Python深度学习实战06-Sonar信号分类

18 Aug 2017

本回合, 我们设计不同的神经网络模型, 对Sonar信号进行分类练习.

1.Sonar 数据集

来源: Index of /ml/machine-learning-databases/undocumented/connectionist-bench/sonar

两类sonar信号辨别:

M: 表示从金属缸返回的声纳信号(bounced off a metal cylinder), 总 111 个样本
R: 表示从圆柱形的岩石返回的声纳信号(bounced off a roughly cylindrical rock) 总97个样本

总样本数目为208, 样本特征数为60, 这60个特征都是 0.0-1.0 范围内的数值.

A benefit of using this dataset is that it is a standard benchmark problem. This means that we have some idea of the expected skill of a good model. Using cross validation, a neural network should be able to achieve performance around 84% with an upper bound on accuracy for custom models at around 88%.

2.神经网络模型性能基准

先定一个基准, 三层神经网络, 拓扑结构为60-60-1.

# Baseline NN model performance
import numpy as np
import pandas as pd

from keras.models import Sequential
from keras.layers import Dense
from keras. wrappers.scikit_learn import KerasClassifier

from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# 随机数设定
seed = 42
np.random.seed(seed)
# 加载数据
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data"
df = pd.read_csv(url, header=None)
dataset = df.values
# 特征与类别
X = dataset[:, 0:60].astype(float)
Y = dataset[:, 60]
# 类别编码成数字形式 0 or 1
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)

# 创建神经网络模型
def creat_baseline():
    # 创建模型
    model = Sequential()
    # 中间层网络参数设定
    model.add(Dense(60, input_dim=60, init='normal', activation='relu'))#
    # 输出层网络参数设定
    model.add(Dense(1, init='normal', activation='sigmoid'))
    # 编译模型
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 评估模型
estimator = KerasClassifier(build_fn=creat_baseline, nb_epoch=100, batch_size=5, verbose=0)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator, X, encoded_Y, cv=kfold)
print("Baseline: {0}% ({1}%)".format(results.mean()*100, results.std()*100))

结果:

Baseline: 76.0367974258% (7.84685282946%)

结果显示神经网络模型在unseen data上的准确率为76.03%. 我们单单设计一个三层的神经网络,准确率就达到70%以上. 如果我们改进下, 会出现怎样的结果. 下面我们来看看.

可参考:

3.数据处理

我们将数据特征正则化(standardize).数据特征的均值为0, 标准差为1.

This is where the data is rescaled such that the mean value for each attribute is 0 and the standard deviation is 1.

# 两分类问题使用标准化数据
import numpy as np
import pandas as pd

from keras.models import Sequential
from keras.layers import Dense
from keras. wrappers.scikit_learn import KerasClassifier

from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# 随机数设定
seed = 42
np.random.seed(seed)
# 加载数据
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data"
df = pd.read_csv(url, header=None)
dataset = df.values
# 特征与类别
X = dataset[:, 0:60].astype(float)
Y = dataset[:, 60]
# 类别编码成数字
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)

# 模型函数
def creat_baseline():
    # 创建模型
    model = Sequential()
    model.add(Dense(60, input_dim=60, init='normal', activation='relu'))
    model.add(Dense(1, init='normal', activation='sigmoid'))
    # 编译模型
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 评估模型
np.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=creat_baseline, nb_epoch=100,
                                        batch_size=5, verbose=0)))
# 封装在pipeline中
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Standardized: {0}% ({1}%)".format(results.mean()*100, results.std()*100))

结果:Standardized: 85.5887454477% (5.27619115623%)

结果显示神经网络模型在unseen data上的准确率为85.59%.

可参考:

4.改变神经网络层数和节点数

改变神经网络的拓扑结构, 来观察模型性能.

smaller, 建立一个小的神经网络模型, 三层: 60-30-1;
larger: 建立一个大的神经网络模型, 四层: 60-60-30-1.

# Smaller 神经网络模型 60-30-1
import numpy as np
import pandas as pd

from keras.models import Sequential
from keras.layers import Dense
from keras. wrappers.scikit_learn import KerasClassifier

from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# 随机数设定
seed = 42
np.random.seed(seed)
# 加载数据
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data"
df = pd.read_csv(url, header=None)
dataset = df.values
# 特征与类别
X = dataset[:, 0:60].astype(float)
Y = dataset[:, 60]
# 类别编码成数字
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)

# smaller_model
def creat_smaller():
    # 创建模型
    model = Sequential()
    model.add(Dense(30, input_dim=60, init='normal', activation='relu'))
    model.add(Dense(1, init='normal', activation='sigmoid'))
    # 编译模型
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

np.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=creat_smaller, nb_epoch=100,
                                         batch_size=5, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Smaller: {0:.2f}% ({1:.2f}%)".format(results.mean()*100, results.std()*100))

结果:Smaller: 81.30% (5.70%)

Smaller网络模型与基准的网络模型baseline 相比, 它们的神经网络层数相同, 就中间隐含层节点数不同, 前者节点数为30, 后者节点数为60.

Smaller神经网络的结果为81.30%, 比baseline神经网络模型的85.59%低4.29%.

# Larger神经网络模型 60-60-30-1
import numpy as np
import pandas as pd

from keras.models import Sequential
from keras.layers import Dense
from keras. wrappers.scikit_learn import KerasClassifier

from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# 随机数设定
seed = 42
np.random.seed(seed)
# 加载数据
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data"
df = pd.read_csv(url, header=None)
dataset = df.values
# 特征与类别
X = dataset[:, 0:60].astype(float)
Y = dataset[:, 60]
# 类别编码成数字
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)

# larger_model
def creat_larger():
    # 创建模型
    model = Sequential()
    model.add(Dense(60, input_dim=60, init='normal', activation='relu'))
    model.add(Dense(30, init='normal', activation='relu'))
    model.add(Dense(1, init='normal', activation='sigmoid'))
    # 编译模型
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

np.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=creat_larger, nb_epoch=100,
                                         batch_size=5, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Larger: {0:.2f}% ({1:.2f}%)".format(results.mean()*100, results.std()*100))

Larger网络模型(60-60-30-1)与基准的网络模型baseline(60-60-1)相比, 它们的神经网络层数不同,前者比后者多一层.

Larger神经网络的结果为88.49%, 比baseline神经网络模型的85.59% 高2.9%.