使用keras深度学习实现回归问题示例_深度学习 数据回归

通常情况下，我们都是用深度学习做分类，但有时候也会用来做回归。
原文出处：Regression Tutorial with the Keras Deep Learning Library in Python
1. 这里作者使用Keras 和python的scikit-learn机器学习库来实现了对房价的回归预测。关于scikit-learn与Keras联合可参考 Scikit-Learn接口包装器
2. 原文第一个例程中，作者介绍了怎么导入数据，然后搭建了一个单隐层的神经网络。因为时回归问题，在输出层作者并没有使用激活函数。然后使用Scikit-Learn接口包装器将Sequential模型作为Scikit-Learn工作流的一部分，最后使用了交叉验证方法来评估模型好坏。
3. 第二个例程中，作者将数据进行了规范化处理，并评估其效果
4. 第三个例程中，作者分别改变了网络的深度和宽度，并查看其效果。
5. 代码和数据集原文中都有，把源码整理后再在这里贴一下：
housing_price.py

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# load dataset
dataframe = pandas.read_csv("housing.csv", delim_whitespace=True, header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:, 0:13]
Y = dataset[:, 13]
# define base mode
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, init='normal', activation='relu'))
    model.add(Dense(1, init='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# evaluate model with standardized dataset
estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=100, batch_size=5, verbose=0)
# use 10-fold cross validation to evaluate this baseline model
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()))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35

housing_price2.py

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# load dataset
dataframe = pandas.read_csv("housing.csv", delim_whitespace=True, header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:, 0:13]
Y = dataset[:, 13]
# define base mode
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, init='normal', activation='relu'))
    model.add(Dense(1, init='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# evaluate model with standardized dataset
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, nb_epoch=50, batch_size=5, verbose=0)))
pipeline = Pipeline(estimators)
# use 10-fold cross validation to evaluate this baseline model
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print("Standardized: %.2f (%.2f) MSE" % (results.mean(), results.std()))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

housing_price3.py

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# load dataset
dataframe = pandas.read_csv("housing.csv", delim_whitespace=True, header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:, 0:13]
Y = dataset[:, 13]
# define base mode
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, init='normal', activation='relu'))
    model.add(Dense(6, init='normal', activation='relu'))
    model.add(Dense(1, init='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# evaluate model with standardized dataset
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, nb_epoch=50, batch_size=5, verbose=0)))
pipeline = Pipeline(estimators)
# use 10-fold cross validation to evaluate this baseline model
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print("Standardized: %.2f (%.2f) MSE" % (results.mean(), results.std()))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39

housing_price4.py

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# load dataset
dataframe = pandas.read_csv("housing.csv", delim_whitespace=True, header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:, 0:13]
Y = dataset[:, 13]
# define base mode
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(20, input_dim=13, init='normal', activation='relu'))
    model.add(Dense(1, init='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# evaluate model with standardized dataset
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, nb_epoch=50, batch_size=5, verbose=0)))
pipeline = Pipeline(estimators)
# use 10-fold cross validation to evaluate this baseline model
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print("Standardized: %.2f (%.2f) MSE" % (results.mean(), results.std()))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38