非常罕见的主从复制唯一索引列数据重复冲突案例

本文分析了一主两从GreatSQL半同步复制环境中,因unique_checks=OFF、启用Change Buffer且唯一索引页不在Buffer Pool中,导致主库插入重复唯一索引数据,进而引发从库SQL线程中断(错误码1062)的罕见案例。通过binlog解析、源码分析与复现实验,验证了三条件并发触发唯一性失效的机制,并指出GreatSQL 8.4.4已将innodb_change_buffering默认值调整为off以增强稳定性。

问题发生

某银行客户的开发测试环境,使用的是一主两从半同步复制GreatSQL。现场DBA发现一个slave节点的sql线程异常,show slave status检查错误码是1062,检查另一个slave的复制线程状态正常。

GreatSQL > show slave status \G
*************************** 1. row ***************************
               Slave_IO_State: Waiting for source to send event
                        ......
                   Last_Errno: 1062
                   Last_Error: Coordinator stopped because there were error(s) in the worker(s). The most recent failure being: Worker 1 failed executing transaction '1b79061d-71ae-11ef-9633-00163e2cb875:17436241' at master log greatdb-bin.000288, end_log_pos 362599392.. See error log and/or performance_schema.replication_applier_status_by_worker table for more details about this failure or others, if any.
                        ......

检查表performance_schema.replication_applier_status_by_worker显示信息如下:

GreatSQL > SELECT * FROM performance_schema.replication_applier_status_by_worker \G
....
LAST_ERROR_NUMBER: 1062
LAST_ERROR_MESSAGE: Worker 1 failed executing transaction '1b79061d-71ae-11ef-9633-00163e2cb875:17436241' at master log greatdb-bin.000288, end_log_pos 362599392; Could not execute Write_rows event on table xxxx; Duplicate entry 'Ms001-0xx-20230901-1' for key 'fnd_xxxxx_yyy.UK0_fnd_xxxxx_yyy', Error_code: 1062; handler error HA_ERR_FOUND_DUPP_KEY; the event's master log First, end_log_pos 362599392
...

提示表nfesp_fesl1_db_jy.fnd_xxxxx_yyy表的唯一索引UK0_fnd_xxxxx_yyy有重复数据。

检查表nfesp_fesl1_db_jy.fnd_xxxxx_yyy的建表语句:

CREATE TABLE `fnd_xxxxx_yyy` (
  `IvsmPd_ECD` varchar(80) COLLATE utf8mb4_bin NOT NULL COMMENT '产品编码',
  `Txn_Mkt_ID` char(19) COLLATE utf8mb4_bin NOT NULL COMMENT '市场编号',
  `FndCo_Agnc_Sale_InsID` varchar(9) COLLATE utf8mb4_bin NOT NULL COMMENT '代理销售商代码',
  `Fnd_Qtn_Dt` char(8) COLLATE utf8mb4_bin NOT NULL COMMENT '行情日期',
  `Ext_Fnd_StCd` char(1) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '外部产品状态',
  `NetVal_TpCd` char(1) COLLATE utf8mb4_bin NOT NULL COMMENT '净值类型代码',
  `Unit_Ast_NetVal` decimal(19,8) DEFAULT NULL COMMENT '单位净值',
  `AcNetVal` decimal(19,8) DEFAULT NULL COMMENT '累计净值',
  `Each_TenThsd_Pft_Amt` decimal(10,6) DEFAULT NULL COMMENT '每万份收益额',
  `InBkMt_BdYdRto_ECD` char(10) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '收益率类型',
  `Avrg_Exp_YldRto` decimal(19,8) DEFAULT NULL COMMENT '收益率值',
  `Fnd_Co_Fdbk_Tot_Cps` decimal(19,2) DEFAULT NULL COMMENT '反馈总份数',
  `Fnd_Co_Anc_Fnd_Sz` decimal(19,2) DEFAULT NULL COMMENT '发布产品规模',
  `Tot_Pft_Amt` decimal(22,6) DEFAULT NULL COMMENT '当期总收益',
  `Acm_Pft_Amt` decimal(31,16) DEFAULT NULL COMMENT '累计收益',
  `MULTI_TENANCY_ID` char(5) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '#多实体标识',
  `TXN_ITT_CHNL_ID` char(23) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '#发起渠道编号',
  `TXN_ITT_CHNL_CGY_CODE` char(8) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '#发起渠道类别',
  `Crt_Br_ID` char(9) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '创建分行编号',
  `Mdf_Br_ID` char(9) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '变更分行编号',
  `Scr_Crt_InsID` char(9) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '记录创建机构编号',
  `Scr_Mdf_InsID` char(9) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '记录变更机构编号',
  `Scr_Crt_TrID` char(8) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '记录创建柜员编号',
  `Mdf_TrID` char(8) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '记录变更柜员编号',
  `Ahn_TrID` char(8) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '授权柜员编号',
  `Data_Rcd_Crt_Dt` char(8) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '#数据记录创建日期',
  `Rcd_Crt_Tm` char(6) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '记录创建时间',
  `StMd_Dt` char(8) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '状态变更日期',
  `Rcd_Udt_Tm` char(6) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '记录更新时间',
  `Data_Udt_Dt_Tm` datetime(3) DEFAULT CURRENT_TIMESTAMP(3) ON UPDATE CURRENT_TIMESTAMP(3) COMMENT '#数据更新日期时间',
  `SN_Rcd_ID` bigint NOT NULL AUTO_INCREMENT COMMENT '记录序号',
  PRIMARY KEY (`SN_Rcd_ID`),
  UNIQUE KEY `UKQ_fnd_xxxxx_yyy` (`IvsmPd_ECD`,`Txn_Mkt_ID`,`Fnd_Qtn_Dt`,`NetVal_TpCd`)
) ENGINE=InnoDB AUTO_INCREMENT=1714 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin COMMENT='产品日信息';

