line 880, in load_eof raise EOFError EOFError when trying to load using dill

Question

I am fairly new to python and machine learning. I have been using the neupy library to create neural networks successfully. However, now that I have a decent network, I want to save it. The documentation shows how to use the dill library to do this. The network seems to write to the file correctly, but it will not load the file to be used. The code is repeated because I intend to have to separate scripts after the code is correctly implemented. I have copied the code exactly as given (http://neupy.com/docs/storage.html)

My code is :

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

The errors produced are:

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

Is it possible that the variable notation I have chosen is causing it to loop multiple times causing issues? Or is there possibly to much to store?

Answer 1

Your dumping line should be like

dill.dump(obj, file)

Or

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

dumps returns a string and does not write to the file on it's own. It remained empty and when reading it you received an EOF (end of file) error immediately.