, y_train,
     53              epochs=20,
---> 54              validation_data=(x_test, y_test))
     55 
     56 models = tuner.get_best_models(num_models=1)

~\Anaconda3\envs\deeplearning\lib\site-packages\kerastuner\engine\base_tuner.py in search(self, *fit_args, **fit_kwargs)
    128 
    129             self.on_trial_begin(trial)
--> 130             self.run_trial(trial, *fit_args, **fit_kwargs)
    131             self.on_trial_end(trial)
    132         self.on_search_end()

~\Anaconda3\envs\deeplearning\lib\site-packages\kerastuner\engine\multi_execution_tuner.py in run_trial(self, trial, *fit_args, **fit_kwargs)
    107             averaged_metrics[metric] = np.mean(execution_values)
    108         self.oracle.update_trial(
--> 109             trial.trial_id, metrics=averaged_metrics, step=self._reported_step)
    110 
    111     def _configure_tensorboard_dir(self, callbacks, trial_id, execution=0):

~\Anaconda3\envs\deeplearning\lib\site-packages\kerastuner\engine\oracle.py in update_trial(self, trial_id, metrics, step)
    182         
    183         trial = self.trials[trial_id]
--> 184         self._check_objective_found(metrics)
    185         for metric_name, metric_value in metrics.items():
    186             if not trial.metrics.exists(metric_name):

~\Anaconda3\envs\deeplearning\lib\site-packages\kerastuner\engine\oracle.py in _check_objective_found(self, metrics)
    351                 'Objective value missing in metrics reported to the '
    352                 'Oracle, expected: {}, found: {}'.format(
--> 353                     objective_names, metrics.keys()))
    354 
    355     def _get_trial_dir(self, trial_id):

ValueError: Objective value missing in metrics reported to the Oracle, expected: ['mean_squared_error'], found: dict_keys(['loss', 'root_mean_squared_error', 'val_loss', 'val_root_mean_squared_error'])

従うべき平均二乗誤差としてモデルに指定したため、取得できません。必要な結果を得るためにどのコマンドを変更すればよいか知っていますか?

また、ケラスチューナーで早期停止を呼び出すことはできますか?

4

2 に答える 2