basWelcome to:
Linux/Unix Basics for HPC
October 9, 2014
9:00am - 12:00pm
ROC 1.900
Please submit any questions in this window. Thank you.
You may need to mute your computer's mic if you are getting an audio loop or feedback.
TO MAKE THINGS A LITTLE LESS CONFUSING, PLEASE SUBMIT QUESTIONS AT THE BOTTOM OF THE PAGE. THANKS!
Sorry, is read 4, write 2, and execute 1 in the permissions chmod command?
yes.
Thank you.
What is the difference between cat textfile and more textfile?
cat will show all content of the file even the file is large.
more allows for "output" to be viewed one page at a time.
cat allows for concatenation of files and other more complex manipulations of "output".
Thank you.
why some commands work with capitals and small letters, like PWD or pwd?
PWD is an environment variable. You can do echo $PWD or cd $PWD. This means that PWD is a variable that has a value associated with.
pwd is a command
Thank you
so does the command "pwd" dump the absolute path into variable $PWD each time you run it?
Yes, it is automagically updated for you.
Sorry. Figured it out.
How is "ls -d *" different from "ls"?
Great, thanks.
the "-d" is specifying directory only, but then you are asking for all the the asterisk, at least at first glance, they are equivalent. But, I'd just use "ls".
To clarify, "all" in this case means all non-hidden files. If you really want to see every file; hidden and non-hidden, then you can use the "-a", i.e.: "ls -a".
So, !$ works the same way that up arrow?
!$ is the "end" of the previous command. Consider the following example: We start by looking for a word in a file
$ grep -i joe /some/long/directory/structure/user-lists/list-15
if joe is in that userlist, we want to remove him from it.  Expands to the process ID of the most recently executed background (asynchronous) command. (different command -- whoops)
How to end the addition of text to a file while using cat? Is Ctrl+C correct?
cntrl + d
does that help?
OK, it worked
Cool.
Is it possible to show headers? for this output
$ ls -a -s
total 120
0 . 0 .matplotlib
0 .. 0 .ssh
8 .CFUserTextEncoding 0 CytoscapeConfiguration
32 .DS_Store 0 Desktop
8 .Rapp.history 0 Documents
0 .Trash 0 Downloads
0 .allegroviva 0 Google Drive
48 .bash_history 0 Library
8 .bash_profile 0 Movies
8 .bash_profile.macports-saved_2014-07-03_at_15:46:43 0 Music
0 .config 0 Pictures
0 .cups 0 Public
0 .free42 0 VirtualBox VMs
8 .lesshst 0 this-is-a-new+name
0 .local
What do you mean by headers?
Try ls -lsh
-l for long format
-s for size at the beginning
-h for human readable
How do you find the size of a file?
Hint: "man"
e.g. "man ls"
Then type "/" followed by what you are wanting to find, maybe "/size"
You can then cycle through instances of "size" by hitting "n" for next.
My directories seem to have size 4? (instead of 0)
Yep, me too. There is a finite size to store the directory file. I'm going to guess that Victor's size of 0 might be Mac related??
size of 4 refers to block size.  Block size could be 1024 or 4096 depending on the system
The 68 is the size in bytes.
what do the 'pts/## mean?
The pts/0 is telling you which "psuedo terminal" the user is logged in on. In this case it's terminal #0. The "(:0.0)" Tells you which hostname and display you're using.
Is there an option to put the date of users with "who" command
what do you mean by date?
Use "w" instead if you want to see "dates".
for example if I want only to print users that logged in today or from yesterday?
You would need to filter your results from the system logs.
Victor will be talking more about "filters", e.g. sed and awk later on in the talk.
Thanks
Why are his files listed left justified down the page, and mine are listed across?
(For 'ls -F')
(His are vertical and mine are horizontal)
You can list then vertical by doing this:
ls -1F
Sweet, thanks
how do
shall we talk about how to submit a job to cliuster and how to work with various modules like CUDA etc?
Submitting jobs depends on which cluster you are using. Stampede? Or somewhere else?
stampede
How to know more about stampede cluster?
We have all the information in our user guide: https://www.tacc.utexas.edu/user-services/user-guides/stampede-user-guide
you also can get info about cuda on stampede there.
We'll not be covering that in this tutorial.
If you want information regarding job submission: https://www.tacc.utexas.edu/user-services/user-guides/stampede-user-guide#running-slurm-jobcontrol
Do you have to be UT student to access Stampede?
Not required. You can apply allocation on stampede through xsede.https://kb.iu.edu/d/bazs
As a student you probably need to get your professor to grant you access
4 read (r)
2 write (w)
1 execute (x)
Practical Examples
chmod 400 mydoc.txt read by owner
chmod 040 mydoc.txt read by group
chmod 004 mydoc.txt read by anybody (other)
chmod 200 mydoc.txt write by owner
chmod 020 mydoc.txt write by group
chmod 002 mydoc.txt write by anybody
chmod 100 mydoc.txt execute by owner
chmod 010 mydoc.txt execute by group
chmod 001 mydoc.txt execute by anybody
One can also use a different syntax:
chmod u+rw file
that would give read and write permission to a file. Or
chmod g+X file
that would give execution permission to a group (if the file was executable in the first place when using capital X)
Or a combination:
chmod go+rx file
which will give the group and anybody else read and execute permissions
I find this easier to remember
you also can use "-" to remove the permission. Yep, thanks for adding that!
you can use "a" to represent everybody, user, group and others.
would you please explain grep again?
Victor will be talking more on "grep" specifically.
grep will search in a string
that string can be a file, just a string,...
If it finds what it's looking for, it will print it
Thanks
can someone illustrate a line that would be found by e. but not e* or vice versa?
e* will find any character -- let's try a newline character
cproctor@staff:test>cat test
some text
e
Then
cproctor@staff:test>grep e test
some text
e
cproctor@staff:test>grep e. test
some text
cproctor@staff:test>grep e* test
some text
e
So, the last line the has just an "e" and no newline character "\n" -- hidden, is different.
I see. Thank you.
Re: Exercise 16
Why is he using "ls | grep" on the last line?
That's just an example on how to use grep to filter the output given by ls. What's happening there, is that the output of ls is given to grep, which will filter this output.
Is there a difference between "ls | grep "ski*" and "ls ski*"?
i'm just learning too, but I think grep ski* looks for names with any number of s's k's and i's.
"ls ski*" outputs a list of files with name starting with "ski". "ls | grep "ski*"" filters the output of ls. Giving the lines containing "ski".Thanks
what was the web page he just mentioned for sed and awk ?
http://shop.oreilly.com/product/9781565922259.do
Let's say I screwd up "ls". How do I restore it?
?
type "which ls" and tell me what you get.
Well, I have not modified it. I am just wondering
"ls" is an executable that only the root user may change.
If you were to "screw it up", then you'd have a hard time looking around in directories.
Typically "ls" lives in "/bin/ls"
If you really did mess it up or delete, you'd be in trouble
Do I download it from somewhere? Just curious?
You could. It comes by default with any Linux/Unix environment.
So is ls a process or a file?
an executable file.
can you give an example of a process?
type 'ps aux' and you'll get the list of processes currently running
thank you
My work.cmd did not run.
cat work.cmd
#!/bin/bash
echo "It works"
what's the error message?
-bash: work.cmd: command not found
That's because it is not "on the path" so to speak.
You must show linux where it lives.
Using a relative path, you can type:
./work.cmd
that is, if you are in the same directory
Cool, it worked that way
How do I add something to the $PATH simply from the command line (without editing .bashrc)?
export PATH=$PATH:whatever_you_are_adding
interesting, this works well:
"export PATH=$PATH:$PWD"
Sure, you can always use environment variables
Sorry, should we type them in a shell?
we did sth named work.cmd
Not in this case.
so we have to just write in terminal?!
yes, in that particular example. Sorry for the confusion
oh, ok thanks
Do these variables stay forever in the memory?
No. They only exist in the terminal session that you created them in.
To make them persistent, you usually set them in a file that runs when you start a new terminal. Usually ~/.bashrc file.
Sorry, he went through Export fast... what does it do?
Victor didn't have time to discuss "export" in depth.
It is specifically a bash command that 'exports' a variable into the environment.
If you write a variable inside a shell script and you want to reference it outside the
script, then you would use the "export" command so that it is then saved in the environment
instance of that particular terminal session.
Thanks... why do you need
exercise 2 illustrates that.
Does that help?
hmmm... doesn't work
my test file has one line: "export g=4"
then i do:
[]./test
[]echo $g
(blank)
:-) You're right....I should test my example before I write them
export makes the variable available to sub-processes.
That is,
export name=value
means that the variable name is available to any process you run from that shell process. If you want a process to make use of this variable, use export, and run the process from that shell.
name=value
means the variable scope is restricted to the shell, and is not available to any other process. You would use this for (say) loop variables, temporary variables etc.
It's important to note that exporting a variable doesn't make it available to parent processes. That is, specifying and exporting a variable in a spawned process doesn't make it available in the process that launched it.
(yay stackexchange)
I see... so if I put echo $g into the test program, it should work?
Let's see.
Yes.. returns "4"
But it also works if I just have:
g=4
echo $g
So it does not seem to require export, at least within the script
Correct.
But, if your script gave g as an input to a subprocess, then the export becomes important
Got it, thanks!
cproctor@staff:p>cat test
###########
#!/bin/bash
# Add or remove export here
export g=4
echo -e "test $g"
./silly
###########
cproctor@staff:p>cat silly
###########
#!/bin/bash
echo -e "silly $g"
###########
If you make "test" and "silly" files, chmod u+x to them to make them executable,
and then play with export on the "g" variable, you will see that it is passed to the silly
script when "export" is used and it is not passed when "export" is left off.
do these files .bashrc and .profile have to start with #!/bin/bash ?
no. good question though
Is there a way to log out and log in other than closing my terminal window?
You aren't specifically logging in and out by closing the terminal window.
Oh, so how do I log out? and the log in?
When you open a new terminal session, it starts a new process for your user id.
Logging in and out, to me, means logging in and out of either a remote computer
or starting your local computer.
this is a hidden file right?
I just created it using touch .bashrc
and how can I write? Using cat?
cat > .bashrc
yes. It was created by the system. It is a hidden file since it starts with "."
Do these files have to be called .bashrc and .profile ? I used different names because I knew those files existed elsewhere. (hasn't worked yet)
From .profile you can call any other file, with the source command, so you could create a file named 'my_file' and then do 'source my_file' in the .profile file
I made a .profile file that calls my_file, but it no longer calls the original .bashrc (I had some alias's set up there)
what should I add to .profile?
Add Source path/my_file in your .profile
And, you should make sure you also have: source ~/.bashrc somewhere in your .profile
Got it working!
can you please slow it down a little bit?
the amount of material clearly exceeds 3 hours....
maybe next time we'll do a whole day, or turn scripting into a separate section
The best part is that it is recorded.
It will be available online later so that everyone may review and go at their own pace.
Hmmm. How do I pull out the first argument of the three?
$1 represents the first parameter
3=$1
echo $3 <--- This is very confusing to use. How could you get the third argument?
I was wrong.
Yes... would be better to use a letter variable, right?
It is safer to stay away from BASH specific reserved syntax, yes.
you would do
y=$3
echo $y
This is clearer.
nice... ok, i've got
bash-3.2$ ./namum cat pig donkey
There are 3 arguments
The first argument is cat
echo "first arg is $1" worked for me-- without need for defining other variables
why do you need whitespace around the test?
:-) BASH is a fickle language
Spacing is very important
As to why....I don't know :-)
Haha... Guess we have to ask Linus
How bout this, what's the difference between:
if [ $# > 0 ]; then
and
if [ $# -gt 0 ]; then
?
There could be a subtle difference...I'm checking now
I can't come up with an example where they are different.
Found one:
if [ "a" \> "b" ]; then
echo "woo"
fi
You must add the backslash character within a [ ] construct in order to evaluate
the comparison correctly. With the "-gt" you don't need a backslash.
yes, i see... without the \ my namum outputs The first argument is if I in fact give no arguments.
Right, it always evaluates as true without the backslash.
Good session overall. Would cut some into history at the beginning for later shell scripting
Would love to come back for another session just on shell scripting, great job guys!
Thanks for the feedback. We'll consider a shell scripting course
Thank you so much!
Great webinar!
Thanks guys! It was very useful session especially on shell script
Hope we can have a session just for script writing
I missed to follow this part completely
It went so fast!
But overally great job!
The best part is that it is recorded.
It will be available online later so that everyone may review and go at their own pace.
Thanks :)
What's the URL for the recording?
https://www.youtube.com/watch?v=cyINirlJZzk#t=10
Or if you would like the material with it:
https://www.tacc.utexas.edu/user-services/training/linux-unix-basics-for-hpc


