我正在使用 Keras Tuner 来寻找神经网络的最佳超参数。
params = ['batch_size', 'epochs', 'drop_rate', 'learning_rate', 'num_layers','units_1','units_2','units_3','units_4', 'units_5']
params_nn = pd.DataFrame(np.zeros((len(params), len(commodities))), index = params, columns = commodities)
def build_model(hp):
drop_rate = hp.Choice('drop_rate', values = [0.0, 0.2, 0.4])
model_full = Sequential()
for i in range(hp.Int('num_layers', 2, 5)):
model_full.add(keras.layers.Dense(units = hp.Int('units_'+str(i+1),
min_value=3,
max_value=24,
step=3), activation="relu",
input_shape=(1, x_nn_train[0].shape[2])))
model_full.add(keras.layers.Dropout(drop_rate))
model_full.add(keras.layers.Dense(1))
model_full.compile(loss='huber_loss',
optimizer = keras.optimizers.Adam(hp.Choice('learning_rate',
values = [1e-2, 1e-3, 1e-4])))
return model_full
bayesian_opt_tuner = MyTuner(
build_model,
objective = 'val_loss',
max_trials=70,
executions_per_trial=3)
bayesian_opt_tuner.search(x_nn_train[0], y_nn_train[0],
validation_split=0.2,verbose=1)
best_params = bayesian_opt_tuner.get_best_hyperparameters(1)[0]
该代码在我的 PC 上运行顺畅，但最近我已切换到 AWS 以加快该过程。当我使用 AWS 运行此代码时，经过 30-40 次试验后，我不断收到以下错误：
Trial 39 Complete [00h 00m 03s]
val_loss: 0.005517204757779837
Best val_loss So Far: 0.004489939038952191
Total elapsed time: 00h 07m 48s
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-28-9e7dd2527b22> in <module>
35 overwrite = True)
36
---> 37 bayesian_opt_tuner.search(x_nn_train[0], y_nn_train[0],
38 validation_split=0.2,verbose=1)
39
~/project/graduation/lib/python3.8/site-packages/kerastuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
119 self.on_search_begin()
120 while True:
--> 121 trial = self.oracle.create_trial(self.tuner_id)
122 if trial.status == trial_module.TrialStatus.STOPPED:
123 # Oracle triggered exit.
~/project/graduation/lib/python3.8/site-packages/kerastuner/engine/oracle.py in create_trial(self, tuner_id)
163 values = None
164 else:
--> 165 response = self._populate_space(trial_id)
166 status = response['status']
167 values = response['values'] if 'values' in response else None
~/project/graduation/lib/python3.8/site-packages/kerastuner/tuners/bayesian.py in _populate_space(self, trial_id)
103 x, y = self._vectorize_trials()
104 try:
--> 105 self.gpr.fit(x, y)
106 except exceptions.ConvergenceWarning:
107 # If convergence of the GPR fails, create a random trial.
~/project/graduation/lib/python3.8/site-packages/sklearn/gaussian_process/_gpr.py in fit(self, X, y)
243 self._rng.uniform(bounds[:, 0], bounds[:, 1])
244 optima.append(
--> 245 self._constrained_optimization(obj_func, theta_initial,
246 bounds))
247 # Select result from run with minimal (negative) log-marginal
~/project/graduation/lib/python3.8/site-packages/sklearn/gaussian_process/_gpr.py in _constrained_optimization(self, obj_func, initial_theta, bounds)
492 obj_func, initial_theta, method="L-BFGS-B", jac=True,
493 bounds=bounds)
--> 494 _check_optimize_result("lbfgs", opt_res)
495 theta_opt, func_min = opt_res.x, opt_res.fun
496 elif callable(self.optimizer):
~/project/graduation/lib/python3.8/site-packages/sklearn/utils/optimize.py in _check_optimize_result(solver, result, max_iter, extra_warning_msg)
241 " https://scikit-learn.org/stable/modules/"
242 "preprocessing.html"
--> 243 ).format(solver, result.status, result.message.decode("latin1"))
244 if extra_warning_msg is not None:
245 warning_msg += "\n" + extra_warning_msg
AttributeError: 'str' object has no attribute 'decode'
您能否告诉我可能是什么问题以及如何解决？我搜索了这个问题，许多人建议将 h5py 设置为 2.10.0 版；然而，这并没有帮助。