machine-learning - Keras-如何使用KerasRegressor进行预测？

我是机器学习的新手，我正在尝试处理Keras来执行回归任务。我已经基于this示例实现了此代码。

X = df[['full_sq','floor','build_year','num_room','sub_area_2','sub_area_3','state_2.0','state_3.0','state_4.0']]
y = df['price_doc']

X = np.asarray(X)
y = np.asarray(y)

X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=.2)
def baseline_model():
    model = Sequential()
    model.add(Dense(13, input_dim=9, kernel_initializer='normal',
        activation='relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=100, verbose=False)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(estimator, X_train, Y_train, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))

prediction = estimator.predict(X_test)
accuracy_score(Y_test, prediction)

当我运行代码时，出现以下错误：


  AttributeError: 'KerasRegressor' object has no attribute 'model'


我如何正确地在KerasRegressor中“插入”模型？

最佳答案

您必须在cross_val_score之后再次适合估算器，以评估新数据：

estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=100, verbose=False)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(estimator, X_train, Y_train, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))

estimator.fit(X, y)
prediction = estimator.predict(X_test)
accuracy_score(Y_test, prediction)

工作测试版本：

from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score, KFold
from keras.models import Sequential
from sklearn.metrics import accuracy_score
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
seed = 1

diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]

def baseline_model():
    model = Sequential()
    model.add(Dense(10, input_dim=10, activation='relu'))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model


estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=100, verbose=False)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(estimator, X, y, cv=kfold)
print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))

estimator.fit(X, y)
prediction = estimator.predict(X)
accuracy_score(y, prediction)