发现:有唯一索引UK0_fnd_xxxxx_yyy ( IvsmPd_ECD , Txn_Mkt_ID , Fnd_Qtn_Dt , NetVal_TpCd ) ,检查主库中表fnd_xxxxx_yyy的数据,发现SN_Rcd_ID=860和1711的2条数据的唯一索引的列完全相同。

GreatSQL > SELECT IvsmPd_ECD, Txn_Mkt_ID, Fnd_Qtn_Dt, NetVal_TpCd, SN_Rcd_ID FROM gsqldb.fnd_xxxxx_yyy WHERE SN_Rcd_ID in(860,1711);
+------------+------------+------------+-------------+-----------+
| IvsmPd_ECD | Txn_Mkt_ID | Fnd_Qtn_Dt | NetVal_TpCd | SN_Rcd_ID |
+------------+------------+------------+-------------+-----------+
| MS001      | 0XX        | 20230901   | 1           |       860 |
| MS001      | 0XX        | 20230901   | 1           |      1711 |
+------------+------------+------------+-------------+-----------+
1 row in set (0.00 sec)

问题总结

  • 一主两从的复制架构中,nfesp_fesl1_db_jy.fnd_xxxxx_yyy表在主节点中,唯一索引列存在2条重复数据;
  • 主库重复数据的第2条,在一个slave节点上回放时,由于唯一索引重复而无法插入,导致sql_thread停止;
  • 主库重复数据的第2条,在另一个slave上,可以正常回放,唯一索引列同样存在2条重复数据;

问题分析

解析binlog

使用mysqlbinlog解析master节点的binlog文件greatdb-bin.000288,显示该事务是一个insert语句,在事务中,有如下设置会话变量的信息:

SET **@@session.unique_checks=0**/*!*/;

表明在执行该事务时,用户修改了会话级变量unique_checks,禁用了唯一索引的唯一性检查。

分析程序源码

从源码上看,同时满足以下三个条件,可能导致向唯一索引列插入重复值:

  • 禁用唯一索引检查unique_checks=OFF
  • 使用了change buffer
  • 要更新的唯一索引页不在buffer pool中

问题复现

  1. 配置一主两从的复制环境,配置所有节点使用change buffer。
GreatSQL > SHOW VARIABLES LIKE 'unique%';
+---------------+-------+
| Variable_name | Value |
+---------------+-------+
| unique_checks | ON    |
+---------------+-------+
1 row in set (0.01 sec)