--- layout: post permalink: bestofferbuy.html Tags: Array NameOfCoupon: Best Offer Buy Cash Back Summary: See our Best Offer Buy Coupon Code, Best Offer Buy Rebate, Best Offer Buy Promo Code, Best Offer Buy Discount, Best Offer Buy Coupon Cash Back Rebate here. MetaKeywords: Best Offer Buy Coupon Code, Best Offer Buy Rebate, Best Offer Buy Promo Code, Best Offer Buy Cash Back Rebate, Best Offer Buy Discount, Best Offer Buy Coupon Image: images/22334.gif LinkURL: http://www.shareasale.com/r.cfm?b=175567&u=412002&m=22334 affiliate_url_link: http://www.icouponblog.com/jane.php?id=1165 thesis_readmore: See the Best Offer Buy Coupon thesis_thumb: http://www.icouponblog.com/tn.php?merchantURL=bestofferbuy.com Title: Save 3.5% - Best Offer Buy Coupon Code for bestofferbuy.com. Best Offer Buy Rebate & Discount thesis_description: Our Best Offer Buy Discount Coupon Code for bestofferbuy.com. We have the best Best Offer Buy Rebate anywhere. thesis_keywords: Best Offer Buy, Best Offer Buy Coupon Code, Best Offer Buy Discount, Best Offer Buy Rebate product_name: Best Offer Buy hop_url: http://www.icouponblog.com/hop.php?id=1165 ---
Buy the cheapest electronic items, apparels, jewelry pet supplies, sporting goods, surveillance supplies and lots more from allows drop-ship orders for all registered customers with no extra charges. They have direct links with over 100 factories in China so you can get the best discounts. Buy DVDs, pc accessories, MP3s, MP4, cell phones, tools, hardware and tons of other stuff. Visit www.bestofferbuy.com and start shopping now. You will love the products and the prices even more! Our discount coupons will save you money on all items.
Visit www.bestofferbuy.com for the best deals on hundreds of items. You can get competitive wholesale prices. All items are shipped directly from China. www.bestofferbuy.com allows drop-ship orders for all registered customers with no extra charges. Have peace of mind when you shop at www.bestofferbuy.com. Get batteries, chargers, blue tooth, jewelry, earrings, rings, camping accessories, binoculars and lots of other stuff.
It is your direct line to over one hundred factories in China. Get hundreds of items from all categories of products from electronics, jewelry, hobbies, toys, computers, cell phones and many more. Buy products at the cheapest possible rates with direct shipments from China. Buy with confidence and peace of mind. You will get the best products at the best rates. Why pay more, when you can pay less? Our coupons will get you a great price.


blogc-pagination
- blogc's pagination support
blogc(1) supports some basic pagination and post filtering, when running on
listing
mode. Files are listed in the order that they are provided to
blogc(1) in the command line, no sorting is done.
blogc(1) accepts some variables as -D
options, that are used to filter the
files passed as arguments to it:
FILTER_PAGE
Integer, current page. If calling blogc(1) with 10 files,
FILTER_PER_PAGE=4
and FILTER_PAGE=3
, it will return just the 2 last
files, skipping the first 2 pages with 4 files each one.
FILTER_PER_PAGE
Integer, limits the maximum number of files to be listed. If negative or
0
, no posts are included. Have no effect if FILTER_PAGE
is not
defined.
FILTER_TAG
String, if defined, blogc(1) will only list files that declare a TAGS
variable, as a space-separated list of tags (tags can't have spaces,
obviously). See blogc-source(7) for details about how to define source
variables. The pagination filters will only act on the files with the
provided tag, instead of filtering the whole file set.
FILTER_REVERSE
Boolean (1/y/yes/true/on), if set, blogc(1) will list files in reverse order.
This filter is combined with FILTER_SORT
, and all the other filters will
get the files already in the reverse order.
FILTER_SORT
Boolean (1/y/yes/true/on), if set, blogc(1) will sort files using the DATE
variable provided in the files, instead of respecting the order of the
source files provided to blogc(1). The files are sorted in descending order
and combined with FILTER_REVERSE
, that will result in the files sorted in
ascending order. All the other filters will get the files already sorted.
blogc(1) will export some global blogc-template(7) variables, that can be used to build links for next and previous page.
CURRENT_PAGE
Integer, usually the same value of FILTER_PAGE
pagination paramenter, if
defined, or 1.
FIRST_PAGE
Integer, 1 if more than zero files were listed.
LAST_PAGE
Integer, last page available if more than zero files were listed.
PREVIOUS_PAGE
Integer, CURRENT_PAGE
minus 1, if CURRENT_PAGE
is bigger than 1.
NEXT_PAGE
Integer, CURRENT_PAGE
plus 1, if LAST_PAGE
is bigger than CURRENT_PAGE
.
blogc(1) can output the value of the variables after evaluation, instead of
actually rendering the files, using the -p
option. See blogc(1) for details.
This is useful to know the last page that needs to be built, using -p LAST_PAGE
,
for example.
blogc(1) will also export some global blogc-template(7) variables related to
the DATE
variable, as specified in blogc-source(7).
DATE_FIRST
DATE
variable from the first file in the listing.DATE_LAST
DATE
variable from the last file in the listing.These variables can be also formatted with DATE_FORMAT
global blogc(1) parameter,
if provided, using DATE_FIRST_FORMATTED
and DATE_LAST_FORMATTED
global
template variables.
blogc(1) will also export some global blogc-template(7) variables related to
the FILENAME
variable, as automatically exported by the source file parser,
see blogc-source(7) for details.
FILENAME_FIRST
FILENAME
variable from the first file in the listing.FILENAME_LAST
FILENAME
variable from the last file in the listing.TITLE: My post
TAGS: foo bar baz
-----------------
Post content
This source file defines 3 tags: foo
, bar
and baz
.
{% block listing_once %}

{% ifdef PREVIOUS_PAGE %}

← Newer

{% endif %}
{% ifdef NEXT_PAGE %}

Older →

{% endif %}

{% endblock %}
This example does not uses all the variables, but the concept is the same for all of them.
Please report any issues to: https://github.com/blogc/blogc
Rafael G. Martins<EMAIL_ADDRESS>blogc(1), blogc-source(7), blogc-template(7)


For those of you familiar with SQL and coming to Core Data, you probably want to separate the concept of “database” into the two kinds we’re discussing here. There are relational databases, which is what you’re probably used to, and object databases, which is what Core Data provides. Core Data happens to implement its object database on top of a relational database (SQLite), but that is a opaque implementation detail.
In a relational database, the key actions are the select and the transaction.  In the transaction, you inform the system that you are beginning a transaction, instruct the database to perform a series of transformations, and then commit or roll-back the transaction. It’s a great model, but Core Data doesn’t look much like it.
Core Data provides an object database, which can be thought of as a fancy implementation detail behind an object graph. If your data set is small enough, most everything you can do with Core Data can be done just by creating an object model in memory and saving it out with NSDictionary -writeToFile:atomically: (plus some tedious, but straightforward, code to tie to the UI without bindings). And in fact, that is how I often do it. But what if you have a very large dataset such that reading it all into memory is not efficent enough? Core Data, and specifically NSManagedObject, comes to help. Its primary function is to make the process of faulting transparent. “Faulting” is when you bring something into memory at the point that it’s needed, much like a virtual memory system. And in fact, much of Core Data can be thought of as a virtual memory system for data. Like a VM system, it gives you the illusion that all of your data is available at the same time.
Core Data’s other feature, which really is quite separate, is that it is bindings-compatible. So you can wire up UI elements to NSArrayControllers that are tied to Core Data element classes, and it all just “magically works.” And when you tie an NSArrayController to an NSTableView, it suddenly feels like you’ve got a relational database again, but you don’t. What you have is a slice of an object graph that is being accessed as an ordered collection (i.e. an NSArray). The object graph exists independently of that. It’s just a sea of objects that have pointers to each other. There are no “rows” except when an NSArrayController points to a bunch of objects and provides an order for them. And if another NSArrayController points to those same objects, they can have different orders at the same time. But within the NSManagedObjectContext cloud, they’re all just objects, hanging out, pointing to each other.
So all of this is a very long way of saying that the goal of Core Data is to make the world look like you just have a big object graph. That’s very freeing. In theory, you don’t need to worry about data normalization or problems like that. In practice you still need to worry about good data design, because that’s always important, but you don’t need to worry as much about the underlying impementation details as in a SQL database.
So what I tend to do is start out in the Model Designer and start sketching out my object relationships. Even if you go back and hand-implement them, it’s a good place to lay out your data design. In theory, you shouldn’t need to be worrying about foreign keys or anything like that. Just relationships from one object to another. Just as if it were all going to be stored in memory as a massive data structure.
So let’s take an example of how the thought processes are different. Think of a Department.  You would then SELECT for Employees who had a matching Department key, and you’d wind up with an array-like structure that you would then display.
In CoreData it’s a bit different. You would actually create a Department object. It would have an -employees property. Each Employee would also have a -department property, and these two properties are symmetric. Now yes, deep down in the SQLite there is a unique key that’s tying these folks together, but that’s not really your problem.  You ask the Department “who are your Employees?” And it knows because it has pointers to the actual objects, not their IDs.
 You hand it the Department, and tell it to ask for -employees. For each Employee it returns, ask for -firstName and put it in this column. Then ask for -lastName and put it in this column. And this is why Core Data ties in perfectly with things like NSTableView. It’s all just objects asking other objects about stuff.


