880行，在試圖使用蒔蘿加載時，在load_eof中引發EOFError EOFError

Question

我是python和機器學習的新手。 我一直在使用neupy庫成功創建神經網絡。 但是，既然我有一個不錯的網絡，我想保存它。 該文檔顯示了如何使用蒔蘿庫執行此操作。 網絡似乎已正確寫入文件，但不會加載要使用的文件。 重復代碼是因為我打算在正確實現代碼后必須分離腳本。 我已經完全按照給定的方式復制了代碼（ http://neupy.com/docs/storage.html ）

我的代碼是：

import dill
import csv
import numpy as np
from sklearn import datasets, preprocessing
from sklearn.cross_validation import train_test_split
from neupy import algorithms, layers
from neupy.functions import rmsle

np.random.seed(0)

#variables
EPOCHS = 200
HIDDENLAYER = 17
miss = 0.1
hit = 0.2
TRAIN = 0.7
ROUND = 2
STEP = 0.003
TOL = 0.02
with open('binary_conversion_dataset_input_2.csv','r') as dest1_f:
    data_iter = csv.reader(dest1_f,
                           delimiter = ',',
                           quotechar = '"')
    data = [data for data in data_iter]
data_array1 = np.asarray(data, dtype = float)
hitmiss_in = data_array1    #loads entire dataset from excel csv file

with open('binary_conversion_dataset_target_2.csv','r') as dest2_f:
    data_iter = csv.reader(dest2_f,
                           delimiter = ',',
                           quotechar = '"')
    data = [data for data in data_iter]
data_array2 = np.asarray(data, dtype = float)
hitmiss_target = data_array2    #loads entire dataset from excel csv file



hitmiss_input = hitmiss_in[:,:]   

hitmiss_target = hitmiss_target[:,:]   


hitmiss_predict = [0.53, 0.80, 0.40, 0.20, 0.07]

#####break target set into single numbers
hitmiss_target1a = hitmiss_target[:,0]
hitmiss_target1b = hitmiss_target[:,1]
hitmiss_target1c = hitmiss_target[:,2]
hitmiss_target1d = hitmiss_target[:,3]
hitmiss_target1e = hitmiss_target[:,4]
##hitmiss_target1f = hitmiss_target[:,5]
##hitmiss_target1g = hitmiss_target[:,6]
##hitmiss_target1h = hitmiss_target[:,7]
##hitmiss_target1i = hitmiss_target[:,8]
##hitmiss_target1j = hitmiss_target[:,9]
##hitmiss_target1k = hitmiss_target[:,10]
##hitmiss_target1l = hitmiss_target[:,11]
##hitmiss_target1m = hitmiss_target[:,12]
##hitmiss_target1n = hitmiss_target[:,13]
##hitmiss_target1o = hitmiss_target[:,14]
##hitmiss_target1p = hitmiss_target[:,15]
##hitmiss_target1q = hitmiss_target[:,16]
##hitmiss_target1r = hitmiss_target[:,17]
##hitmiss_target1s = hitmiss_target[:,18]
##hitmiss_target1t = hitmiss_target[:,19]

################################################Neural Network for hit miss

x_train, x_test, y_train, y_test = train_test_split(
   hitmiss_input, hitmiss_target1a, train_size=TRAIN
   )

cgnet = algorithms.ConjugateGradient(
   connection=[
       layers.TanhLayer(5),
       layers.TanhLayer(HIDDENLAYER),
       layers.OutputLayer(1),
   ],
   search_method='golden',
   tol = TOL, step = STEP,
   show_epoch=25,
   optimizations=[algorithms.LinearSearch],
)

cgnet.train(x_train, y_train, x_test, y_test, epochs=EPOCHS)

hitmiss_final_A = cgnet.predict(hitmiss_predict).round(ROUND)

with open('network-storage.dill', 'w') as net:
    dill.dumps(net, dill.HIGHEST_PROTOCOL)

#p = pickle.dumps(g, pickle.HIGHEST_PROTOCOL)
print hitmiss_final_A


import dill
import csv
import numpy as np
from sklearn import datasets, preprocessing
from sklearn.cross_validation import train_test_split
from neupy import algorithms, layers
from neupy.functions import rmsle

np.random.seed(0)

#variables
EPOCHS = 2000
HIDDENLAYER = 17
miss = 0.1
hit = 0.2
TRAIN = 0.7
ROUND = 2
STEP = 0.003
TOL = 0.02
with open('binary_conversion_dataset_input_2.csv','r') as dest1_f:
    data_iter = csv.reader(dest1_f,
                           delimiter = ',',
                           quotechar = '"')
    data = [data for data in data_iter]
data_array1 = np.asarray(data, dtype = float)
hitmiss_in = data_array1    #loads entire dataset from excel csv file

with open('binary_conversion_dataset_target_2.csv','r') as dest2_f:
    data_iter = csv.reader(dest2_f,
                           delimiter = ',',
                           quotechar = '"')
    data = [data for data in data_iter]
data_array2 = np.asarray(data, dtype = float)
hitmiss_target = data_array2    #loads entire dataset from excel csv file




hitmiss_input = hitmiss_in[:,:]    

hitmiss_target = hitmiss_target[:,:]    


hitmiss_predict = [0.53, 0.80, 0.40, 0.20, 0.07]

#####break target set into single numbers
hitmiss_target1a = hitmiss_target[:,0]
hitmiss_target1b = hitmiss_target[:,1]
hitmiss_target1c = hitmiss_target[:,2]
hitmiss_target1d = hitmiss_target[:,3]
hitmiss_target1e = hitmiss_target[:,4]


###Neural Network

x_train, x_test, y_train, y_test = train_test_split(
   hitmiss_input, hitmiss_target1a, train_size=TRAIN
   )

with open('network-storage.dill', 'r') as f:
    cgnet = dill.load(f)



hitmiss_final_A = cgnet.predict(hitmiss_predict).round(ROUND)

print hitmiss_final_A

產生的錯誤是：

Traceback (most recent call last):
  File "C:\Python27\save network script.py", line 171, in <module>
    cgnet = dill.load(f)
  File "C:\Python27\lib\site-packages\dill\dill.py", line 128, in load
    obj = pik.load()
  File "C:\Python27\lib\pickle.py", line 858, in load
    dispatch[key](self)
  File "C:\Python27\lib\pickle.py", line 880, in load_eof
    raise EOFError
EOFError

我選擇的變量表示法是否可能導致它多次循環導致問題？ 還是可能要存儲很多東西？

Answer 1

你的傾銷線應該像

dill.dump(obj, file)

要么

file.write(dill.dumps(...))

dumps返回一個字符串，並且不自行寫入文件。 它保持為空，並且在讀取時會立即收到EOF（文件結尾）錯誤。