GreatSQL > SHOW VARIABLES LIKE 'innodb_change_buffering';
+-------------------------+-------+
| Variable_name           | Value |
+-------------------------+-------+
| innodb_change_buffering | all   |
+-------------------------+-------+
1 row in set (0.01 sec)
  1. 创建测试表sbtest_uq,在(c,pad)上创建唯一索引。

    CREATE TABLE `sbtest_uq` (
      `id` int NOT NULL AUTO_INCREMENT,
      `k` int NOT NULL DEFAULT '0',
      `c` char(120) NOT NULL DEFAULT '',
      `pad` char(60) NOT NULL DEFAULT '',
      PRIMARY KEY (`id`),
      KEY `k_1` (`k`),
      unique key `uk_c_pad`(`c`,`pad`)
    ) ENGINE=InnoDB AUTO_INCREMENT=1000001 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci
    ;
    
  2. 在sbtest_uq中插入唯一索引列(c,pad)=‘1’,'1’的一条数据(id=1)。

GreatSQL > TRUNCATE TABLE sbtest_uq;
Query OK, 0 rows affected (0.10 sec)
-- 插入测试数据
GreatSQL > insert into sbtest_uq(id,k,c,pad) values(1,1,'1','1');
Query OK, 1 row affected (0.01 sec)
-- 插入其他数据
GreatSQL > insert into sbtest_uq select * from sbtest1 where id>1;
Query OK, 999999 rows affected (55.99 sec)
Records: 999999  Duplicates: 0  Warnings: 0
  1. 在master节点,执行其他DML操作,将sbtest_uq从buffer pool中挤出;也可以在innodb_buffer_pool_load_at_startup=off下重启实例。
-- 执行其他操作,将sbtest_uq从buffer pool中挤出 
-- 或者 在innodb_buffer_pool_load_at_startup=off下重启实例
GreatSQL > TRUNCATE TABLE sbtest12;
Query OK, 0 rows affected (0.51 sec)

GreatSQL > insert into sbtest12 SELECT * FROM sbtest2;
Query OK, 1000000 rows affected (28.36 sec)
Records: 1000000  Duplicates: 0  Warnings: 0

-- 检查buffer pool中已没有sbtest_uq
GreatSQL > SELECT * FROM sys.innodb_buffer_stats_by_table WHERE object_schema='test' and object_name='sbtest_uq';
Empty set (0.26 sec)
  1. 在slave节点1,确认buffer pool中没有表sbtest_uq的数据。
-- 检查buffer pool中已没有sbtest_uq
GreatSQL > SELECT * FROM sys.innodb_buffer_stats_by_table WHERE object_schema='test' and object_name='sbtest_uq';
Empty set (0.26 sec)
  1. 在slave节点2,将sbtest_uq的索引数据加载到buffer pool中。
GreatSQL > SELECT * FROM sbtest_uq WHERE c='1' and pad='1';
+----+---+---+-----+
| id | k | c | pad |
+----+---+---+-----+
|  1 | 1 | 1 | 1   |
+----+---+---+-----+
1 row in set (0.19 sec)

GreatSQL > SELECT * FROM sys.innodb_buffer_stats_by_table WHERE object_schema='test' and object_name='sbtest_uq';
+---------------+-------------+------------+-----------+-------+--------------+-----------+-------------+
| object_schema | object_name | allocated  | data      | pages | pages_hashed | pages_old | rows_cached |
+---------------+-------------+------------+-----------+-------+--------------+-----------+-------------+
| test          | sbtest_uq   | 112.00 KiB | 48.45 KiB |     7 |            0 |         7 |         413 |
+---------------+-------------+------------+-----------+-------+--------------+-----------+-------------+
1 row in set (0.28 sec)
  1. 在master节点插入唯一索引列(c,pad)=‘1’,'1’的另一条数据(id=9999999)。
-- 关闭唯一索引检查
GreatSQL > set unique_checks=OFF;
Query OK, 0 rows affected (0.00 sec)
-- 插入重复数据
GreatSQL > insert into sbtest_uq(id,k,c,pad) values(9999999,1,'1','1'); 
Query OK, 1 row affected (0.01 sec)
  1. 检查master和slave上的数据及复制状态。