| Working with Viewports {grid} | R Documentation |
Grid maintains a tree of viewports — nested drawing contexts.
These functions provide ways to add or remove viewports and to navigate amongst viewports in the tree.
pushViewport(..., recording=TRUE) popViewport(n = 1, recording=TRUE) downViewport(name, strict=FALSE, recording=TRUE) seekViewport(name, recording=TRUE) upViewport(n = 1, recording=TRUE)
... |
One or more objects of class |
n |
An integer value indicating how many viewports to
pop or navigate up. The special value |
name |
A character value to identify a viewport in the tree. |
strict |
A boolean indicating whether the vpPath must be matched exactly. |
recording |
A logical value to indicate whether the viewport operation should be recorded on the Grid display list. |
Objects created by the viewport()
function are only
descriptions
of a drawing context. A viewport object must be pushed onto the
viewport tree before it has any effect on drawing.
The viewport tree always has a single root viewport (created by the
system) which corresponds to the entire device (and default
graphical parameter settings). Viewports may be added to the tree
using pushViewport()
and removed from the tree using
popViewport()
.
There is only ever one current viewport, which is the current
position within the viewport tree. All drawing and
viewport operations are relative to the current viewport.
When a viewport is pushed it becomes the current viewport.
When a viewport is popped, the parent viewport becomes
the current viewport. Use upViewport
to navigate to the parent
of the current viewport, without removing the current viewport
from the viewport tree. Use downViewport
to navigate to
a viewport further down the viewport tree and seekViewport
to navigate to a viewport anywhere else in the tree.
If a viewport is pushed and it has the same name
as a
viewport at the same level in the tree, then it replaces the
existing viewport in the tree.
downViewport
returns the number of viewports it went down.
This can be useful for returning to your starting point by doing
something like depth <- downViewport()
then
upViewport(depth)
.
Paul Murrell
# push the same viewport several times grid.newpage() vp <- viewport(width=0.5, height=0.5) pushViewport(vp) grid.rect(gp=gpar(col="blue")) grid.text("Quarter of the device", y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="blue")) pushViewport(vp) grid.rect(gp=gpar(col="red")) grid.text("Quarter of the parent viewport", y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="red")) popViewport(2) # push several viewports then navigate amongst them grid.newpage() grid.rect(gp=gpar(col="grey")) grid.text("Top-level viewport", y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="grey")) if (interactive()) Sys.sleep(1.0) pushViewport(viewport(width=0.8, height=0.7, name="A")) grid.rect(gp=gpar(col="blue")) grid.text("1. Push Viewport A", y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="blue")) if (interactive()) Sys.sleep(1.0) pushViewport(viewport(x=0.1, width=0.3, height=0.6, just="left", name="B")) grid.rect(gp=gpar(col="red")) grid.text("2. Push Viewport B (in A)", y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="red")) if (interactive()) Sys.sleep(1.0) upViewport(1) grid.text("3. Up from B to A", y=unit(1, "npc") - unit(2, "lines"), gp=gpar(col="blue")) if (interactive()) Sys.sleep(1.0) pushViewport(viewport(x=0.5, width=0.4, height=0.8, just="left", name="C")) grid.rect(gp=gpar(col="green")) grid.text("4. Push Viewport C (in A)", y=unit(1, "npc") - unit(1, "lines"), gp=gpar(col="green")) if (interactive()) Sys.sleep(1.0) pushViewport(viewport(width=0.8, height=0.6, name="D")) grid.rect() grid.text("5. Push Viewport D (in C)", y=unit(1, "npc") - unit(1, "lines")) if (interactive()) Sys.sleep(1.0) upViewport(0) grid.text("6. Up from D to top-level", y=unit(1, "npc") - unit(2, "lines"), gp=gpar(col="grey")) if (interactive()) Sys.sleep(1.0) downViewport("D") grid.text("7. Down from top-level to D", y=unit(1, "npc") - unit(2, "lines")) if (interactive()) Sys.sleep(1.0) seekViewport("B") grid.text("8. Seek from D to B", y=unit(1, "npc") - unit(2, "lines"), gp=gpar(col="red")) pushViewport(viewport(width=0.9, height=0.5, name="A")) grid.rect() grid.text("9. Push Viewport A (in B)", y=unit(1, "npc") - unit(1, "lines")) if (interactive()) Sys.sleep(1.0) seekViewport("A") grid.text("10. Seek from B to A (in ROOT)", y=unit(1, "npc") - unit(3, "lines"), gp=gpar(col="blue")) if (interactive()) Sys.sleep(1.0) seekViewport(vpPath("B", "A")) grid.text("11. Seek from\nA (in ROOT)\nto A (in B)") popViewport(0)


I am currently studying Master of Human Computer Interaction MHCI at Carnegie Mellon University. Previously designed at Live Nation. Currently designing at CHIMPS Lab.
Design for Space
Protecting security on mobile devices
Interaction Design, Prototyping
Delay Reveal Social Dating App
Robot AR Health Tracking


1.

2.
This poem was written by John Saxon (an author of math textbooks).
((12 + 144 + 20 + (3 * 4^(1/2))) / 7) + (5 * 11) = 9^2 + 0
A Dozen, a Gross and a Score,
plus three times the square root of four,
divided by seven,
plus five times eleven,
equals nine squared and not a bit more.
3.
Integral z-squared dz
from 1 to the cube root of 3
times the cosine
of three pi over 9
equals log of the cube root of 'e'.
And it's correct, too.


Tamber iOS SDK
Recommendation engines for developers, easy as π. Build blazing fast, head-scratchingly accurate hosted recommendation engines in minutes.
The Tamber iOS SDK makes it easy to track events (user-item interactions) and get recommendations for your users inside your iOS app.
Get a free api key to get started.
Requirements
Our SDK is compatible with iOS 8.0 and above. It requires Xcode 8.0+ to build the source.
Documentation
This ReadMe covers the basics of using the Tamber iOS SDK. Check out the sdk documentation for a comprehensive look at the classes and methods, and the full API Reference for Tamber documentation.
Installation
Install Tamber with CocoaPods. If you do not yet have a Podfile for your project, $ cd
into your project directory and run $ pod init
.
Add this line to your Podfile:
pod 'Tamber'
Then, run the following command:
$ pod install
Usage
If you are just getting started, check out the Quick Start guide for instructions on how to get setup.
To begin tracking events, set your publishable project key.
#import "AppDelegate.h"
@import Tamber;
@implementation AppDelegate
- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions {
[Tamber setPublishableProjectKey:@"your_project_key"];
// do any other necessary launch configuration
return YES;
}
@end
Set User
Set the User wherever you load your user’s unique ID from your backend, or wherever you load user ids from.
[Tamber setUser:@"user_id"];
Track Events
Stream events to your Tamber project as the user interacts with items in your app.
TMBItem *item = [TMBItem itemWithId:@"item_id"
properties:@{
@"type":@"book",
@"title":@"The Moon is a Harsh Mistress",
@"img_url":@"https://img.domain.com/book/The_Moon_is_a_Harsh Mistress.jpg",
@"stock":[NSNumber numberWithInteger:34]
}
tags:@[@"sci-fi", @"bestseller"]
];
TMBEventParams *params = [TMBEventParams eventWithItem:item behavior:@"like" context:@{@"page": @"homepage", @"section": @"featured"}];
[[Tamber client] trackEvent:params responseCompletion:^(TMBEventResponse *object, NSHTTPURLResponse *response, NSError *error) {
if(error){
// Handle error
} else {
object.events[0] // Event tracked
object.recs // Returns updated recommendations if params.getRecs is set - [params setGetRecs:<TMBDiscoverParams>]
}
}];
You may also set the item to just the unique ID of the item, but this will limit the capabilities of your engine.
TMBEventParams *params = [TMBEventParams eventWithItem:@"item_id" behavior:@"like"];
Get Recommendations
Once you have seeded some events and created your engine, you can start pulling user recommendations into your app.
To get recommendations for the user to show on a homepage, or in any recommended section:
TMBDiscoverParams *params = [TMBDiscoverParams discoverRecommended:[NSNumber numberWithInt:8]];
[[Tamber client] discoverNext:params responseCompletion:^(TMBDiscoverResponse *object, NSHTTPURLResponse *response, NSError *error) {
if(error){
// Handle error
} else {
for(TMBDiscovery *discovery in object.discoveries){
discovery.item // Recommended item id
discovery.score // Recommendation score (relative to other results, not a predicted rating)
}
}
}];
To get similar items for an ‘up next’ section on an item page:
TMBDiscoverParams *params = [TMBDiscoverParams discoverNext:@"item_id" number:[NSNumber numberWithInt:10]];
[[Tamber client] discoverNext:params responseCompletion:^(TMBDiscoverResponse *object, NSHTTPURLResponse *response, NSError *error) {
if(error){
// Handle error
} else {
for(TMBDiscovery *discovery in object.discoveries){
discovery.item // Recommended item id
discovery.score // Recommendation score (relative to other results, not a predicted rating)
}
}
}];
If you are setting properties for your items, you can include these properties in recommendation responses to simplify data handling. For example, you might have title
, img
, and price
properties that you can use to display items to users without needing to make an additional request for each recommendation.
TMBDiscoverParams *params = [TMBDiscoverParams alloc] discoverNext:[NSNumber numberWithInt:50] getProperties:true];
[[Tamber client] discoverNext:params responseCompletion:^(TMBDiscoverResponse *object, NSHTTPURLResponse *response, NSError *error) {
if(error){
// Handle error
} else {
for(TMBDiscovery *discovery in object.discoveries){
discovery.item // Recommended item id
discovery.score // Recommendation score (relative to other results, not a predicted rating)
discovery.properties // Dictionary of item properties
}
}
}];
Anonymous / Signed-Out Users
If your app allows users to interact with content before creating an account, or when they are logged out, but you have some unique identifier (like the device id) you may set the user to this id and track events as normal. Then, when the user creates an account or logs in, you can merge
the anonymous user into the logged-in user.
// At app launch / wherever appropriate in your code
[Tamber setUser:@"anonymous-device-id"];
// On signup/login:
NSString *toUser = @"user-id"; // The id of the user to which you want to merge.
[[Tamber client] mergeToUser:toUser responseCompletion:^(TMBUser *object, NSHTTPURLResponse *response, NSError *error) {
if(error){
// Handle error. Note that mergeToUser internally sets the default user to the `toUser`.
} else {
object.events // Array of merged user events (`TMBEvent` objects)
object.metadata // Dictionary of the merged user metadata. Note that field-value conflicts default to the `toUser`.
}
}];
Testing Recommendations
We recommend creating test users during testing. To do this, simply call the makeTestUser
method.
#define DEV_MODE true
[Tamber setUser:@"testing-user-id"];
if(DEV_MODE){
[Tamber makeTestUser];
}


Demos
The laziest greeting possible - Get started
ref what?
What an instance can do
Instance super powers
Bindings are forever
Bindings - they nest!
Bindings - they nest!
One depends the other
Calculare
On a submission...
One Control to bind 'em all
Selects (1)
Selects (2)
Validation and Alerts
Template Expressions (1)
Template Expressions (2)
and... Action
todo
Unbound


Project 2: Exquisite Corpse
Mythical Mashup: This exquisite corpse involces three seperate parts of a creature, combined along the same guidelines.
Rules: Influence from mythology Follow set color scheme Maintain set points of contact between boxes
Mythical Mashup: This exquisite corpse involces three seperate parts of a creature, combined along the same guidelines.
Rules: Influence from mythology Follow set color scheme Maintain set points of contact between boxes


This sample shows, how to use fancytree as a navigation menu.
The tree initialization uses title
tags.
The activate
handler then uses node.data.href
to open the the pages in the embedded iframe.
Note that the navigation will fallback to standard HTML links,
when JavaScript is disabled.
A focused node will automatically be activated after 2 seconds (use the
keyboard to try this out).
The [Toggle tree] button in the embedded welcome page also gives an example on
how to access a tree that exists outside the own frame.
|
|
Fancytree project home Link to this page Example Browser View source code


