我编写了将gridsearch方法应用于由keras构建的LSTM网络的代码。一切似乎都正常，但传递batch_size时出现了一些问题。
我试图更改batch_size的格式，但是据我所知，它必须是一个元组。
#LSTM ok
from Methods.LSTM_1HL import LSTM_method
Yhat_train_LSTM, Yhat_test_LSTM = LSTM_method(X_train, X_test, Y_train,
Y_test)
def create_model(optimizer, hl1_nodes, input_shape):
# creation of the NN - Electric Load
# LSTM layers followed by other LSTM layer must have the parameter "return_sequences" set at True
model = Sequential()
model.add(LSTM(units = hl1_nodes , input_shape=input_shape, return_sequences=False))
model.add(Dense(1, activation="linear")) # output layer
model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['accuracy'])
model.summary()
return model
def LSTM_method(X_train, X_test, Y_train, Y_test):
# normalize X and Y data
mmsx = MinMaxScaler()
mmsy = MinMaxScaler()
X_train = mmsx.fit_transform(X_train)
X_test = mmsx.transform(X_test)
Y_train = mmsy.fit_transform(Y_train)
Y_test = mmsy.transform(Y_test)
X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])
# NN for Electric Load
# LSTM Input Shape
time_steps = 1 # number of time-steps you are feeding a sequence (?)
inputs_numb = X_train.shape[1] # number of inputs
input_shape=(time_steps, inputs_numb)
model = KerasRegressor(build_fn=create_model,verbose=1)
#GridSearch code
start=time()
optimizers = ['rmsprop', 'adam']
epochs = np.array([100, 500, 1000])
hl1_nodes = np.array([1, 10, 50])
btcsz = np.array([1,X_train.shape[0]])
param_grid = dict(optimizer=optimizers, hl1_nodes=hl1_nodes, input_shape=input_shape, nb_epoch=epochs,batch_size=btcsz)
scoring = make_scorer(accuracy_score) #in order to use a metric as a scorer
grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring = scoring)
grid_result = grid.fit(X_train, Y_train)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
for params, mean_score, scores in grid_result.grid_scores_:
print("%f (%f) with: %r" % (scores.mean(), scores.std(), params))
print("total time:",time()-start)
# Predictions - Electric Load
Yhat_train = grid_result.predict(X_train, verbose=0)
X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])
Yhat_test = grid_result.predict(X_test, verbose=0)
# Denormalization - Electric Load
Yhat_train = mmsy.inverse_transform(Yhat_train)
Yhat_test = mmsy.inverse_transform(Yhat_test)
Y_train = mmsy.inverse_transform(Y_train)
Y_test = mmsy.inverse_transform(Y_test)
return Yhat_train, Yhat_test
出现以下错误：
TypeError Traceback (most recent call last)
in
10 #from Methods.LSTM_1HL import create_model
11
---> 12 Yhat_train_LSTM, Yhat_test_LSTM = LSTM_method(X_train, X_test, Y_train, Y_test)
c:\Users\ER180124\Code\LoadForecasting\Methods\LSTM_1HL.py in LSTM_method(X_train, X_test, Y_train, Y_test)
62 scoring = make_scorer(accuracy_score) #in order to use a metric as a scorer
63 grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring = scoring)
---> 64 grid_result = grid.fit(X_train, Y_train)
65
66 print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
~\.conda\envs\PierEnv\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
720 return results_container[0]
721
--> 722 self._run_search(evaluate_candidates)
723
724 results = results_container[0]
~\.conda\envs\PierEnv\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
1189 def _run_search(self, evaluate_candidates):
1190 """Search all candidates in param_grid"""
-> 1191 evaluate_candidates(ParameterGrid(self.param_grid))
1192
1193
~\.conda\envs\PierEnv\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)
709 for parameters, (train, test)
710 in product(candidate_params,
--> 711 cv.split(X, y, groups)))
712
713 all_candidate_params.extend(candidate_params)
~\.conda\envs\PierEnv\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
915 # remaining jobs.
