Deep-Learning-Project/new_unPickle.py at master · novking/Deep-Learning-Project · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Mon Feb 29 14:39:24 2016

@author: yiz613
"""
import pickle
import numpy as np
import gzip


def convertData( inputDataFile, the_relation_file ):

    def load(filename):
        with open(filename, "rb") as f:
            while True:
                try:
                    yield pickle.load(f)
                except EOFError:
                    break


    items = load(inputDataFile)

    unpickleList=list(items)
    inputMatrix=list()


    for eachSample in unpickleList:

        inputMatrix.append(eachSample[:])

    lengthOfInput = len(inputMatrix[0])

    relation = load(the_relation_file)
    targetResult = next(relation)
    #targetResultNDarray = np.array(targetResult, dtype=np.int64)


    inputMatrixNDarray = np.array(inputMatrix, dtype=np.float64)
    targetResultNDarray = np.array(targetResult, dtype=np.int64)

    theanoMatchedInput = (inputMatrixNDarray, targetResultNDarray)
    return (theanoMatchedInput, lengthOfInput)

#
#    for eachSample in unpickleList:
#        targetResult.append(eachSample[0])
#        inputMatrix.append(eachSample[1:])
#
#    lengthOfInput = len(inputMatrix[0])
#
#
#    targetResultNDarray = np.array(targetResult, dtype=np.int64)
#
#
#    inputMatrixNDarray = np.array(inputMatrix, dtype=np.float64)
#
#
#    theanoMatchedInput = (inputMatrixNDarray, targetResultNDarray)
#    return (theanoMatchedInput, lengthOfInput)


def mnistCompare (inputFile):
    with gzip.open(inputFile, 'rb') as f:
        a = pickle.load(f, encoding='latin1')
    return a

if __name__ == "__main__":
     a,b = convertData('valid','valid_relation')
     print (b)
     #c = mnistCompare ('mnist.pkl.gz')