When you work with us, you are part of a team of committed collaborators. All aspects
of our software development process are intergrated,interactive
and agile.
Our approach unifies design,development and product management to create exceptional
products.
We start each project by understanding your business goals, the user's need and the
product requirements
Then we help translate them into effective and elegant solutions.
Before we sign a contract or write a line of code, our team will spend the necessary time needed to understand your product
vision
The outcome of this scoping session is proposal or high-level statement of work.
based on your requirements
,we can provide a complete offering or just the pieces you need.
Our design practice offers a full range of services including brand strategy, interaction and visual design and user experience testing. Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to make sure what get built is what is needed.
All engineers are fluent in the latest enterprise, mobile and web development technologies.
They collaborate with your team to write, and improve code on a daily basis, using proven practices
such as test-driven development and pair programming.
Planning and development is iterative.
Because we are constantly coding and testing, the products we build are always ready to go live.
This iterative process allows for changes as business requirements evolve.
Coders
Kenya
Mighty
Ontario security
Join our events
Apps
Lets
Chat
Mobile
Application
Burned Mock-up
Kenya
Giraffe
Restaurant


HOME
Ears
Eyes
Let us be your ears
Upload for Translation
Upload File
×
Select Language
Afrikanns
Albanian
Arabic
Armenian
Basque
Bengali
Bulgarian
Catalan
Cambodian
Chinese (Mandarin)
Croation
Czech
Danish
Dutch
English
Estonian
Fiji
Finnish
French
Georgian
German
Greek
Gujarati
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Javanese
Korean
Latin
Latvian
Lithuanian
Macedonian
Malay
Malayalam
Maltese
Maori
Marathi
Mongolian
Nepali
Norwegian
Persian
Polish
Portuguese
Punjabi
Quechua
Romanian
Russian
Samoan
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Tamil
Tatar
Telugu
Thai
Tibetan
Tonga
Turkish
Ukranian
Urdu
Uzbek
Vietnamese
Welsh
Xhosa
Your browser does not support the video tag.
Final Translated Video
Scene Details
There is spokesperson. There's also a girl.
Translated: Hay un vocero. También hay una niña.


The world around us has become quite cluttered and the web is no exception. Ads, banners, icons, badges, signs, pop-ups, buttons, and so on – sometimes it can all get a bit heavy. So why not give your site visitors a break from all the noise and clutter? Embracing things like flat design and white space (you can read more about both these terms here) can do wonders for your site visitor’s experience. Try to keep everything simplistic or even minimal with only your most important content spotlighted. Sometimes less really is more.
Make some mental or actual notes on what you would like to emulate on your own site. Do you think a long scrolling page would work well with your site? Maybe you really admire someone elses approach to their contact page. It can be something as small as imitating a use of an arrow icon that points to an important message. Whatever it is you find appealing, think about how you can make it happen in your own website’s design scheme.
Visual hierarchy, what’s that again? It’s a term that basically means our eyes pay attention to web space in a certain pattern – a pattern that can help you optimize important content on your site. For example, if you create a ‘sign up now’ button, you likely want as many people as possible to click it and follow-through with the sign-up process. Visual hierarchy tells us that the eyes move top to bottom, left to right. This means you’ll get the most eyes on your button in the top left corner of your site, and those eyes could very well mean more clicks. Remember, only put your most important content in these coveted spaces – if you put too much in one spot your visitor will be overwhelmed and you won’t get the result you’re aiming for.


[1]
Hi def recording and burning,no tapes,; price but worth it
[2]
excellent quality; poor software for computer editing


My MVC Application
[
Log On
]
Home
About
C:\Users\user-000\documents\visual studio 2010\Projects\ShoppingSmart\ShoppingSmart\
To learn more about ASP.NET MVC visit
http://asp.net/mvc
.


Welcome to XML Security
News
July 2005
Version 1.2.1 of the C++ library has been released. This minor release fixes versioning problems in the Windows project files. See the changelog for more information.
June 2005
Version 1.2 of the C++ library has been released. This version includes a number of bug fixes, together with a beta release of code to process and generate XKMS messages. See the changelog for more information.
February 2005
Version 1.2.1 of the Java library has been released. This is a bugfix version, for more detail information see the changelog.
December 2004
Version 1.2 of the Java library has been released. This version provides :
- Better speed & memory utilization.
- Easier JCE integration.
April 2004
Version 1.1 of the Java library has been released. This version provides :
- Beta implementation of XML Encryption
- Bug fixes to Signature implementation
March 2004
Version 1.1 of the C++ library has been released. Supporting Xerces 2.5, 2.4 and 2.3 together with Xalan 1.6 and 1.7, this version provides :
- Beta implementation of XML Encryption
- Improved support for Windows Crypto API
- Bug fixes to Signature implementation
August 2003
Version 1.00 of the C++ library is now released. This is the first stable release of the library. Functionality is still fairly basic, but all mandatory parts of the the DSIG standard are implemented.
This version supports Xerces 2.2 and 2.3 and Xalan 1.6.
May 2003
The Beta 0.20 of the C++ library has now been released. Features:
- Ability to use the Windows Crypto API as a crypto provider
- Several minor bug fixes in transforms and UNIX build process
February 2003
The site has now been re-built using the new xml.apache.org standard, using Forrest.
The Project
The XML Security project is aimed at providing implementation of security standards for XML. Currently the focus is on the W3C standards :
Once these are implemented, XML Key Management is likely to be the next focus for the project.
Two libraries are currently available.
- A Java library, which includes a mature Digital Signature implementation. Encryption is currently under development.
- A C++ library is also now available. Functionality is currently more basic than that provided by the Java library.


The Error Handler control will handle a variety of different errors. It will support form and generic errors.
It does this via extending the other elements in DataSift Bootstrap with their correct error stylings.
When the validation fails on an input the following error message should
be shown.
Tooltip
The tooltip control enables tooltips to be displayed on elements. The tooltip
will be positioned relative to the element and can accept any HTML based string.
Name
Type
Description
Message
string
The message to display in the tooltip
Element
DOMElement
Element to attach the tooltip to
Options
object
See options
Options
Name
Type
Description
position
string
One of the following; top, bottom, left and right
className
array
Additional css class names for the tooltip
Basic Example
In this example we set the position to bottom which will cause the element to
be rendered below the element.
Hover Example
The most common usercase for a tooltip is to show the tooltip on hover. In
example we are attaching an event listener to add and remove the tooltip. Notice
how we are assigning the tooltip to this so we can remove it at a later date.
Actionable Content
A slightly more indepth example involves using the tooltip with a content block
which contains a link. This requires the tooltip to be able to stay when we
mouse out of the element and into the tooltip.
Tooltips as Errors
Please see the error documentation for more information in using tooltips as
error messages. Typically this can be achieved by adding the css class error
to the tooltip class list.
02
Design
Colors
An introduction to our colour philosophy.
Primary palette
The primary colors are mainly used on the logo.
Name
Hex
RGB
CMYK
Pantone
SaSS Variable
DataSift Orange
#FA7F36
250,127,54
0,62,88,0
158C
$datasift-orange
Night Sky
#393943
57,57,67
73,67,53,47
532C
$night-sky
Secondary palette
We use the secondary palette to compliment the primary palette.
Name
Hex
RGB
CMYK
SaSS Variable
Slate
#50626F
80,98,111
0,62,88,0
$datasift-slate
Blue
#42A7CA
66,167,202
69,17,12,0
$datasift-blue
Typography
Font
We use Google's Open Sans as our default font. You can run this on your site using Google Fonts. We recommend the link script below to add Open Sans to your project.
Headings
DataSift Bootstrap supports the headings h1 through to h6 as both tags and classes.


About Buck
A Man Of Many Hats
Buck Melton is a native of the Philadelphia area and a long-time Bay Area resident. More...
Featured Portfolio Item
Home Page Wireframe
Within the first week at Dev Bootcamp, Buck created this beautiful home page wireframe using time-honored web design principles and Balsamiq. More...
Featured Blog Post
What's My Biggest Fear?
Once you make the leap into beginning a developer bootcamp, all sorts of fears work their way to the surface. Some of us are more or less successful at burying those fears. More...


| Column Name | Data type |
| name | String |
| String | |
| password | String (upper case,lower case,special character |
| password_confirmation | String (same as password) |
| Status code | Response |
| 201 Created | Successfully created user! |
| Status code | Message |
| 422 Unprocessable Entity | Given data was invalid |


CrossFit is a strength, conditioning, and overall fitness program consisting mainly of a mix of aerobic
exercise, calisthenics (body weight exercises), and Olympic weightlifting
CrossFit is designed to build wide-ranging, overall, and comprehensive level of fitness in functional
competence in all 10 key physical parameters: cardiovascular/respiratory endurance, stamina, strength,
flexibility, power, speed, coordination, agility, balance, accuracy


