20191208_神经网络交叉验证_神经网络1000 500 50 2

这个单子难点在神经网络交叉验证，之前只会机器学习的交叉验证，借鉴一个微信文章才做出来的，文档链接https://mp.weixin.qq.com/s?__biz=MzA4OTg5NzY3NA==&mid=2649345834&idx=1&sn=3c748d2d3c0ac89395da25a07a75cefa&chksm=880e808fbf7909999e2775254dc6ac0b02fd4fc582977a4de640b6249d838725d6fedf00aead&mpshare=1&scene=23&srcid=1211ZPzaGxnuVALs2XUXGnMx&sharer_sharetime=1576057502530&sharer_shareid=d4f40f7a25def68e84cbad465c76535f#rd

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
1
2
3
4
5

test=pd.read_csv('CMP3751M_CMP9772M_ML_Assignment 2-dataset-nuclear_plants_final.csv')
test.head()
1
2

test.describe()
1

test.isnull().sum()
1

Status                   0
Power_range_sensor_1     0
Power_range_sensor_2     0
Power_range_sensor_3     0
Power_range_sensor_4     0
Pressure _sensor_1       0
Pressure _sensor_2       0
Pressure _sensor_3       0
Pressure _sensor_4       0
Vibration_sensor_1       0
Vibration_sensor_2       0
Vibration_sensor_3       0
Vibration_sensor_4       0
dtype: int64
1
2
3
4
5
6
7
8
9
10
11
12
13
14

def function(a):
	if 'Normal'in a :
		return 1
	else:
		return 0
test['Status'] = test.apply(lambda x: function(x['Status']), axis = 1)
1
2
3
4
5
6

from sklearn.preprocessing import StandardScaler
x = test.drop(['Status'],axis=1)
y=test['Status']
X_scaler = StandardScaler()
x = X_scaler.fit_transform(x)
1
2
3
4
5

print('data shape: {0}; no. positive: {1}; no. negative: {2}'.format(
    x.shape, y[y==1].shape[0], y[y==0].shape[0]))
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
1
2
3
4

data shape: (996, 12); no. positive: 498; no. negative: 498
1

import keras
from keras.models import Sequential
from keras.layers import Dense
classifier = Sequential()
1
2
3
4

Using Theano backend.
WARNING (theano.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`
WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.
WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
1
2
3
4

for i in [50,500,1000]:
    classifier = Sequential()
    classifier.add(Dense(i, kernel_initializer = 'uniform',activation ='sigmoid', input_dim=12))
    classifier.add(Dense(1, kernel_initializer = 'uniform',activation ='sigmoid'))
    classifier.compile(optimizer= 'adam',loss = 'binary_crossentropy',metrics = ['accuracy'])
    classifier.fit(X_train, y_train, batch_size = 700, epochs = 1)
    print(' nerve cell{} accuracy rate '.format(i))
1
2
3
4
5
6
7

Epoch 1/1
796/796 [==============================] - 2s 2ms/step - loss: 0.6935 - accuracy: 0.5113
 nerve cell50 accuracy rate 
Epoch 1/1
796/796 [==============================] - 14s 17ms/step - loss: 0.6934 - accuracy: 0.5113
 nerve cell500 accuracy rate 
Epoch 1/1
796/796 [==============================] - 28s 35ms/step - loss: 0.6924 - accuracy: 0.4975
 nerve cell1000 accuracy rate 
1
2
3
4
5
6
7
8
9

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
1
2

def make_classifier():
    classifier = Sequential()
    classiifier.add(Dense(3, kernel_initializer = 'uniform', activation = 'relu', input_dim=12))
    classiifier.add(Dense(3, kernel_initializer = 'uniform', activation = 'relu'))
    classifier.add(Dense(1, kernel_initializer = 'uniform', activation = 'sigmoid'))
    classifier.compile(optimizer= 'adam',loss = 'binary_crossentropy',metrics = ['accuracy'])
    return classifier
1
2
3
4
5
6
7

classiifier = KerasClassifier(build_fn = make_classifier,
                            batch_size=100, nb_epoch=300)
1
2

accuracies = cross_val_score(estimator = classiifier,X = X_train,y = y_train,cv = 10,n_jobs = -1)
1

mean = accuracies.mean()
1

import keras
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense
for i in [50,500,1000]:
    classifier = Sequential()
    classiifier.add(Dense(i,kernel_initializer = 'uniform', activation = 'relu', input_dim=12))
    classifier.add(Dense(1, kernel_initializer = 'uniform', activation = 'sigmoid'))
    classifier.compile(optimizer= 'adam',loss = 'binary_crossentropy',metrics = ['accuracy'])
    classifier.fit(X_train, y_train, batch_size = 100, epochs = 300)
    print(' nerve cell{} accuracy rate '.format(i))
1
2
3
4
5
6
7
8
9
10
11
12

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

<ipython-input-14-f1690d4285f8> in <module>()
      6 for i in [50,500,1000]:
      7     classifier = Sequential()
----> 8     classiifier.add(Dense(i,kernel_initializer = 'uniform', activation = 'relu', input_dim=12))
      9     classifier.add(Dense(1, kernel_initializer = 'uniform', activation = 'sigmoid'))
     10     classifier.compile(optimizer= 'adam',loss = 'binary_crossentropy',metrics = ['accuracy'])


NameError: name 'classiifier' is not defined
1
2
3
4
5
6
7
8
9
10
11
12
13