Keras

Question

我是机器学习的新手，我正在尝试处理 Keras 来执行回归任务。我已经根据 this 示例实现了这段代码。

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，我得到这个错误：

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

我怎样才能正确 'insert' KerasRegressor 中的模型？

Answer 1

您必须在 cross_val_score 之后再次拟合估计器以评估新数据：

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)

Answer 2

为了评估您的系统性能，您可以像下面这样计算误差。您也不需要调用 KFold 和 cross_val_score.

import numpy as np
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)
estimator.fit(X, y)
prediction = estimator.predict(X)

train_error =  np.abs(y - prediction)
mean_error = np.mean(train_error)
min_error = np.min(train_error)
max_error = np.max(train_error)
std_error = np.std(train_error)

Answer 3

您可以直接使用模型本身来代替kerasRegressor。这两个代码片段给出了完全相同的结果：

estimator = KerasRegressor(build_fn=baseline_model)
estimator.fit(X, y, nb_epoch=100, batch_size=100, verbose=False, shuffle=False)
prediction = estimator.predict(X)


model = baseline_model()
model.fit(X, y, nb_epoch=100, batch_size=100, verbose=False, shuffle=False)
prediction = model.predict(X)

请注意，kerasRegressor 和模型的 fit() 函数的 shuffle 参数必须为 False。此外，为了获得固定的初始状态并获得可重现的结果，您需要在脚本的开头添加这些代码行：

session = K.get_session()
init_op = tf.group(tf.tables_initializer(),tf.global_variables_initializer(), tf.local_variables_initializer())
session.run(init_op)
np.random.seed(1)
tf.set_random_seed(1)

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regression

machine-learning

neural-network

scikit-learn