Members |
|
|---|---|
| tensorflow::ops::BatchToSpace | BatchToSpace for 4-D tensors of type T. |
| tensorflow::ops::BatchToSpaceND | BatchToSpace for N-D tensors of type T. |
| tensorflow::ops::Bitcast | Bitcasts a tensor from one type to another without copying data. |
| tensorflow::ops::BroadcastDynamicShape | Return the shape of s0 op s1 with broadcast. |
| tensorflow::ops::BroadcastTo | Broadcast an array for a compatible shape. |
| tensorflow::ops::CheckNumerics | Checks a tensor for NaN and Inf values. |
| tensorflow::ops::Concat | Concatenates tensors along one dimension. |
| tensorflow::ops::ConjugateTranspose | Shuffle dimensions of x according to a permutation and conjugate the result. |
| tensorflow::ops::DebugGradientIdentity | Identity op for gradient debugging. |
| tensorflow::ops::DebugGradientRefIdentity | Identity op for gradient debugging. |
| tensorflow::ops::DeepCopy | Makes a copy of x . |
| tensorflow::ops::DepthToSpace | DepthToSpace for tensors of type T. |
| tensorflow::ops::Dequantize | Dequantize the 'input' tensor into a float Tensor. |
| tensorflow::ops::Diag | Returns a diagonal tensor with a given diagonal values. |
| tensorflow::ops::DiagPart | Returns the diagonal part of the tensor. |
| tensorflow::ops::EditDistance | Computes the (possibly normalized) Levenshtein Edit Distance. |
| tensorflow::ops::Empty | Creates a tensor with the given shape. |
| tensorflow::ops::EnsureShape | Ensures that the tensor's shape matches the expected shape. |
| tensorflow::ops::ExpandDims | Inserts a dimension of 1 into a tensor's shape. |
| tensorflow::ops::ExtractImagePatches | Extract patches from images and put them in the "depth" output dimension. |
| tensorflow::ops::ExtractVolumePatches | Extract patches from input and put them in the "depth" output dimension. |
| tensorflow::ops::FakeQuantWithMinMaxArgs | Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type. |
| tensorflow::ops::FakeQuantWithMinMaxArgsGradient | Compute gradients for a FakeQuantWithMinMaxArgs operation. |
| tensorflow::ops::FakeQuantWithMinMaxVars | Fake-quantize the 'inputs' tensor of type float via global float scalars min |
| tensorflow::ops::FakeQuantWithMinMaxVarsGradient | Compute gradients for a FakeQuantWithMinMaxVars operation. |
| tensorflow::ops::FakeQuantWithMinMaxVarsPerChannel | Fake-quantize the 'inputs' tensor of type float and one of the shapes: [d] ,. |
| tensorflow::ops::FakeQuantWithMinMaxVarsPerChannelGradient | Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation. |
| tensorflow::ops::Fill | Creates a tensor filled with a scalar value. |
| tensorflow::ops::Gather | Gather slices from params according to indices . |
| tensorflow::ops::GatherNd | Gather slices from params into a Tensor with shape specified by indices . |
| tensorflow::ops::GatherV2 | Gather slices from params axis axis according to indices . |
| tensorflow::ops::GuaranteeConst | Gives a guarantee to the TF runtime that the input tensor is a constant. |
| tensorflow::ops::Identity | Return a tensor with the same shape and contents as the input tensor or value. |
| tensorflow::ops::IdentityN | Returns a list of tensors with the same shapes and contents as the input. |
| tensorflow::ops::ImmutableConst | Returns immutable tensor from memory region. |
| tensorflow::ops::InplaceAdd | Adds v into specified rows of x. |
| tensorflow::ops::InplaceSub | Subtracts v into specified rows of x . |
| tensorflow::ops::InplaceUpdate | Updates specified rows with values in v . |
| tensorflow::ops::InvertPermutation | Computes the inverse permutation of a tensor. |
| tensorflow::ops::MatrixBandPart | Copy a tensor setting everything outside a central band in each innermost matrix. |
| tensorflow::ops::MatrixDiag | Returns a batched diagonal tensor with a given batched diagonal values. |
| tensorflow::ops::MatrixDiagPart | Returns the batched diagonal part of a batched tensor. |
| tensorflow::ops::MatrixSetDiag | Returns a batched matrix tensor with new batched diagonal values. |
| tensorflow::ops::MirrorPad | Pads a tensor with mirrored values. |
| tensorflow::ops::OneHot | Returns a one-hot tensor. |
| tensorflow::ops::OnesLike | Returns a tensor of ones with the same shape and type as x. |
| tensorflow::ops::QuantizedReshape | Packs a list of N rank-R tensors into one rank-(R+1) tensor. |
| tensorflow::ops::Rank | Returns the rank of a tensor. |
| tensorflow::ops::Reshape | Reshapes a tensor. |
| tensorflow::ops::ResourceStridedSliceAssign | Assignvalue to the sliced l-value reference of ref . |
| tensorflow::ops::Reverse | Reverses specific dimensions of a tensor. |
| tensorflow::ops::ReverseSequence | Reverses variable length slices. |
| tensorflow::ops::ScatterNd | Scatter updates into a new tensor according to indices . |
| tensorflow::ops::ScatterNdNonAliasingAdd | Applies sparse addition to input using individual values or slices. |
| tensorflow::ops::SetDiff1D | Computes the difference between two lists of numbers or strings. |
| tensorflow::ops::Shape | Returns the shape of a tensor. |
| tensorflow::ops::ShapeN | Returns shape of tensors. |
| tensorflow::ops::Size | Returns the size of a tensor. |
| tensorflow::ops::Slice | Return a slice from 'input'. |
| tensorflow::ops::Snapshot | Returns a copy of the input tensor. |
| tensorflow::ops::SpaceToBatch | SpaceToBatch for 4-D tensors of type T. |
| tensorflow::ops::SpaceToBatchND | SpaceToBatch for N-D tensors of type T. |
| tensorflow::ops::SpaceToDepth | SpaceToDepth for tensors of type T. |
| tensorflow::ops::Split | Splits a tensor into num_split tensors along one dimension. |
| tensorflow::ops::SplitV | Splits a tensor into num_split tensors along one dimension. |
| tensorflow::ops::Squeeze | Removes dimensions of size 1 from the shape of a tensor. |
| tensorflow::ops::StopGradient | Stops gradient computation. |
| tensorflow::ops::StridedSlice | Return a strided slice from input . |
| tensorflow::ops::StridedSliceAssign | Assignvalue to the sliced l-value reference of ref . |
| tensorflow::ops::StridedSliceGrad | Returns the gradient of StridedSlice . |
| tensorflow::ops::Tile | Constructs a tensor by tiling a given tensor. |
| tensorflow::ops::Transpose | Shuffle dimensions of x according to a permutation. |
| tensorflow::ops::Unique | Finds unique elements in a 1-D tensor. |
| tensorflow::ops::UniqueV2 | Finds unique elements along an axis of a tensor. |
| tensorflow::ops::UniqueWithCounts | Finds unique elements in a 1-D tensor. |
| tensorflow::ops::UniqueWithCountsV2 | Finds unique elements along an axis of a tensor. |
| tensorflow::ops::UnravelIndex | Converts a flat index or array of flat indices into a tuple of. |
| tensorflow::ops::Unstack | Unpacks a given dimension of a rank-R tensor into num rank-(R-1) tensors. |
| tensorflow::ops::Where | Returns locations of nonzero / true values in a tensor. |
| tensorflow::ops::ZerosLike | Returns a tensor of zeros with the same shape and type as x. |
Members |
|
|---|---|
| tensorflow::ops::AllCandidateSampler | Generates labels for candidate sampling with a learned unigram distribution. |
| tensorflow::ops::ComputeAccidentalHits | Computes the ids of the positions in sampled_candidates that match true_labels. |
| tensorflow::ops::FixedUnigramCandidateSampler | Generates labels for candidate sampling with a learned unigram distribution. |
| tensorflow::ops::LearnedUnigramCandidateSampler | Generates labels for candidate sampling with a learned unigram distribution. |
| tensorflow::ops::LogUniformCandidateSampler | Generates labels for candidate sampling with a log-uniform distribution. |
| tensorflow::ops::UniformCandidateSampler | Generates labels for candidate sampling with a uniform distribution. |
Members |
|
|---|---|
| tensorflow::ops::Abort | Raise a exception to abort the process when called. |
| tensorflow::ops::ControlTrigger | Does nothing. |
| tensorflow::ops::LoopCond | Forwards the input to the output. |
| tensorflow::ops::Merge | Forwards the value of an available tensor from inputs to output . |
| tensorflow::ops::NextIteration | Makes its input available to the next iteration. |
| tensorflow::ops::RefNextIteration | Makes its input available to the next iteration. |
| tensorflow::ops::RefSelect | Forwards the index th element of inputs to output . |
| tensorflow::ops::RefSwitch | Forwards the ref tensor data to the output port determined by pred . |
| tensorflow::ops::Switch | Forwards data to the output port determined by pred . |
Members |
|
|---|---|
| tensorflow::ClientSession | A ClientSession object lets the caller drive the evaluation of the TensorFlow graph constructed with the C++ API. |
| tensorflow::Input | Represents a tensor value that can be used as an operand to an Operation. |
| tensorflow::InputList | A type for representing the input to ops that require a list of tensors. |
| tensorflow::Operation | Represents a node in the computation graph. |
| tensorflow::Output | Represents a tensor value produced by an Operation. |
| tensorflow::Scope | A Scope object represents a set of related TensorFlow ops that have the same properties such as a common name prefix. |
| tensorflow::Status | Denotes success or failure of a call in Tensorflow. |
| tensorflow::Tensor | Represents an n-dimensional array of values. |
Members |
|
|---|---|
| tensorflow::ops::AccumulatorApplyGradient | Applies a gradient to a given accumulator. |
| tensorflow::ops::AccumulatorNumAccumulated | Returns the number of gradients aggregated in the given accumulators. |
| tensorflow::ops::AccumulatorSetGlobalStep | Updates the accumulator with a new value for global_step. |
| tensorflow::ops::AccumulatorTakeGradient | Extracts the average gradient in the given ConditionalAccumulator. |
| tensorflow::ops::Barrier | Defines a barrier that persists across different graph executions. |
| tensorflow::ops::BarrierClose | Closes the given barrier. |
| tensorflow::ops::BarrierIncompleteSize | Computes the number of incomplete elements in the given barrier. |
| tensorflow::ops::BarrierInsertMany | For each key, assigns the respective value to the specified component. |
| tensorflow::ops::BarrierReadySize | Computes the number of complete elements in the given barrier. |
| tensorflow::ops::BarrierTakeMany | Takes the given number of completed elements from a barrier. |
| tensorflow::ops::ConditionalAccumulator | A conditional accumulator for aggregating gradients. |
| tensorflow::ops::DeleteSessionTensor | Delete the tensor specified by its handle in the session. |
| tensorflow::ops::DynamicPartition | Partitions data into num_partitions tensors using indices from partitions . |
| tensorflow::ops::DynamicStitch | Interleave the values from the data tensors into a single tensor. |
| tensorflow::ops::FIFOQueue | A queue that produces elements in first-in first-out order. |
| tensorflow::ops::GetSessionHandle | Store the input tensor in the state of the current session. |
| tensorflow::ops::GetSessionHandleV2 | Store the input tensor in the state of the current session. |
| tensorflow::ops::GetSessionTensor | Get the value of the tensor specified by its handle. |
| tensorflow::ops::MapClear | Op removes all elements in the underlying container. |
| tensorflow::ops::MapIncompleteSize | Op returns the number of incomplete elements in the underlying container. |
| tensorflow::ops::MapPeek | Op peeks at the values at the specified key. |
| tensorflow::ops::MapSize | Op returns the number of elements in the underlying container. |
| tensorflow::ops::MapStage | Stage (key, values) in the underlying container which behaves like a hashtable. |
