我正在尝试使用以下代码来设置GridSearchCV纪元和batch_size：
jq -L $HOME 'include ".jq/url_decode"; ...
并出现以下错误：
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, shuffle=False)
X_train2 = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))
y_train2 = np.ravel(y_train.values)
X_test2 = X_test.values.reshape((X_test.shape[0], 1, X_test.shape[1]))
y_test2 = np.ravel(y_test.values)
def build_model():
model = Sequential()
model.add(LSTM(500, input_shape=(1, X_train.shape[1])))
model.add(Dense(1))
model.compile(loss="mse", optimizer="adam")
return model
new_model = KerasRegressor(build_fn=build_model, verbose=0)
pipe = Pipeline([('s', StandardScaler()), ('reg', new_model)])
param_gridd = {'reg__epochs': [5, 6], 'reg__batch_size': [71, 72]}
model = GridSearchCV(estimator=pipe, param_grid=param_gridd)
# ------------------ if the following two lines are uncommented the code works -> problem with Pipeline?
# param_gridd = {'epochs':[5,6], 'batch_size': [71, 72]}
# model = GridSearchCV(estimator=new_model, param_grid=param_gridd)
fitted = model.fit(X_train2, y_train2, validation_data=(X_test2, y_test2), verbose=2, shuffle=False)
我怀疑这与File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 722, in fit
self._run_search(evaluate_candidates)
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 1191, in _run_search
evaluate_candidates(ParameterGrid(self.param_grid))
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 711, in evaluate_candidates
cv.split(X, y, groups)))
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 917, in __call__
if self.dispatch_one_batch(iterator):
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 759, in dispatch_one_batch
self._dispatch(tasks)
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 716, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/externals/oblib/_parallel_backends.py", line 182, in apply_async
result = ImmediateResult(func)
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 549, in __init__
self.results = batch()
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 225, in __call__
for func, args, kwargs in self.items]
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 225, in <listcomp>
for func, args, kwargs in self.items]
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 528, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/pipeline.py", line 265, in fit
Xt, fit_params = self._fit(X, y, **fit_params)
File "/home/geo/anaconda3/lib/python3.6/site-packages/sklearn/pipeline.py", line 202, in _fit
step, param = pname.split('__', 1)
ValueError: not enough values to unpack (expected 2, got 1)
中的命名有关，但不确定是怎么回事。请注意，当我从代码中删除param_gridd并直接在new_model上使用GridSearchCV时，代码可以正常工作。
答案 0 :(得分：1)
我认为问题在于KerasRegressor的适合参数的馈送方式。
validation_data，shuffle不是GridSearchCV的参数，而是reg。
试试吧！
fitted = model.fit(X_train2, y_train2,**{'reg__validation_data':(X_test2, y_test2),'reg__verbose':2, 'reg__shuffle':False} )
编辑： 基于@Vivek kumar的发现，我为您的预处理编写了一个包装。
from sklearn.preprocessing import StandardScaler
class custom_StandardScaler():
def __init__(self):
self.scaler =StandardScaler()
def fit(self,X,y=None):
self.scaler.fit(X)
return self
def transform(self,X,y=None):
X_new=self.scaler.transform(X)
X_new = X_new.reshape((X.shape[0], 1, X.shape[1]))
return X_new
这将帮助您实现标准缩放器以及创建新尺寸。请记住，我们必须先将评估数据集转换为fit_params（），然后使用单独的缩放器（offline_scaler()）对其进行转换。
from sklearn.datasets import load_boston
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from keras.layers import LSTM
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np
seed = 1
boston = load_boston()
X, y = boston['data'], boston['target']
X_train, X_eval, y_train, y_eval = train_test_split(X, y, test_size=0.2, random_state=42)
def build_model():
model = Sequential()
model.add(LSTM(5, input_shape=(1, X_train.shape[1])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='Adam', metrics=['mae'])
return model
new_model = KerasRegressor(build_fn=build_model, verbose=0)
param_gridd = {'reg__epochs':[2,3], 'reg__batch_size':[16,32]}
pipe = Pipeline([('s', custom_StandardScaler()),('reg', new_model)])
offline_scaler = custom_StandardScaler()
offline_scaler.fit(X_train)
X_eval2 = offline_scaler.transform(X_eval)
model = GridSearchCV(estimator=pipe, param_grid=param_gridd,cv=3)
fitted = model.fit(X_train, y_train,**{'reg__validation_data':(X_eval2, y_eval),'reg__verbose':2, 'reg__shuffle':False} )
答案 1 :(得分：1)
正如@AI_Learning所说，此行应该有效：
fitted = model.fit(X_train2, y_train2,
reg__validation_data=(X_test2, y_test2),
reg__verbose=2, reg__shuffle=False)
管道要求将参数命名为"component__parameter"。因此，在reg__之前加上参数即可。
但是，这不起作用，因为StandardScaler会抱怨数据尺寸。您会看到，当您这样做时：
X_train2 = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))
...
X_test2 = X_test.values.reshape((X_test.shape[0], 1, X_test.shape[1]))
您将X_train2和X_test2设为3D数据。您已完成使其适用于LSTM的操作，但不适用于StandardScaler，因为这需要形状为(n_samples, n_features)的二维数据。
如果您像这样从管道中删除StandardScaler：
pipe = Pipeline([('reg', new_model)])
尝试输入我和建议的代码@AI_Learning，它将起作用。这表明与管道无关，但与不兼容的变压器一起使用。
您可以将StandardScaler移出管道并执行以下操作：
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, shuffle=False)
std = StandardScaler()
X_train = std.fit_transform(X_train)
X_test = std.transform(X_test)
X_train2 = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))
y_train2 = np.ravel(y_train.values)
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