Train in utility.py file

def train(train_q,val_q,plotFilePath,ACMNet_object,epoch,lr,batchSize,dropoutRate,choice,max_len) :
    """ train for one epoch
        Parameters
        ----------
            train_q, val_q : list of dictionary
                list of training data, each of which has structure of :
                    {
                        'imdb_key' : string
                        'question' : 2d numpy array (sentence, word_vec)
                        'answers' : 3d numpy array (5, sentence, word_vec)
                        'correct_index' : int
                    }
            plotFilePath : string
                path of plot files in word vector form
            ACMNet_object : object instance
                from class "MODEL" built up by tensorflow
            epoch : int
            lr : float
                learning rate
            batchSize : int
            dropoutRate : float
            choice : int
                number of given choices (default 5)
            max_len : list
                only 3 elements are in the list, 
                ["max length of sentence in plot", "max length of question", "max length of choice"]
    """

    accuracy_list = {'train':[],'val':[]}
    global_step = 0
    filler = [0]*300        

    for i in range(epoch):      

        batchP = []
        batchQ = []
        batchAnsVec = []
        batchAnsOpt = []
        batchcount_epoch = 0
        totalcost_epoch = 0
        for question in train_q:
            imdb_key = plotFilePath+question["imdb_key"]+".split.wiki.json"
            with open(imdb_key) as data_file:
                P = json.load(data_file)
            batchP.append(P) #need check
            batchQ.append(question["question"])
            AnsOption = []

            for j in range(choice):
                if question["answers"][j]:
                    pass
                else:
                    question["answers"][j] = [filler]
                batchAnsVec.append(question["answers"][j])
                AnsOption.append(0)
            AnsOption[question["correct_index"]] = 1
            batchAnsOpt.append(AnsOption)
            cost = 0
            if len(batchP) == batchSize:
                batchcount_epoch+=1
                global_step+=1

                batchP = varSentencePadding(batchP,max_len[0])
                batchQ = batchPadding(batchQ,max_len[1])
                batchAnsVec =  batchPadding(batchAnsVec,max_len[2])

                cost = ACMNet_object.train(batchP,batchQ,batchAnsVec,batchAnsOpt,dropoutRate)

                totalcost_epoch+=cost
                print ('global_step '+str(global_step)+' ,Cost of Epoch ' + str(i) + ' batch ' + str(batchcount_epoch) + ": "+str(cost))

                batchP = []
                batchQ = []
                batchAnsVec = []
                batchAnsOpt = []
                if global_step%200 == 0:
                    accuracy_list['val'].append(test(ACMNet_object,plotFilePath,val_q,batchSize,max_len,choice,True))
                    print('Val Accuracy: ',accuracy_list['val'])
                    if np.argmax(np.asarray(accuracy_list['val'])) == len(accuracy_list['val'])-1:
                        ACMNet_object.save(global_step)
                    if i > 30 and global_step%400 == 0:
                        accuracy_list['train'].append(test(ACMNet_object,plotFilePath,train_q,batchSize,max_len,choice,False))
                    print('Train Accuracy: ',accuracy_list['train'])                                   

    return accuracy_list

fig.1 def train in utility.py file.

Fig.1 shows the flowchart of train function in utility.py file.