| tensorflow::ops::MapUnstage | Op removes and returns the values associated with the key. |
| tensorflow::ops::MapUnstageNoKey | Op removes and returns a random (key, value) |
| tensorflow::ops::OrderedMapClear | Op removes all elements in the underlying container. |
| tensorflow::ops::OrderedMapIncompleteSize | Op returns the number of incomplete elements in the underlying container. |
| tensorflow::ops::OrderedMapPeek | Op peeks at the values at the specified key. |
| tensorflow::ops::OrderedMapSize | Op returns the number of elements in the underlying container. |
| tensorflow::ops::OrderedMapStage | Stage (key, values) in the underlying container which behaves like a ordered. |
| tensorflow::ops::OrderedMapUnstage | Op removes and returns the values associated with the key. |
| tensorflow::ops::OrderedMapUnstageNoKey | Op removes and returns the (key, value) element with the smallest. |
| tensorflow::ops::PaddingFIFOQueue | A queue that produces elements in first-in first-out order. |
| tensorflow::ops::ParallelDynamicStitch | Interleave the values from the data tensors into a single tensor. |
| tensorflow::ops::PriorityQueue | A queue that produces elements sorted by the first component value. |
| tensorflow::ops::QueueClose | Closes the given queue. |
| tensorflow::ops::QueueDequeue | Dequeues a tuple of one or more tensors from the given queue. |
| tensorflow::ops::QueueDequeueMany | Dequeues n tuples of one or more tensors from the given queue. |
| tensorflow::ops::QueueDequeueUpTo | Dequeues n tuples of one or more tensors from the given queue. |
| tensorflow::ops::QueueEnqueue | Enqueues a tuple of one or more tensors in the given queue. |
| tensorflow::ops::QueueEnqueueMany | Enqueues zero or more tuples of one or more tensors in the given queue. |
| tensorflow::ops::QueueIsClosed | Returns true if queue is closed. |
| tensorflow::ops::QueueIsClosedV2 | Returns true if queue is closed. |
| tensorflow::ops::QueueSize | Computes the number of elements in the given queue. |
| tensorflow::ops::RandomShuffleQueue | A queue that randomizes the order of elements. |
| tensorflow::ops::RecordInput | Emits randomized records. |
| tensorflow::ops::SparseAccumulatorApplyGradient | Applies a sparse gradient to a given accumulator. |
| tensorflow::ops::SparseAccumulatorTakeGradient | Extracts the average sparse gradient in a SparseConditionalAccumulator. |
| tensorflow::ops::SparseConditionalAccumulator | A conditional accumulator for aggregating sparse gradients. |
| tensorflow::ops::Stage | Stage values similar to a lightweight Enqueue. |
| tensorflow::ops::StageClear | Op removes all elements in the underlying container. |
| tensorflow::ops::StagePeek | Op peeks at the values at the specified index. |
| tensorflow::ops::StageSize | Op returns the number of elements in the underlying container. |
| tensorflow::ops::TensorArray | An array of Tensors of given size. |
| tensorflow::ops::TensorArrayClose | Delete the TensorArray from its resource container. |
| tensorflow::ops::TensorArrayConcat | Concat the elements from the TensorArray into value value . |
| tensorflow::ops::TensorArrayGather | Gather specific elements from the TensorArray into output value . |
| tensorflow::ops::TensorArrayGrad | Creates a TensorArray for storing the gradients of values in the given handle. |
| tensorflow::ops::TensorArrayGradWithShape | Creates a TensorArray for storing multiple gradients of values in the given handle. |
| tensorflow::ops::TensorArrayRead | Read an element from the TensorArray into output value . |
| tensorflow::ops::TensorArrayScatter | Scatter the data from the input value into specific TensorArray elements. |
| tensorflow::ops::TensorArraySize | Get the current size of the TensorArray. |
| tensorflow::ops::TensorArraySplit | Split the data from the input value into TensorArray elements. |
| tensorflow::ops::TensorArrayWrite | Push an element onto the tensor_array. |
| tensorflow::ops::Unstage | Op is similar to a lightweight Dequeue. |
Members |
|
|---|---|
| tensorflow::ops::AdjustContrast | Adjust the contrast of one or more images. |
| tensorflow::ops::AdjustHue | Adjust the hue of one or more images. |
| tensorflow::ops::AdjustSaturation | Adjust the saturation of one or more images. |
| tensorflow::ops::CropAndResize | Extracts crops from the input image tensor and resizes them. |
| tensorflow::ops::CropAndResizeGradBoxes | Computes the gradient of the crop_and_resize op wrt the input boxes tensor. |
| tensorflow::ops::CropAndResizeGradImage | Computes the gradient of the crop_and_resize op wrt the input image tensor. |
| tensorflow::ops::DecodeAndCropJpeg | Decode and Crop a JPEG-encoded image to a uint8 tensor. |
| tensorflow::ops::DecodeBmp | Decode the first frame of a BMP-encoded image to a uint8 tensor. |
| tensorflow::ops::DecodeGif | Decode the first frame of a GIF-encoded image to a uint8 tensor. |
| tensorflow::ops::DecodeJpeg | Decode a JPEG-encoded image to a uint8 tensor. |
| tensorflow::ops::DecodePng | Decode a PNG-encoded image to a uint8 or uint16 tensor. |
| tensorflow::ops::DrawBoundingBoxes | Draw bounding boxes on a batch of images. |
| tensorflow::ops::EncodeJpeg | JPEG-encode an image. |
| tensorflow::ops::EncodePng | PNG-encode an image. |
| tensorflow::ops::ExtractGlimpse | Extracts a glimpse from the input tensor. |
| tensorflow::ops::ExtractJpegShape | Extract the shape information of a JPEG-encoded image. |
| tensorflow::ops::HSVToRGB | Convert one or more images from HSV to RGB. |
| tensorflow::ops::NonMaxSuppression | Greedily selects a subset of bounding boxes in descending order of score,. |
| tensorflow::ops::NonMaxSuppressionV2 | Greedily selects a subset of bounding boxes in descending order of score,. |
| tensorflow::ops::NonMaxSuppressionV3 | Greedily selects a subset of bounding boxes in descending order of score,. |
| tensorflow::ops::NonMaxSuppressionV4 | Greedily selects a subset of bounding boxes in descending order of score,. |
| tensorflow::ops::NonMaxSuppressionWithOverlaps | Greedily selects a subset of bounding boxes in descending order of score,. |
| tensorflow::ops::QuantizedResizeBilinear | Resize quantized images to size using quantized bilinear interpolation. |
| tensorflow::ops::RGBToHSV | Converts one or more images from RGB to HSV. |
| tensorflow::ops::ResizeArea | Resize images to size using area interpolation. |
| tensorflow::ops::ResizeBicubic | Resize images to size using bicubic interpolation. |
| tensorflow::ops::ResizeBilinear | Resize images to size using bilinear interpolation. |
| tensorflow::ops::ResizeNearestNeighbor | Resize images to size using nearest neighbor interpolation. |
| tensorflow::ops::SampleDistortedBoundingBox | Generate a single randomly distorted bounding box for an image. |
| tensorflow::ops::SampleDistortedBoundingBoxV2 | Generate a single randomly distorted bounding box for an image. |
Members |
|
|---|---|
| tensorflow::ops::FixedLengthRecordReader | A Reader that outputs fixed-length records from a file. |
| tensorflow::ops::IdentityReader | A Reader that outputs the queued work as both the key and value. |
| tensorflow::ops::LMDBReader | A Reader that outputs the records from a LMDB file. |
| tensorflow::ops::MatchingFiles | Returns the set of files matching one or more glob patterns. |
| tensorflow::ops::MergeV2Checkpoints | V2 format specific: merges the metadata files of sharded checkpoints. |
| tensorflow::ops::ReadFile | Reads and outputs the entire contents of the input filename. |
| tensorflow::ops::ReaderNumRecordsProduced | Returns the number of records this Reader has produced. |
| tensorflow::ops::ReaderNumWorkUnitsCompleted | Returns the number of work units this Reader has finished processing. |
| tensorflow::ops::ReaderRead | Returns the next record (key, value pair) produced by a Reader. |
| tensorflow::ops::ReaderReadUpTo | Returns up to num_records (key, value) pairs produced by a Reader. |
| tensorflow::ops::ReaderReset | Restore a Reader to its initial clean state. |
| tensorflow::ops::ReaderRestoreState | Restore a reader to a previously saved state. |
| tensorflow::ops::ReaderSerializeState | Produce a string tensor that encodes the state of a Reader. |
| tensorflow::ops::Restore | Restores a tensor from checkpoint files. |
| tensorflow::ops::RestoreSlice | Restores a tensor from checkpoint files. |
| tensorflow::ops::RestoreV2 | Restores tensors from a V2 checkpoint. |
| tensorflow::ops::Save | Saves the input tensors to disk. |
| tensorflow::ops::SaveSlices | Saves input tensors slices to disk. |
| tensorflow::ops::SaveV2 | Saves tensors in V2 checkpoint format. |
| tensorflow::ops::ShardedFilename | Generate a sharded filename. |
| tensorflow::ops::ShardedFilespec | Generate a glob pattern matching all sharded file names. |
| tensorflow::ops::TFRecordReader | A Reader that outputs the records from a TensorFlow Records file. |
| tensorflow::ops::TextLineReader | A Reader that outputs the lines of a file delimited by ' '. |
| tensorflow::ops::WholeFileReader | A Reader that outputs the entire contents of a file as a value. |
| tensorflow::ops::WriteFile | Writes contents to the file at input filename. |
Members |
|
|---|---|
| tensorflow::ops::Assert | Asserts that the given condition is true. |
| tensorflow::ops::HistogramSummary | Outputs a Summary protocol buffer with a histogram. |
| tensorflow::ops::MergeSummary | Merges summaries. |
| tensorflow::ops::Print | Prints a list of tensors. |
| tensorflow::ops::PrintV2 | Prints a string scalar. |
| tensorflow::ops::ScalarSummary | Outputs a Summary protocol buffer with scalar values. |
| tensorflow::ops::TensorSummary | Outputs a Summary protocol buffer with a tensor. |
| tensorflow::ops::TensorSummaryV2 | Outputs a Summary protocol buffer with a tensor and per-plugin data. |
| tensorflow::ops::Timestamp | Provides the time since epoch in seconds. |
Members |
|
|---|---|
| tensorflow::ops::Abs | Computes the absolute value of a tensor. |
| tensorflow::ops::AccumulateNV2 | Returns the element-wise sum of a list of tensors. |
| tensorflow::ops::Acos | Computes acos of x element-wise. |
| tensorflow::ops::Acosh | Computes inverse hyperbolic cosine of x element-wise. |
| tensorflow::ops::Add | Returns x + y element-wise. |
| tensorflow::ops::AddN | Add all input tensors element wise. |
| tensorflow::ops::AddV2 | Returns x + y element-wise. |
| tensorflow::ops::All | Computes the "logical and" of elements across dimensions of a tensor. |
| tensorflow::ops::Angle | Returns the argument of a complex number. |
| tensorflow::ops::Any | Computes the "logical or" of elements across dimensions of a tensor. |
| tensorflow::ops::ApproximateEqual | Returns the truth value of abs(x-y) < tolerance element-wise. |
| tensorflow::ops::ArgMax | Returns the index with the largest value across dimensions of a tensor. |
| tensorflow::ops::ArgMin | Returns the index with the smallest value across dimensions of a tensor. |
| tensorflow::ops::Asin | Computes asin of x element-wise. |
| tensorflow::ops::Asinh | Computes inverse hyperbolic sine of x element-wise. |
| tensorflow::ops::Atan | Computes atan of x element-wise. |
| tensorflow::ops::Atan2 | Computes arctangent of y/x element-wise, respecting signs of the arguments. |
| tensorflow::ops::Atanh | Computes inverse hyperbolic tangent of x element-wise. |
| tensorflow::ops::BatchMatMul | Multiplies slices of two tensors in batches. |
| tensorflow::ops::BesselI0e | Computes the Bessel i0e function of x element-wise. |
| tensorflow::ops::BesselI1e | Computes the Bessel i1e function of x element-wise. |
| tensorflow::ops::Betainc | Compute the regularized incomplete beta integral \(I_x(a, b)\). |