916 self._iterating = False
--> 917 if self.dispatch_one_batch(iterator):
918 self._iterating = self._original_iterator is not None
919
~\.conda\envs\PierEnv\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
757 return False
758 else:
--> 759 self._dispatch(tasks)
760 return True
761
~\.conda\envs\PierEnv\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
714 with self._lock:
715 job_idx = len(self._jobs)
--> 716 job = self._backend.apply_async(batch, callback=cb)
717 # A job can complete so quickly than its callback is
718 # called before we get here, causing self._jobs to
~\.conda\envs\PierEnv\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
180 def apply_async(self, func, callback=None):
181 """Schedule a func to be run"""
--> 182 result = ImmediateResult(func)
183 if callback:
184 callback(result)
~\.conda\envs\PierEnv\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
547 # Don't delay the application, to avoid keeping the input
548 # arguments in memory
--> 549 self.results = batch()
550
551 def get(self):
~\.conda\envs\PierEnv\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
~\.conda\envs\PierEnv\lib\site-packages\sklearn\externals\joblib\parallel.py in (.0)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
~\.conda\envs\PierEnv\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
526 estimator.fit(X_train, **fit_params)
527 else:
--> 528 estimator.fit(X_train, y_train, **fit_params)
529
530 except Exception as e:
~\.conda\envs\PierEnv\lib\site-packages\keras\wrappers\scikit_learn.py in fit(self, x, y, **kwargs)
139 **self.filter_sk_params(self.build_fn.__call__))
140 else:
--> 141 self.model = self.build_fn(**self.filter_sk_params(self.build_fn))
142
143 loss_name = self.model.loss
c:\Users\ER180124\Code\LoadForecasting\Methods\LSTM_1HL.py in create_model(optimizer, hl1_nodes, input_shape)
19 # LSTM layers followed by other LSTM layer must have the parameter "return_sequences" set at True
20 model = Sequential()
---> 21 model.add(LSTM(units = hl1_nodes , input_shape=input_shape, return_sequences=False))
22 model.add(Dense(1, activation="linear")) # output layer
23 model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['accuracy'])
~\.conda\envs\PierEnv\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\.conda\envs\PierEnv\lib\site-packages\keras\layers\recurrent.py in __init__(self, units, activation, recurrent_activation, use_bias, kernel_initializer, recurrent_initializer, bias_initializer, unit_forget_bias, kernel_regularizer, recurrent_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, recurrent_constraint, bias_constraint, dropout, recurrent_dropout, implementation, return_sequences, return_state, go_backwards, stateful, unroll, **kwargs)
2183 stateful=stateful,
2184 unroll=unroll,
-> 2185 **kwargs)
2186 self.activity_regularizer = regularizers.get(activity_regularizer)
2187
~\.conda\envs\PierEnv\lib\site-packages\keras\layers\recurrent.py in __init__(self, cell, return_sequences, return_state, go_backwards, stateful, unroll, **kwargs)
406 '(tuple of integers, '
407 'one integer per RNN state).')
--> 408 super(RNN, self).__init__(**kwargs)
409 self.cell = cell
410 self.return_sequences = return_sequences
~\.conda\envs\PierEnv\lib\site-packages\keras\engine\base_layer.py in __init__(self, **kwargs)
145 batch_size = None
146 batch_input_shape = (
--> 147 batch_size,) + tuple(kwargs['input_shape'])
148 self.batch_input_shape = batch_input_shape
149
TypeError: 'int' object is not iterable
我不明白为什么在错误消息的最后一部分中，当我定义一个为元组的批处理大小时会得到“ batch_size = None”。
答案 0 :(得分：0)
好吧，我想我遇到了你的问题。
在进行CV搜索时，很可能会使用可能的配置的叉积从参数字典中生成参数网格。您的参数字典中有input_shape的{{1}}，实际上是两个整数的序列。因此，您输入的形状参数为(time_steps, inputs_numb)或time_steps。然后，它们在堆栈跟踪的最后一行变为inputs_numb或(None,) + (times_steps)。这是一个元组+ int运算，因此无效。相反，您希望配置空间只有一个可能的(None,) + (inputs_numb)。
您应该做的就是转换此行
input_shape对此：
input_shape=(time_steps, inputs_numb)