【文件系统】文件系统中的数据结构

文件系统--底层架构(图文详解) 一篇文章带你彻底了解文件系统的底层原理! 阅读详情

目录

 

0.参考

1.文件系统的主要数据结构

2.超级块及其操作函数

3.inode及其操作函数

4.目录项数据结构定义及操作函数

5.文件结构体的定义及文件的操作函数


0.参考

推荐老师及视频:
西安邮电大学研究生导师陈莉君老师《高级操作系统》,可以上B站搜索教学视频,为防止被删,可以缓存.

推荐ups主:善不外来名不虚传

清华大学《高级操作系统》视频,陈渝老师在2020年2-5月录了这个视频

推荐书目:
《LINUX内核设计与实现》第二版,第三版


推荐链接:
内容摘选自
https://www.cnblogs.com/smartjourneys/p/7258226.html--文件系统1
https://www.cnblogs.com/smartjourneys/p/7260911.html--文件系统2
https://blog.youkuaiyun.com/yunsongice/article/details/5683859
https://blog.youkuaiyun.com/GerryLee93/article/details/106476030 --哈希链表

(以上感恩)

1.文件系统的主要数据结构

文件系统本身的数据结构
Linux内核采用VFS(Virtual Filesystem Switch)框架
每个文件系统都需要在VFS中注册,即填充一个struct file_system_type结构
此结构描述的是文件系统的名称以及一个指向对应VFS超级块读取例程的地址等信息

文件系统宏观上的两个数据结构:
(1)struct file_system_type
(2)struct vfsmount


文件系统的四个主要对象对应的数据结构
(1)超级块对象   存放系统中已安装文件系统的信息
(2)索引节点对象 存放关于具体文件的一般信息
(3)目录项对象   存放目录项与对应文件进行链接的信息
(4)文件对象     存放打开文件与进程之间进行交互的有关信息



文件系统宏观上的两个数据结构:
(1)
struct file_system_type {

	const char *name;
	int fs_flags;
	struct super_block *(*read_super) (struct super_block *, void *, int);//ext2_read_super读取超级块,一般是第一个块
	struct module *owner;
	struct file_system_type * next;
	struct list_head fs_supers;

};

5.8.13中这么定义:
struct file_system_type {
	const char *name;
	int fs_flags;
#define FS_REQUIRES_DEV		1 
#define FS_BINARY_MOUNTDATA	2
#define FS_HAS_SUBTYPE		4
#define FS_USERNS_MOUNT		8	/* Can be mounted by userns root */
#define FS_DISALLOW_NOTIFY_PERM	16	/* Disable fanotify permission events */
#define FS_RENAME_DOES_D_MOVE	32768	/* FS will handle d_move() during rename() internally. */
	int (*init_fs_context)(struct fs_context *);
	const struct fs_parameter_spec *parameters;
	struct dentry *(*mount) (struct file_system_type *, int,const char *, void *);
	void (*kill_sb) (struct super_block *);
	struct module *owner;
	struct file_system_type * next;
	struct hlist_head fs_supers;

	struct lock_class_key s_lock_key;
	struct lock_class_key s_umount_key;
	struct lock_class_key s_vfs_rename_key;
	struct lock_class_key s_writers_key[SB_FREEZE_LEVELS];

	struct lock_class_key i_lock_key;
	struct lock_class_key i_mutex_key;
	struct lock_class_key i_mutex_dir_key;
};

这相当于一个文件系统的类,比如会有各种文件系统,比如说ext2文件系统,xfs文件系统,drm文件系统
等系统.这样在去实例化的时候,相当于定义了一个类的一个对象,每一个文件系统都是一个对象,对象
与对象之间通过指针串起来.这里的链表是单向链表,可能是不经常添加或者删除文件系统吧.

(2)还有一个与文件系统本身相关的数据结构:struct vfsmout,在5.8.13中是这么定义的:
struct vfsmount {
	struct dentry *mnt_root;	/* root of the mounted tree */
	struct super_block *mnt_sb;	/* pointer to superblock */
	int mnt_flags;
} __randomize_layout;


