Python 如何修复验证步骤中使用的代码？

当我训练Resnet时，洗牌验证集的精度不等于非洗牌版本的精度。 训练集以同样的方式洗牌

我试着每次使用一个样本向网络提供信息，在vali集合中获得了相同的准确性。但是，当我每次使用一批样本时，比如说256个样本，精度是不相等的

培训过程

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从理论上讲，vali集合的顺序不会影响结果。

我假设您使用的是退出或批量标准化。在这两种情况下，您都需要在推理过程中向训练参数提供bool-tensorflow变量。由于顺序似乎会影响结果，因此我将假设批量标准化：
.
feed training=False，带有tensorflow占位符

count = 1 
while 1:
    
    #get training_data
    if (count - 1) % 5200 == 0:
        train_set,train_label = get_traindata(eng, 70)
        
    tset,tlabel = train_set,train_label
    tset_num = tset.shape[0]
    
    #split and train
    num_batches = tset_num // batch_size
    order = np.random.choice(tset_num, num_batches * batch_size,replace = False)
    train_data_subset = tset[order,:]
    train_labels_subset = tlabel[order]

    for step in range(num_batches):
           
        offset = step * validation_size
        feed_dict = {
                    x: train_data_subset[offset:offset+batch_size,:],
                    y: train_labels_subset[offset:offset+batch_size],
                    }
        _, tloss, tacc,gd = sess.run([train_op,loss, accuracy,grad_norm], 
                                     feed_dict=feed_dict)
        if (count-1) % 200 == 0:
            
            
            #get validationset
            if (count-1) % 1400 == 0:
                vset,vlabel = get_validata(eng, 20)
                vnum_batches = vset.shape[0] // validation_size
                order = np.random.choice(vset.shape[0], vnum_batches * validation_size,replace = False)  #shuffled
                va_data_subset = vset[order,:]
                va_labels_subset = vlabel[order]
            
            loss_list = []
            error_list = []
            loss_list_s = []
            error_list_s = []
            for stepv in range(vnum_batches):
               offsetv = stepv * validation_size
               feed_dict_shuffle = {
                    x: va_data_subset[offsetv:offsetv+validation_size,:],
                    y: va_labels_subset[offsetv:offsetv+validation_size],
                    }
               vloss_s, vacc_s = sess.run([loss, accuracy], feed_dict=feed_dict_shuffle)
               loss_list_s.append(vloss_s)
               error_list_s.append(vacc_s)
               feed_dict = {
                    x: vset[offsetv:offsetv+validation_size,:],
                    y: vlabel[offsetv:offsetv+validation_size],
                    }
               vloss, vacc = sess.run([loss, accuracy], feed_dict=feed_dict)
               loss_list.append(vloss)
               error_list.append(vacc)
            #the performance in the shuffled vali-set
            vloss_s = np.mean(loss_list_s)
            vacc_s = np.mean(error_list_s)
            #the performance in the unshuffled vali-set
            vloss = np.mean(loss_list)
            vacc = np.mean(error_list)