master节点:(c,pad)列上已存在重复数据。

-- 查询表sbtest_uq,确认c,pad列上已存在重复数据
GreatSQL > SELECT * FROM sbtest_uq WHERE id in(1,9999999);
+---------+---+---+-----+
| id      | k | c | pad |
+---------+---+---+-----+
|       1 | 1 | 1 | 1   |
| 9999999 | 1 | 1 | 1   |
+---------+---+---+-----+
2 rows in set (0.00 sec)

slave节点1:(c,pad)列上已存在重复数据;复制线程Slave_SQL_Running=Yes

GreatSQL > SELECT * FROM sbtest_uq WHERE id in(1,9999999);
+---------+---+---+-----+
| id      | k | c | pad |
+---------+---+---+-----+
|       1 | 1 | 1 | 1   |
| 9999999 | 1 | 1 | 1   |
+---------+---+---+-----+
2 rows in set (0.07 sec)

GreatSQL > show slave status\G
*************************** 1. row ***************************
               Slave_IO_State: Waiting for source to send event
                    。。。。。。
             Slave_IO_Running: Yes
            **Slave_SQL_Running: Yes**
                    。。。。。。
           Retrieved_Gtid_Set: e6ea0eeb-591c-11ef-8984-00163e919d0a:209358-209371
            Executed_Gtid_Set: e6cbf195-591c-11ef-99d9-00163e33cf00:1-1572367,
e6d361d5-591c-11ef-b1d5-00163e0ea4a5:1-138208,
e6ea0eeb-591c-11ef-8984-00163e919d0a:1-209371
                    ......
1 row in set, 1 warning (0.00 sec)

slave节点2:(c,pad)列上没有重复数据;复制线程Slave_SQL_Running=No,提示唯一索引重复。

GreatSQL > SELECT * FROM sbtest_uq WHERE id in(1,9999999);
+----+---+---+-----+
| id | k | c | pad |
+----+---+---+-----+
|  1 | 1 | 1 | 1   |
+----+---+---+-----+
1 row in set (0.00 sec)

GreatSQL > show slave status\G
*************************** 1. row ***************************
               Slave_IO_State: Waiting for source to send event
                        ......
             Slave_IO_Running: Yes
            **Slave_SQL_Running: No**
                        ......
                   Last_Errno: 1062
                   Last_Error: Coordinator stopped because there were error(s) in the worker(s). The most recent failure being: Worker 1 failed executing transaction 'e6ea0eeb-591c-11ef-8984-00163e919d0a:209371' at master log greatdb-bin.000020, end_log_pos 606. See error log and/or performance_schema.replication_applier_status_by_worker table for more details about this failure or others, if any.
                        ......  
               Last_SQL_Errno: 1062
               Last_SQL_Error: Coordinator stopped because there were error(s) in the worker(s). The most recent failure being: Worker 1 failed executing transaction 'e6ea0eeb-591c-11ef-8984-00163e919d0a:209371' at master log greatdb-bin.000020, end_log_pos 606. See error log and/or performance_schema.replication_applier_status_by_worker table for more details about this failure or others, if any.
                        ......
           Retrieved_Gtid_Set: e6ea0eeb-591c-11ef-8984-00163e919d0a:209366-209371
            Executed_Gtid_Set: e6cbf195-591c-11ef-99d9-00163e33cf00:1-1572367,
e6d361d5-591c-11ef-b1d5-00163e0ea4a5:1-138208,
e6ea0eeb-591c-11ef-8984-00163e919d0a:1-209370
                        ......  
1 row in set, 1 warning (0.00 sec)