D:\005-代码\001-开源项目源码\004-内核源码\linux-2.6.11.10\include\linux\mount.h中是这么定义的:
struct vfsmount
{
	struct list_head mnt_hash;
	struct vfsmount *mnt_parent;	/* fs we are mounted on */
	struct dentry *mnt_mountpoint;	/* dentry of mountpoint */
	struct dentry *mnt_root;	/* root of the mounted tree */
	struct super_block *mnt_sb;	/* pointer to superblock */
	struct list_head mnt_mounts;	/* list of children, anchored here */
	struct list_head mnt_child;	/* and going through their mnt_child */
	atomic_t mnt_count;
	int mnt_flags;
	int mnt_expiry_mark;		/* true if marked for expiry */
	char *mnt_devname;		/* Name of device e.g. /dev/dsk/hda1 */
	struct list_head mnt_list;
	struct list_head mnt_fslink;	/* link in fs-specific expiry list */
	struct namespace *mnt_namespace; /* containing namespace */
};



每个文件系统都有一个全局file_system_type类型的全局变量,例如:ext2_fs_type代表ext2文件系统
mount的时候会调用read_super.



 

 

2.超级块及其操作函数

超级块用来描述整个文件系统的信息
每个具体的文件系统都有自己的超级块
VFS超级块是各种文件系统在安装时建立的,并在卸载时被自动删除,其数据结构是super_block
所有超级块对象都以双向循环链表的形式链接在一起
super_block的数据结构,摘自5.8.13版本的内核
D:\005-代码\001-开源项目源码\004-内核源码\linux-5.8.13\linux-5.8.13\include\linux\fs.h
struct super_block {
	struct list_head	s_list;		/* Keep this first */
	dev_t			s_dev;		/* search index; _not_ kdev_t */
	unsigned char		s_blocksize_bits;
	unsigned long		s_blocksize;
	loff_t			s_maxbytes;	/* Max file size */
	struct file_system_type	*s_type;
	const struct super_operations	*s_op;
	const struct dquot_operations	*dq_op;
	const struct quotactl_ops	*s_qcop;
	const struct export_operations *s_export_op;
	unsigned long		s_flags;
	unsigned long		s_iflags;	/* internal SB_I_* flags */
	unsigned long		s_magic;
	struct dentry		*s_root;
	struct rw_semaphore	s_umount;
	int			s_count;
	atomic_t		s_active;
#ifdef CONFIG_SECURITY
	void                    *s_security;
#endif
	const struct xattr_handler **s_xattr;
#ifdef CONFIG_FS_ENCRYPTION
	const struct fscrypt_operations	*s_cop;
	struct key		*s_master_keys; /* master crypto keys in use */
#endif
#ifdef CONFIG_FS_VERITY
	const struct fsverity_operations *s_vop;
#endif
	struct hlist_bl_head	s_roots;	/* alternate root dentries for NFS */
	struct list_head	s_mounts;	/* list of mounts; _not_ for fs use */
	struct block_device	*s_bdev;
	struct backing_dev_info *s_bdi;
	struct mtd_info		*s_mtd;
	struct hlist_node	s_instances;
	unsigned int		s_quota_types;	/* Bitmask of supported quota types */
	struct quota_info	s_dquot;	/* Diskquota specific options */

	struct sb_writers	s_writers;

	/*
	 * Keep s_fs_info, s_time_gran, s_fsnotify_mask, and
	 * s_fsnotify_marks together for cache efficiency. They are frequently
	 * accessed and rarely modified.
	 */
	void			*s_fs_info;	/* Filesystem private info */

	/* Granularity of c/m/atime in ns (cannot be worse than a second) */
	u32			s_time_gran;
	/* Time limits for c/m/atime in seconds */
	time64_t		   s_time_min;
	time64_t		   s_time_max;
#ifdef CONFIG_FSNOTIFY
	__u32			s_fsnotify_mask;
	struct fsnotify_mark_connector __rcu	*s_fsnotify_marks;
#endif

	char			s_id[32];	/* Informational name */
	uuid_t			s_uuid;		/* UUID */

	unsigned int		s_max_links;
	fmode_t			s_mode;

	/*
	 * The next field is for VFS *only*. No filesystems have any business
	 * even looking at it. You had been warned.
	 */
	struct mutex s_vfs_rename_mutex;	/* Kludge */

	/*
	 * Filesystem subtype.  If non-empty the filesystem type field
	 * in /proc/mounts will be "type.subtype"
	 */
	const char *s_subtype;

	const struct dentry_operations *s_d_op; /* default d_op for dentries */

	/*
	 * Saved pool identifier for cleancache (-1 means none)
	 */
	int cleancache_poolid;

	struct shrinker s_shrink;	/* per-sb shrinker handle */

	/* Number of inodes with nlink == 0 but still referenced */
	atomic_long_t s_remove_count;