| tensorflow::ops::Bincount | Counts the number of occurrences of each value in an integer array. |
| tensorflow::ops::Bucketize | Bucketizes 'input' based on 'boundaries'. |
| tensorflow::ops::Cast | Cast x of type SrcT to y of DstT. |
| tensorflow::ops::Ceil | Returns element-wise smallest integer not less than x. |
| tensorflow::ops::ClipByValue | Clips tensor values to a specified min and max. |
| tensorflow::ops::CompareAndBitpack | Compare values of input to threshold and pack resulting bits into a uint8 . |
| tensorflow::ops::Complex | Converts two real numbers to a complex number. |
| tensorflow::ops::ComplexAbs | Computes the complex absolute value of a tensor. |
| tensorflow::ops::Conj | Returns the complex conjugate of a complex number. |
| tensorflow::ops::Cos | Computes cos of x element-wise. |
| tensorflow::ops::Cosh | Computes hyperbolic cosine of x element-wise. |
| tensorflow::ops::Cross | Compute the pairwise cross product. |
| tensorflow::ops::Cumprod | Compute the cumulative product of the tensor x along axis . |
| tensorflow::ops::Cumsum | Compute the cumulative sum of the tensor x along axis . |
| tensorflow::ops::Digamma | Computes Psi, the derivative of Lgamma (the log of the absolute value of. |
| tensorflow::ops::Div | Returns x / y element-wise. |
| tensorflow::ops::DivNoNan | Returns 0 if the denominator is zero. |
| tensorflow::ops::Equal | Returns the truth value of (x == y) element-wise. |
| tensorflow::ops::Erf | Computes the Gauss error function of x element-wise. |
| tensorflow::ops::Erfc | Computes the complementary error function of x element-wise. |
| tensorflow::ops::Exp | Computes exponential of x element-wise. |
| tensorflow::ops::Expm1 | Computes exponential of x - 1 element-wise. |
| tensorflow::ops::Floor | Returns element-wise largest integer not greater than x. |
| tensorflow::ops::FloorDiv | Returns x // y element-wise. |
| tensorflow::ops::FloorMod | Returns element-wise remainder of division. |
| tensorflow::ops::Greater | Returns the truth value of (x > y) element-wise. |
| tensorflow::ops::GreaterEqual | Returns the truth value of (x >= y) element-wise. |
| tensorflow::ops::HistogramFixedWidth | Return histogram of values. |
| tensorflow::ops::Igamma | Compute the lower regularized incomplete Gamma function P(a, x) . |
| tensorflow::ops::Igammac | Compute the upper regularized incomplete Gamma function Q(a, x) . |
| tensorflow::ops::Imag | Returns the imaginary part of a complex number. |
| tensorflow::ops::Inv | Computes the reciprocal of x element-wise. |
| tensorflow::ops::IsFinite | Returns which elements of x are finite. |
| tensorflow::ops::IsInf | Returns which elements of x are Inf. |
| tensorflow::ops::IsNan | Returns which elements of x are NaN. |
| tensorflow::ops::Less | Returns the truth value of (x < y) element-wise. |
| tensorflow::ops::LessEqual | Returns the truth value of (x <= y) element-wise. |
| tensorflow::ops::Lgamma | Computes the log of the absolute value of Gamma(x) element-wise. |
| tensorflow::ops::LinSpace | Generates values in an interval. |
| tensorflow::ops::Log | Computes natural logarithm of x element-wise. |
| tensorflow::ops::Log1p | Computes natural logarithm of (1 + x) element-wise. |
| tensorflow::ops::LogicalAnd | Returns the truth value of x AND y element-wise. |
| tensorflow::ops::LogicalNot | Returns the truth value of NOT x element-wise. |
| tensorflow::ops::LogicalOr | Returns the truth value of x OR y element-wise. |
| tensorflow::ops::MatMul | Multiply the matrix "a" by the matrix "b". |
| tensorflow::ops::Max | Computes the maximum of elements across dimensions of a tensor. |
| tensorflow::ops::Maximum | Returns the max of x and y (i.e. |
| tensorflow::ops::Mean | Computes the mean of elements across dimensions of a tensor. |
| tensorflow::ops::Min | Computes the minimum of elements across dimensions of a tensor. |
| tensorflow::ops::Minimum | Returns the min of x and y (i.e. |
| tensorflow::ops::Mod | Returns element-wise remainder of division. |
| tensorflow::ops::Multiply | Returns x * y element-wise. |
| tensorflow::ops::Negate | Computes numerical negative value element-wise. |
| tensorflow::ops::NotEqual | Returns the truth value of (x != y) element-wise. |
| tensorflow::ops::Polygamma | Compute the polygamma function \(^{(n)}(x)\). |
| tensorflow::ops::Pow | Computes the power of one value to another. |
| tensorflow::ops::Prod | Computes the product of elements across dimensions of a tensor. |
| tensorflow::ops::QuantizeDownAndShrinkRange | Convert the quantized 'input' tensor into a lower-precision 'output', using the. |
| tensorflow::ops::QuantizedAdd | Returns x + y element-wise, working on quantized buffers. |
| tensorflow::ops::QuantizedMatMul | Perform a quantized matrix multiplication of a by the matrix b . |
| tensorflow::ops::QuantizedMul | Returns x * y element-wise, working on quantized buffers. |
| tensorflow::ops::Range | Creates a sequence of numbers. |
| tensorflow::ops::Real | Returns the real part of a complex number. |
| tensorflow::ops::RealDiv | Returns x / y element-wise for real types. |
| tensorflow::ops::Reciprocal | Computes the reciprocal of x element-wise. |
| tensorflow::ops::RequantizationRange | Given a quantized tensor described by (input, input_min, input_max), outputs a. |
| tensorflow::ops::Requantize | Convert the quantized 'input' tensor into a lower-precision 'output', using the. |
| tensorflow::ops::Rint | Returns element-wise integer closest to x. |
| tensorflow::ops::Round | Rounds the values of a tensor to the nearest integer, element-wise. |
| tensorflow::ops::Rsqrt | Computes reciprocal of square root of x element-wise. |
| tensorflow::ops::SegmentMax | Computes the maximum along segments of a tensor. |
| tensorflow::ops::SegmentMean | Computes the mean along segments of a tensor. |
| tensorflow::ops::SegmentMin | Computes the minimum along segments of a tensor. |
| tensorflow::ops::SegmentProd | Computes the product along segments of a tensor. |
| tensorflow::ops::SegmentSum | Computes the sum along segments of a tensor. |
| tensorflow::ops::Sigmoid | Computes sigmoid of x element-wise. |
| tensorflow::ops::Sign | Returns an element-wise indication of the sign of a number. |
| tensorflow::ops::Sin | Computes sin of x element-wise. |
| tensorflow::ops::Sinh | Computes hyperbolic sine of x element-wise. |
| tensorflow::ops::SparseMatMul | Multiply matrix "a" by matrix "b". |
| tensorflow::ops::SparseSegmentMean | Computes the mean along sparse segments of a tensor. |
| tensorflow::ops::SparseSegmentMeanGrad | Computes gradients for SparseSegmentMean. |
| tensorflow::ops::SparseSegmentMeanWithNumSegments | Computes the mean along sparse segments of a tensor. |
| tensorflow::ops::SparseSegmentSqrtN | Computes the sum along sparse segments of a tensor divided by the sqrt of N. |
| tensorflow::ops::SparseSegmentSqrtNGrad | Computes gradients for SparseSegmentSqrtN. |
| tensorflow::ops::SparseSegmentSqrtNWithNumSegments | Computes the sum along sparse segments of a tensor divided by the sqrt of N. |
| tensorflow::ops::SparseSegmentSum | Computes the sum along sparse segments of a tensor. |
| tensorflow::ops::SparseSegmentSumWithNumSegments | Computes the sum along sparse segments of a tensor. |
| tensorflow::ops::Sqrt | Computes square root of x element-wise. |
| tensorflow::ops::Square | Computes square of x element-wise. |
| tensorflow::ops::SquaredDifference | Returns (x - y)(x - y) element-wise. |
| tensorflow::ops::Subtract | Returns x - y element-wise. |
| tensorflow::ops::Sum | Computes the sum of elements across dimensions of a tensor. |
| tensorflow::ops::Tan | Computes tan of x element-wise. |
| tensorflow::ops::Tanh | Computes hyperbolic tangent of x element-wise. |
| tensorflow::ops::TruncateDiv | Returns x / y element-wise for integer types. |
| tensorflow::ops::TruncateMod | Returns element-wise remainder of division. |
| tensorflow::ops::UnsortedSegmentMax | Computes the maximum along segments of a tensor. |
| tensorflow::ops::UnsortedSegmentMin | Computes the minimum along segments of a tensor. |
| tensorflow::ops::UnsortedSegmentProd | Computes the product along segments of a tensor. |
| tensorflow::ops::UnsortedSegmentSum | Computes the sum along segments of a tensor. |
| tensorflow::ops::Where3 | Selects elements from x or y , depending on condition . |
| tensorflow::ops::Xdivy | Returns 0 if x == 0, and x / y otherwise, elementwise. |
| tensorflow::ops::Xlogy | Returns 0 if x == 0, and x * log(y) otherwise, elementwise. |
| tensorflow::ops::Zeta | Compute the Hurwitz zeta function \((x, q)\). |
Members |
|
|---|---|
| tensorflow::ops::AvgPool | Performs average pooling on the input. |
| tensorflow::ops::AvgPool3D | Performs 3D average pooling on the input. |
| tensorflow::ops::AvgPool3DGrad | Computes gradients of average pooling function. |
| tensorflow::ops::BiasAdd | Adds bias to value . |
| tensorflow::ops::BiasAddGrad | The backward operation for "BiasAdd" on the "bias" tensor. |
| tensorflow::ops::Conv2D | Computes a 2-D convolution given 4-D input and filter tensors. |
| tensorflow::ops::Conv2DBackpropFilter | Computes the gradients of convolution with respect to the filter. |
| tensorflow::ops::Conv2DBackpropInput | Computes the gradients of convolution with respect to the input. |
| tensorflow::ops::Conv3D | Computes a 3-D convolution given 5-D input and filter tensors. |
| tensorflow::ops::Conv3DBackpropFilterV2 | Computes the gradients of 3-D convolution with respect to the filter. |
| tensorflow::ops::Conv3DBackpropInputV2 | Computes the gradients of 3-D convolution with respect to the input. |
| tensorflow::ops::DataFormatDimMap | Returns the dimension index in the destination data format given the one in. |
| tensorflow::ops::DataFormatVecPermute | Returns the permuted vector/tensor in the destination data format given the. |
| tensorflow::ops::DepthwiseConv2dNative | Computes a 2-D depthwise convolution given 4-D input and filter tensors. |
| tensorflow::ops::DepthwiseConv2dNativeBackpropFilter | Computes the gradients of depthwise convolution with respect to the filter. |
| tensorflow::ops::DepthwiseConv2dNativeBackpropInput | Computes the gradients of depthwise convolution with respect to the input. |
| tensorflow::ops::Dilation2D | Computes the grayscale dilation of 4-D input and 3-D filter tensors. |
| tensorflow::ops::Dilation2DBackpropFilter | Computes the gradient of morphological 2-D dilation with respect to the filter. |
| tensorflow::ops::Dilation2DBackpropInput | Computes the gradient of morphological 2-D dilation with respect to the input. |
| tensorflow::ops::Elu | Computes exponential linear: exp(features) - 1 if < 0, features otherwise. |
| tensorflow::ops::FractionalAvgPool | Performs fractional average pooling on the input. |
| tensorflow::ops::FractionalMaxPool | Performs fractional max pooling on the input. |
| tensorflow::ops::FusedBatchNorm | Batch normalization. |
| tensorflow::ops::FusedBatchNormGrad | Gradient for batch normalization. |
| tensorflow::ops::FusedBatchNormGradV2 | Gradient for batch normalization. |
| tensorflow::ops::FusedBatchNormV2 | Batch normalization. |
| tensorflow::ops::FusedPadConv2D | Performs a padding as a preprocess during a convolution. |
| tensorflow::ops::FusedResizeAndPadConv2D | Performs a resize and padding as a preprocess during a convolution. |
| tensorflow::ops::InTopK | Says whether the targets are in the top K predictions. |
| tensorflow::ops::InTopKV2 | Says whether the targets are in the top K predictions. |
| tensorflow::ops::L2Loss | L2 Loss. |
