我的网格搜索CV不起作用，我不知道为什么

Question

您好，我对 GridSearchCV 有问题，它在mnist_dataset上完美运行，但在我自己的数据上却不能，我不知道为什么。

  # df = pd.read_csv('bank-full.csv',sep=';')
# print(df.head())
#
# print(df.shape)
#
# print(df.columns)
# print(df.info)
# df.columns = [col.replace('"', '') for col in df.columns]
#
#
# df.drop(columns=['day', 'poutcome'], axis =1 , inplace=True)
#
#
# print(df.head())
# print(df.shape)
#
# le = preprocessing.LabelEncoder()
# df.job = le.fit_transform(df.job)
# df.education = le.fit_transform(df.education)
# df.housing = le.fit_transform(df.housing)
# df.loan = le.fit_transform(df.loan)
# #df.poutcome = le.fit_transform(df.poutcome)
# df.month = le.fit_transform(df.month)
# df.contact = le.fit_transform(df.contact)
# df.marital = le.fit_transform(df.marital)
# df.default = le.fit_transform(df.default)
# df.y = le.fit_transform(df.y)
#
#
#
# print(df.head())
#
# X = df.iloc[:, 0:14]
# y = df.iloc[:, 14]
# X = np.array(X, dtype="float64")
# y = np.array(y,dtype="float64")
#
# scaler = Normalizer()
# X = scaler.fit_transform(X)
#
#
#
#
# x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=0)
# model = LogisticRegression(penalty='l2', max_iter=1000)
# model.fit(x_train, y_train)
# prediction = model.predict(x_test)
# from sklearn.metrics import accuracy_score
# print("ACC: {} ".format(accuracy_score(y_test, prediction)))
#
#
# print(x_train.shape)
#
# nn = Sequential()
# nn.add(Dense(120,input_dim = 14, activation='relu'))
# nn.add(Dense(240,activation='relu'))
#
#
# nn.add(Dense(1))
# nn.add(Activation('sigmoid'))
#
# nn.compile(loss=keras.losses.binary_crossentropy,
#                optimizer='sgd',
#                metrics=['accuracy'])
#
# nn.fit(x_train, y_train,
#            batch_size=10,
#            epochs=10,
#            verbose=1,
#
#            validation_data=(x_test, y_test))
#
# loss_acc = nn.evaluate(x_test, y_test, verbose=0)
# print('Test loss:', loss_acc[0])
# print('Test accuracy:', loss_acc[1])

data = bm.load_data('bank-full.csv')
data = bm.preprocess_data(data)
X,y = bm.split_data(data)
scaler = Normalizer()
X = scaler.fit_transform(X)
x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=0)

start = time()
model = KerasClassifier(build_fn=nnmodel.create_model())
optimizers = ['rmsprop', 'adam']
init = ['glorot_uniform', 'normal', 'uniform']
epochs = np.array([50, 100, 150])
batches = np.array([5, 10, 20])
param_grid = dict(optimizer=optimizers, nb_epoch=epochs, batch_size=batches, init=init)
grid = GridSearchCV(estimator=model, param_grid=param_grid)
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)

这个评论部分只是一个简单的 keras 模型，效果很好，但在下面，如果我在这个模型上尝试 gridSearchCV，它会给我这个错误：https://pastebin.com/mhJLSXAS，例如，如果我运行这个程序 https://www.kaggle.com/shujunge/gridsearchcv-with-keras 它在我的数据上正常工作，它不是，有人知道为什么吗？

Answer 1

Scikitlearn每次都会构建新模型。脚本必须在网格搜索方法中构建具有特定参数的分类器。因此，您必须将方法名称作为参数发送，而不是其结果。

可能nnmodel.create_model您的函数会根据参数创建新模型。所以试着改变：

build_fn=nnmodel.create_model()

自：

build_fn=nnmodel.create_model