	/* Pending fsnotify inode refs */
	atomic_long_t s_fsnotify_inode_refs;

	/* Being remounted read-only */
	int s_readonly_remount;

	/* per-sb errseq_t for reporting writeback errors via syncfs */
	errseq_t s_wb_err;

	/* AIO completions deferred from interrupt context */
	struct workqueue_struct *s_dio_done_wq;
	struct hlist_head s_pins;

	/*
	 * Owning user namespace and default context in which to
	 * interpret filesystem uids, gids, quotas, device nodes,
	 * xattrs and security labels.
	 */
	struct user_namespace *s_user_ns;

	/*
	 * The list_lru structure is essentially just a pointer to a table
	 * of per-node lru lists, each of which has its own spinlock.
	 * There is no need to put them into separate cachelines.
	 */
	struct list_lru		s_dentry_lru;
	struct list_lru		s_inode_lru;
	struct rcu_head		rcu;
	struct work_struct	destroy_work;

	struct mutex		s_sync_lock;	/* sync serialisation lock */

	/*
	 * Indicates how deep in a filesystem stack this SB is
	 */
	int s_stack_depth;

	/* s_inode_list_lock protects s_inodes */
	spinlock_t		s_inode_list_lock ____cacheline_aligned_in_smp;
	struct list_head	s_inodes;	/* all inodes */

	spinlock_t		s_inode_wblist_lock;
	struct list_head	s_inodes_wb;	/* writeback inodes */
} __randomize_layout;

 

struct super_operations {
   	struct inode *(*alloc_inode)(struct super_block *sb);
	void (*destroy_inode)(struct inode *);
	void (*free_inode)(struct inode *);

   	void (*dirty_inode) (struct inode *, int flags);
	int (*write_inode) (struct inode *, struct writeback_control *wbc);
	int (*drop_inode) (struct inode *);
	void (*evict_inode) (struct inode *);
	void (*put_super) (struct super_block *);
	int (*sync_fs)(struct super_block *sb, int wait);
	int (*freeze_super) (struct super_block *);
	int (*freeze_fs) (struct super_block *);
	int (*thaw_super) (struct super_block *);
	int (*unfreeze_fs) (struct super_block *);
	int (*statfs) (struct dentry *, struct kstatfs *);
	int (*remount_fs) (struct super_block *, int *, char *);
	void (*umount_begin) (struct super_block *);

	int (*show_options)(struct seq_file *, struct dentry *);
	int (*show_devname)(struct seq_file *, struct dentry *);
	int (*show_path)(struct seq_file *, struct dentry *);
	int (*show_stats)(struct seq_file *, struct dentry *);
#ifdef CONFIG_QUOTA
	ssize_t (*quota_read)(struct super_block *, int, char *, size_t, loff_t);
	ssize_t (*quota_write)(struct super_block *, int, const char *, size_t, loff_t);
	struct dquot **(*get_dquots)(struct inode *);
#endif
	int (*bdev_try_to_free_page)(struct super_block*, struct page*, gfp_t);
	long (*nr_cached_objects)(struct super_block *,
				  struct shrink_control *);
	long (*free_cached_objects)(struct super_block *,
				    struct shrink_control *);
};

 

 

3.inode及其操作函数

 

struct inode_operations {
	struct dentry * (*lookup) (struct inode *,struct dentry *, unsigned int);
	const char * (*get_link) (struct dentry *, struct inode *, struct delayed_call *);
	int (*permission) (struct inode *, int);
	struct posix_acl * (*get_acl)(struct inode *, int);

	int (*readlink) (struct dentry *, char __user *,int);

	int (*create) (struct inode *,struct dentry *, umode_t, bool);
	int (*link) (struct dentry *,struct inode *,struct dentry *);
	int (*unlink) (struct inode *,struct dentry *);
	int (*symlink) (struct inode *,struct dentry *,const char *);
	int (*mkdir) (struct inode *,struct dentry *,umode_t);
	int (*rmdir) (struct inode *,struct dentry *);
	int (*mknod) (struct inode *,struct dentry *,umode_t,dev_t);
	int (*rename) (struct inode *, struct dentry *,
			struct inode *, struct dentry *, unsigned int);
	int (*setattr) (struct dentry *, struct iattr *);
	int (*getattr) (const struct path *, struct kstat *, u32, unsigned int);
	ssize_t (*listxattr) (struct dentry *, char *, size_t);
	int (*fiemap)(struct inode *, struct fiemap_extent_info *, u64 start,
		      u64 len);
	int (*update_time)(struct inode *, struct timespec64 *, int);
	int (*atomic_open)(struct inode *, struct dentry *,
			   struct file *, unsigned open_flag,
			   umode_t create_mode);
	int (*tmpfile) (struct inode *, struct dentry *, umode_t);
	int (*set_acl)(struct inode *, struct posix_acl *, int);
} ____cacheline_aligned;