GreatSQL > SELECT * FROM performance_schema.replication_applier_status_by_worker\G
*************************** 1. row ***************************
                                           CHANNEL_NAME: 
                                              WORKER_ID: 1
                                              THREAD_ID: NULL
                                          SERVICE_STATE: OFF
                                      LAST_ERROR_NUMBER: 1062
                                     LAST_ERROR_MESSAGE: Worker 1 failed executing transaction 'e6ea0eeb-591c-11ef-8984-00163e919d0a:209371' at master log greatdb-bin.000020, end_log_pos 606; Could not execute Write_rows event on table test.sbtest_uq; Duplicate entry '1-1' for key 'sbtest_uq.uk_c_pad', Error_code: 1062; handler error HA_ERR_FOUND_DUPP_KEY; the event's master log greatdb-bin.000019, end_log_pos 606
                                   LAST_ERROR_TIMESTAMP: 2026-09-14 09:35:47.947584
                               LAST_APPLIED_TRANSACTION: e6ea0eeb-591c-11ef-8984-00163e919d0a:209370
     LAST_APPLIED_TRANSACTION_ORIGINAL_COMMIT_TIMESTAMP: 2026-09-14 09:31:27.529353
    LAST_APPLIED_TRANSACTION_IMMEDIATE_COMMIT_TIMESTAMP: 2026-09-14 09:31:27.529353
         LAST_APPLIED_TRANSACTION_START_APPLY_TIMESTAMP: 2026-09-14 09:31:28.832359
           LAST_APPLIED_TRANSACTION_END_APPLY_TIMESTAMP: 2026-09-14 09:31:55.294233
                                   APPLYING_TRANSACTION: e6ea0eeb-591c-11ef-8984-00163e919d0a:209371
         APPLYING_TRANSACTION_ORIGINAL_COMMIT_TIMESTAMP: 2026-09-14 09:35:47.858275
        APPLYING_TRANSACTION_IMMEDIATE_COMMIT_TIMESTAMP: 2026-09-14 09:35:47.858275
             APPLYING_TRANSACTION_START_APPLY_TIMESTAMP: 2026-09-14 09:35:47.861972
                 LAST_APPLIED_TRANSACTION_RETRIES_COUNT: 0
   LAST_APPLIED_TRANSACTION_LAST_TRANSIENT_ERROR_NUMBER: 0
  LAST_APPLIED_TRANSACTION_LAST_TRANSIENT_ERROR_MESSAGE: 
LAST_APPLIED_TRANSACTION_LAST_TRANSIENT_ERROR_TIMESTAMP: 0000-00-00 00:00:00.000000
                     APPLYING_TRANSACTION_RETRIES_COUNT: 0
       APPLYING_TRANSACTION_LAST_TRANSIENT_ERROR_NUMBER: 0
      APPLYING_TRANSACTION_LAST_TRANSIENT_ERROR_MESSAGE: 
    APPLYING_TRANSACTION_LAST_TRANSIENT_ERROR_TIMESTAMP: 0000-00-00 00:00:00.000000

小结

测试过程验证了在innodb_change_buffering=all,unique_checks=OFF时,唯一索引列中存在插入重复数据可能。在主从复制的slave节点,如果buffer pool中没有唯一索引信息,该事务会正常回放,插入重复数据;如果buffer pool中存在唯一索引的信息,该事务回放时,会由于唯一索引重复而失败,从而导致sql线程中断。

对于客户的问题,由于buffer pool中没有索引UK0_fnd_xxxxx_yyy的信息,所以在master节点能够插入重复数据;但在slave端回放binlog时,如果索引UK0_fnd_xxxxx_yyy的信息在buffer pool中,可以正常插入,但唯一索引列有重复值;如果索引UK0_fnd_xxxxx_yyy的信息在buffer pool中,会由于唯一索引重复从而导致sql线程中断。

总结

经实验验证,当同时满足以下三个条件时,存在向唯一索引列插入重复值的风险:

  • 禁用了唯一性检查(unique_checks=OFF)
  • 启用了变更缓冲(Change Buffer)
  • 待更新的唯一索引不在缓冲池(Buffer Pool)中

通常情况下,应用系统禁用唯一性检查(unique_checks=OFF)是为了提升数据加载速度。但此操作的前提是必须确保唯一索引列的数据绝对唯一;若无法保证,请务必不要禁用该检查,否则极易导致数据重复,进而威胁业务系统的正常运行。在主从复制架构中,这还可能导致从库(Slave)的 SQL 线程中断。此外,在导入 mysqldump 导出文件时需特别留意:默认配置下 mysqldump 会自动在文件中添加 unique_checks=0,若未加注意,极易造成唯一索引列的数据重复。

在 GreatSQL 8.4.4 版本中,innodb_change_buffering 的默认值已调整为 none。这主要是考虑到在现代 SSD 硬件环境下,变更缓冲(Change Buffer)带来的性能收益已有所下降。将该默认值设为 none,不仅能提升数据库开箱即用的稳定性和可控性,更能有效避免在 unique_checks=OFF 时向唯一索引插入重复值的问题。

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")
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