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有284個(gè)訓(xùn)練樣本,273個(gè)測(cè)試樣本,通過(guò)對(duì)數(shù)據(jù)的處理后進(jìn)入基于LSTM的多層循環(huán)神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練,測(cè)試樣本測(cè)試準(zhǔn)確率可達(dá)70+

資源截圖

代碼片段和文件信息


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??????┃   ━   ┃
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??????┃      ?┃
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??????┗━┓   ┏━━━┛
????????┃   ┃???神獸保佑
????????┃   ┃???代碼無(wú)BUG!
????????┃   ┗━━━━━━━━━┓
????????┃       ????┣┓
????????┃    ?????????┏┛
????????┗━┓?┓?┏━━━┳?┓?┏━┛
??????????┃?┫?┫???┃?┫?┫
??????????┗━┻━┛???┗━┻━┛

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import?tensorflow?as?tf
import??numpy?as?np
import??scipy.io
import?time

data?=scipy.io.loadmat(‘G2MM06_NormalDataSet_train.mat‘)
data1=scipy.io.loadmat(‘G2MM06_NormalDataSet_test.mat‘)

print(data1[‘DataSet‘][0][0][0][0][5])
pi=data1[‘DataSet‘][0][0][0][0][5]
print(pi[0][0]pi[0][1])

log_dir=‘/tmp/tensorflow/logs/12‘
“““
X_train[0]=np.array(X_train[0])
X_train[0]=np.array(X_train[0])?
print(X_train[0][0][1]np.shape(X_train[0]))
“““

lst=[]
for?m?in?range(284):
?????X_train?=?data[‘DataSet‘][m][0][0][0][2]
?????X_train=X_train.T
?????pi?=?data[‘DataSet‘][m][0][0][0][5]
?????
?????for?k?in?range(6):
???????for?i?in?range(57):
?????????for?j?in?range(1+i):
?????????????lst.append(X_train[k][i][j])

???????lst.append(pi[0][0])
???????lst.append(pi[0][1])
?????????

X_trainNew1=np.array(lst)
X_trainNew1=X_trainNew1.reshape([-11655])

X_trainMu1=data[‘DataSet‘][0][0][0][0][1]
for?i?in?range(283):
????X_trainMu1=np.hstack((X_trainMu1data[‘DataSet‘][i+1][0][0][0][1]))
X_trainMu1=X_trainMu1.T

X_trainNew1?=?np.hstack((X_trainNew1X_trainMu1))
print(“訓(xùn)練集“)
print(X_trainNew1np.shape(X_trainNew1))



lst3=[]
for?m?in?range(284):
?????X_train?=?data[‘DataSet‘][m][0][0][0][4]
?????X_train=X_train.T

?????for?k?in?range(6):
???????for?i?in?range(57):
?????????for?j?in?range(1+i):
?????????????lst3.append(X_train[k][i][j])
X_trainNew2=np.array(lst3)
X_trainNew2=X_trainNew2.reshape([-11653])

X_trainMu2=data[‘DataSet‘][0][0][0][0][3]
for?i?in?range(283):
????X_trainMu2=np.hstack((X_trainMu2data[‘DataSet‘][i+1][0][0][0][3]))
X_trainMu2=X_trainMu2.T

X_trainNew2?=?np.hstack((X_trainNew2X_trainMu2))
print(“訓(xùn)練集“)
print(X_trainNew2np.shape(X_trainNew2))




X_trainNew3=np.hstack((X_trainNew1X_trainNew2))
print(np.shape(X_trainNew3))





lst1=[]
for?m?in?range(273):
?????X_test?=?data1[‘DataSet‘][m][0][0][0][2]
?????X_test=X_test.T
?????pi?=?data1[‘DataSet‘][m][0][0][0][5]
?????for?k?in?range(6):
???????for?i?in?range(57):
?????????for?j?in?range(1+i):
?????????????lst1.append(X_test[k][i][j])
???????lst1.append(pi[0][0])
???????lst1.append(pi[0][1])


X_testNew1=np.array(lst1)
X_testNew1=X_testNew1.reshape([-11655])

X_testMu1=data1[‘DataSet‘][0][0][0][0][1]
for?i?in?range(272):
????X_testMu1=np.hstack((X_testMu1data1[‘DataSet‘][i+1][0][0][0][1]))
X_testMu1=X_testMu1.T

X_testNew1?=?np.hstack((X_testNew1X_testMu1))

print(“測(cè)試集“)
print(X_testNew1np.shape(X_testNew1))

lst4=[]
for?m?in?range(273):
?????X_test?=?data1[‘DataSet‘][m][0][0][0][4]
?????X_test=X_test.T
?????for?k?in?range(6):
???????for?i?in?range(57):
?????????for?j?in?range(1+i):
?????????????lst4.append(X_test[k][i][j])

X_testNew2=np.array(lst4)
X_testNew2

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