 

 

 

4.目录项数据结构定义及操作函数

 

 

5.文件结构体的定义及文件的操作函数

struct file_operations {
	struct module *owner;
	loff_t (*llseek) (struct file *, loff_t, int);
	ssize_t (*read) (struct file *, char __user *, size_t, loff_t *);
	ssize_t (*write) (struct file *, const char __user *, size_t, loff_t *);
	ssize_t (*read_iter) (struct kiocb *, struct iov_iter *);
	ssize_t (*write_iter) (struct kiocb *, struct iov_iter *);
	int (*iopoll)(struct kiocb *kiocb, bool spin);
	int (*iterate) (struct file *, struct dir_context *);
	int (*iterate_shared) (struct file *, struct dir_context *);
	__poll_t (*poll) (struct file *, struct poll_table_struct *);
	long (*unlocked_ioctl) (struct file *, unsigned int, unsigned long);
	long (*compat_ioctl) (struct file *, unsigned int, unsigned long);
	int (*mmap) (struct file *, struct vm_area_struct *);
	unsigned long mmap_supported_flags;
	int (*open) (struct inode *, struct file *);
	int (*flush) (struct file *, fl_owner_t id);
	int (*release) (struct inode *, struct file *);
	int (*fsync) (struct file *, loff_t, loff_t, int datasync);
	int (*fasync) (int, struct file *, int);
	int (*lock) (struct file *, int, struct file_lock *);
	ssize_t (*sendpage) (struct file *, struct page *, int, size_t, loff_t *, int);
	unsigned long (*get_unmapped_area)(struct file *, unsigned long, unsigned long, unsigned long, unsigned long);
	int (*check_flags)(int);
	int (*flock) (struct file *, int, struct file_lock *);
	ssize_t (*splice_write)(struct pipe_inode_info *, struct file *, loff_t *, size_t, unsigned int);
	ssize_t (*splice_read)(struct file *, loff_t *, struct pipe_inode_info *, size_t, unsigned int);
	int (*setlease)(struct file *, long, struct file_lock **, void **);
	long (*fallocate)(struct file *file, int mode, loff_t offset,
			  loff_t len);
	void (*show_fdinfo)(struct seq_file *m, struct file *f);
#ifndef CONFIG_MMU
	unsigned (*mmap_capabilities)(struct file *);
#endif
	ssize_t (*copy_file_range)(struct file *, loff_t, struct file *,
			loff_t, size_t, unsigned int);
	loff_t (*remap_file_range)(struct file *file_in, loff_t pos_in,
				   struct file *file_out, loff_t pos_out,
				   loff_t len, unsigned int remap_flags);
	int (*fadvise)(struct file *, loff_t, loff_t, int);
} __randomize_layout;






 

 

数据结构与算法之B树在文件系统中的作用 本文旨在全面剖析B树在文件系统中的作用。通过对B树的原理、算法、数学模型等方面的深入研究,结合文件系统的特点和需求,揭示B树如何优化文件系统的性能。范围涵盖了B树的基本概念、核心算法、在文件系统中的具体应用案例,以及相关的工具和资源推荐。本文首先介绍B树和文件系统的基本概念,为后续的深入分析奠定基础。接着详细阐述B树的核心算法原理,并给出Python代码实现。通过数学模型和公式进一步分析B树的性能。在项目实战部分,展示B树在文件系统中的具体应用。然后列举实际应用场景,推荐相关的工具和资源。 阅读详情

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MHD0815的博客 266

深度学习工具包 DeepLearningToolbox

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]); 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概念 VFS(Virtual Filesystem Switch)称为虚拟文件系统或虚拟文件系统转换,是一个内核软件层,在具体的文件系统之上抽象的一层,用来处理与Posix文件系统相关的所有调用,表现为能够给各种文件系统提供一个通用的接口,使上层的应用程序能够使用通用的接口访问不同文件系统,同时也为不同文件系统的通信提供了媒介。 架构 VFS在整个Linux系统中的架构视图如下: Linu...

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虚拟文件系统中的数据结构(fs_struct、files_struct) http://xd03071149.blog.163.com/blog/static/1235063632012912114

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