对数据集的shuffle处理需要设置相应的buffer_size参数，相当于需要将相应数目的样本读入内存，且这部分内存会在训练过程中一直保持占用。完全的shuffle需要将整个数据集读入内存，这在大规模数据集的情况下是不现实的，故需要结合设备内存以及Batch大小将TFRecord文件随机划分为多个子文件，再对数据集做local shuffle（即设置相对较小的buffer_size，不小于单个子文件的样本数）。

Shuffle和划分

下文以一个异常检测数据集（正负样本不平衡）为例，在生成第一批TFRecord时，我将正负样本分别写入单独的TFrecord文件以备后续在对正负样本有不同处理策略的情况下无需再解析example_proto。比如在以下代码中，我对正负样本有不同的验证集比例，并将他们写入不同的验证集文件。

import numpy as np
import tensorflow as tf
from tqdm.notebook import tqdm as tqdm

# TFRecord划分
raw_normal_dataset = tf.data.TFRecordDataset("normal_16_256.tfrecords","GZIP")
raw_anomaly_dataset = tf.data.TFRecordDataset("anomaly_16_256.tfrecords","GZIP")
normal_val_writer = tf.io.TFRecordWriter(r'ex_1/'+'normal_val_16_256.tfrecords',"GZIP")
anomaly_val_writer = tf.io.TFRecordWriter(r'ex_1/'+'anomaly_val_16_256.tfrecords',"GZIP")
train_writer_list = [tf.io.TFRecordWriter(r'ex_1/'+'train_16_256_{}.tfrecords'.format(i),"GZIP") for i in range(SUBFILE_NUM+1)]
with tqdm(total=LEN_NORMAL_DATASET+LEN_ANOMALY_DATASET) as pbar:
    for example_proto in raw_normal_dataset:
        # 划分训练集和测试集
        if np.random.random() > 0.99: # 正样本测试集的比例
            normal_val_writer.write(example_proto.numpy())
        else:
            train_writer_list[np.random.randint(0,SUBFILE_NUM+1)].write(example_proto.numpy())
        pbar.update(1)

    for example_proto in raw_anomaly_dataset:
        # 划分训练集和测试集
        if np.random.random() > 0.7: # 负样本测试集的比例
            anomaly_val_writer.write(example_proto.numpy())
        else:
            train_writer_list[np.random.randint(0,SUBFILE_NUM+1)].write(example_proto.numpy())
        pbar.update(1)
normal_val_writer.close()
anomaly_val_writer.close()
for train_writer in train_writer_list:
    train_writer.close()

读取

raw_train_dataset = tf.data.TFRecordDataset([r'ex_1/'+'train_16_256_{}.tfrecords'.format(i) for i in range(SUBFILE_NUM+1)],"GZIP")
raw_train_dataset = raw_train_dataset.shuffle(buffer_size=100000).batch(BATCH_SIZE)
parsed_train_dataset = raw_train_dataset.map(map_func=map_func)

raw_normal_val_dataset = tf.data.TFRecordDataset(r'ex_1/'+'normal_val_16_256.tfrecords',"GZIP")
raw_anomaly_val_dataset = tf.data.TFRecordDataset(r'ex_1/'+'anomaly_val_16_256.tfrecords',"GZIP")
parsed_nomarl_val_dataset = raw_normal_val_dataset.batch(BATCH_SIZE).map(map_func=map_func)
parsed_anomaly_val_dateset = raw_anomaly_val_dataset.batch(BATCH_SIZE).map(map_func=map_func)

标签：TFRecord,Shuffle,读取,val,writer,dataset,raw,train,anomaly
来源： https://www.cnblogs.com/yc0806/p/16526114.html