| tensorflow::ops::LRN | Local Response Normalization. |
| tensorflow::ops::LogSoftmax | Computes log softmax activations. |
| tensorflow::ops::MaxPool | Performs max pooling on the input. |
| tensorflow::ops::MaxPool3D | Performs 3D max pooling on the input. |
| tensorflow::ops::MaxPool3DGrad | Computes gradients of max pooling function. |
| tensorflow::ops::MaxPool3DGradGrad | Computes second-order gradients of the maxpooling function. |
| tensorflow::ops::MaxPoolGradGrad | Computes second-order gradients of the maxpooling function. |
| tensorflow::ops::MaxPoolGradGradV2 | Computes second-order gradients of the maxpooling function. |
| tensorflow::ops::MaxPoolGradGradWithArgmax | Computes second-order gradients of the maxpooling function. |
| tensorflow::ops::MaxPoolGradV2 | Computes gradients of the maxpooling function. |
| tensorflow::ops::MaxPoolV2 | Performs max pooling on the input. |
| tensorflow::ops::MaxPoolWithArgmax | Performs max pooling on the input and outputs both max values and indices. |
| tensorflow::ops::NthElement | Finds values of the n -th order statistic for the last dimension. |
| tensorflow::ops::QuantizedAvgPool | Produces the average pool of the input tensor for quantized types. |
| tensorflow::ops::QuantizedBatchNormWithGlobalNormalization | Quantized Batch normalization. |
| tensorflow::ops::QuantizedBiasAdd | Adds Tensor 'bias' to Tensor 'input' for Quantized types. |
| tensorflow::ops::QuantizedConv2D | Computes a 2D convolution given quantized 4D input and filter tensors. |
| tensorflow::ops::QuantizedMaxPool | Produces the max pool of the input tensor for quantized types. |
| tensorflow::ops::QuantizedRelu | Computes Quantized Rectified Linear: max(features, 0) |
| tensorflow::ops::QuantizedRelu6 | Computes Quantized Rectified Linear 6: min(max(features, 0), 6) |
| tensorflow::ops::QuantizedReluX | Computes Quantized Rectified Linear X: min(max(features, 0), max_value) |
| tensorflow::ops::Relu | Computes rectified linear: max(features, 0) . |
| tensorflow::ops::Relu6 | Computes rectified linear 6: min(max(features, 0), 6) . |
| tensorflow::ops::Selu | Computes scaled exponential linear: scale * alpha * (exp(features) - 1) |
| tensorflow::ops::Softmax | Computes softmax activations. |
| tensorflow::ops::SoftmaxCrossEntropyWithLogits | Computes softmax cross entropy cost and gradients to backpropagate. |
| tensorflow::ops::Softplus | Computes softplus: log(exp(features) + 1) . |
| tensorflow::ops::Softsign | Computes softsign: features / (abs(features) + 1) . |
| tensorflow::ops::SparseSoftmaxCrossEntropyWithLogits | Computes softmax cross entropy cost and gradients to backpropagate. |
| tensorflow::ops::TopK | Finds values and indices of the k largest elements for the last dimension. |
Members |
|
|---|---|
| tensorflow::ops::NoOp | Does nothing. |
Members |
|
|---|---|
| tensorflow::ops::DecodeCSV | Convert CSV records to tensors. |
| tensorflow::ops::DecodeCompressed | Decompress strings. |
| tensorflow::ops::DecodeJSONExample | Convert JSON-encoded Example records to binary protocol buffer strings. |
| tensorflow::ops::DecodeRaw | Reinterpret the bytes of a string as a vector of numbers. |
| tensorflow::ops::ParseExample | Transforms a vector of brain.Example protos (as strings) into typed tensors. |
| tensorflow::ops::ParseSequenceExample | Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors. |
| tensorflow::ops::ParseSingleExample | Transforms a tf.Example proto (as a string) into typed tensors. |
| tensorflow::ops::ParseSingleSequenceExample | Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors. |
| tensorflow::ops::ParseTensor | Transforms a serialized tensorflow.TensorProto proto into a Tensor. |
| tensorflow::ops::SerializeTensor | Transforms a Tensor into a serialized TensorProto proto. |
| tensorflow::ops::StringToNumber | Converts each string in the input Tensor to the specified numeric type. |
Members |
|
|---|---|
| tensorflow::ops::Multinomial | Draws samples from a multinomial distribution. |
| tensorflow::ops::ParameterizedTruncatedNormal | Outputs random values from a normal distribution. |
| tensorflow::ops::RandomGamma | Outputs random values from the Gamma distribution(s) described by alpha. |
| tensorflow::ops::RandomNormal | Outputs random values from a normal distribution. |
| tensorflow::ops::RandomPoissonV2 | Outputs random values from the Poisson distribution(s) described by rate. |
| tensorflow::ops::RandomShuffle | Randomly shuffles a tensor along its first dimension. |
| tensorflow::ops::RandomUniform | Outputs random values from a uniform distribution. |
| tensorflow::ops::RandomUniformInt | Outputs random integers from a uniform distribution. |
| tensorflow::ops::TruncatedNormal | Outputs random values from a truncated normal distribution. |
Members |
|
|---|---|
| tensorflow::ops::AddManySparseToTensorsMap | Add an N -minibatch SparseTensor to a SparseTensorsMap , return N handles. |
| tensorflow::ops::AddSparseToTensorsMap | Add a SparseTensor to a SparseTensorsMap return its handle. |
| tensorflow::ops::DeserializeManySparse | Deserialize and concatenate SparseTensors from a serialized minibatch. |
| tensorflow::ops::DeserializeSparse | Deserialize SparseTensor objects. |
| tensorflow::ops::SerializeManySparse | Serialize an N -minibatch SparseTensor into an [N, 3] Tensor object. |
| tensorflow::ops::SerializeSparse | Serialize a SparseTensor into a [3] Tensor object. |
| tensorflow::ops::SparseAdd | Adds two SparseTensor objects to produce another SparseTensor . |
| tensorflow::ops::SparseAddGrad | The gradient operator for the SparseAdd op. |
| tensorflow::ops::SparseConcat | Concatenates a list of SparseTensor along the specified dimension. |
| tensorflow::ops::SparseCross | Generates sparse cross from a list of sparse and dense tensors. |
| tensorflow::ops::SparseDenseCwiseAdd | Adds up a SparseTensor and a dense Tensor, using these special rules: |
| tensorflow::ops::SparseDenseCwiseDiv | Component-wise divides a SparseTensor by a dense Tensor. |
| tensorflow::ops::SparseDenseCwiseMul | Component-wise multiplies a SparseTensor by a dense Tensor. |
| tensorflow::ops::SparseFillEmptyRows | Fills empty rows in the input 2-D SparseTensor with a default value. |
| tensorflow::ops::SparseFillEmptyRowsGrad | The gradient of SparseFillEmptyRows. |
| tensorflow::ops::SparseReduceMax | Computes the max of elements across dimensions of a SparseTensor. |
| tensorflow::ops::SparseReduceMaxSparse | Computes the max of elements across dimensions of a SparseTensor. |
| tensorflow::ops::SparseReduceSum | Computes the sum of elements across dimensions of a SparseTensor. |
| tensorflow::ops::SparseReduceSumSparse | Computes the sum of elements across dimensions of a SparseTensor. |
| tensorflow::ops::SparseReorder | Reorders a SparseTensor into the canonical, row-major ordering. |
| tensorflow::ops::SparseReshape | Reshapes a SparseTensor to represent values in a new dense shape. |
| tensorflow::ops::SparseSlice | Slice a SparseTensor based on the start and size . |
| tensorflow::ops::SparseSliceGrad | The gradient operator for the SparseSlice op. |
| tensorflow::ops::SparseSoftmax | Applies softmax to a batched N-D SparseTensor . |
| tensorflow::ops::SparseSparseMaximum | Returns the element-wise max of two SparseTensors. |
| tensorflow::ops::SparseSparseMinimum | Returns the element-wise min of two SparseTensors. |
| tensorflow::ops::SparseSplit | Split a SparseTensor into num_split tensors along one dimension. |
| tensorflow::ops::SparseTensorDenseAdd | Adds up a SparseTensor and a dense Tensor , producing a dense Tensor . |
| tensorflow::ops::SparseTensorDenseMatMul | Multiply SparseTensor (of rank 2) "A" by dense matrix "B". |
| tensorflow::ops::TakeManySparseFromTensorsMap | Converts a sparse representation into a dense tensor. |
Members |
|
|---|---|
| tensorflow::ops::Assign | Update 'ref' by assigning 'value' to it. |
| tensorflow::ops::AssignAdd | Update 'ref' by adding 'value' to it. |
| tensorflow::ops::AssignSub | Update 'ref' by subtracting 'value' from it. |
| tensorflow::ops::CountUpTo | Increments 'ref' until it reaches 'limit'. |
| tensorflow::ops::DestroyTemporaryVariable | Destroys the temporary variable and returns its final value. |
| tensorflow::ops::IsVariableInitialized | Checks whether a tensor has been initialized. |
| tensorflow::ops::ResourceCountUpTo | Increments variable pointed to by 'resource' until it reaches 'limit'. |
| tensorflow::ops::ResourceScatterNdAdd | Adds sparse updates to individual values or slices within a given. |
| tensorflow::ops::ResourceScatterNdUpdate | Applies sparse updates to individual values or slices within a given. |
| tensorflow::ops::ScatterAdd | Adds sparse updates to a variable reference. |
| tensorflow::ops::ScatterDiv | Divides a variable reference by sparse updates. |
| tensorflow::ops::ScatterMax | Reduces sparse updates into a variable reference using the max operation. |
| tensorflow::ops::ScatterMin | Reduces sparse updates into a variable reference using the min operation. |
| tensorflow::ops::ScatterMul | Multiplies sparse updates into a variable reference. |
| tensorflow::ops::ScatterNdAdd | Applies sparse addition between updates and individual values or slices. |
| tensorflow::ops::ScatterNdSub | Applies sparse subtraction between updates and individual values or slices. |
| tensorflow::ops::ScatterNdUpdate | Applies sparse updates to individual values or slices within a given. |
| tensorflow::ops::ScatterSub | Subtracts sparse updates to a variable reference. |
| tensorflow::ops::ScatterUpdate | Applies sparse updates to a variable reference. |
| tensorflow::ops::TemporaryVariable | Returns a tensor that may be mutated, but only persists within a single step. |
| tensorflow::ops::Variable | Holds state in the form of a tensor that persists across steps. |
Members |
|
|---|---|
| tensorflow::ops::AsString | Converts each entry in the given tensor to strings. |
| tensorflow::ops::DecodeBase64 | Decode web-safe base64-encoded strings. |
| tensorflow::ops::EncodeBase64 | Encode strings into web-safe base64 format. |
| tensorflow::ops::ReduceJoin | Joins a string Tensor across the given dimensions. |
| tensorflow::ops::RegexFullMatch | Check if the input matches the regex pattern. |
| tensorflow::ops::RegexReplace | Replaces the match of pattern in input with rewrite. |
| tensorflow::ops::StringFormat | Formats a string template using a list of tensors. |
| tensorflow::ops::StringJoin | Joins the strings in the given list of string tensors into one tensor;. |
| tensorflow::ops::StringLength | String lengths of input . |
| tensorflow::ops::StringSplit | Split elements of input based on delimiter into a SparseTensor . |
| tensorflow::ops::StringSplitV2 | Split elements of source based on sep into a SparseTensor . |
| tensorflow::ops::StringStrip | Strip leading and trailing whitespaces from the Tensor. |
| tensorflow::ops::StringToHashBucket | Converts each string in the input Tensor to its hash mod by a number of buckets. |
| tensorflow::ops::StringToHashBucketFast | Converts each string in the input Tensor to its hash mod by a number of buckets. |
| tensorflow::ops::StringToHashBucketStrong | Converts each string in the input Tensor to its hash mod by a number of buckets. |
| tensorflow::ops::Substr | Return substrings from Tensor of strings. |
| tensorflow::ops::UnicodeScript | Determine the script codes of a given tensor of Unicode integer code points. |
Members |
|
|---|---|
| tensorflow::ops::ApplyAdadelta | Update '*var' according to the adadelta scheme. |
| tensorflow::ops::ApplyAdagrad | Update '*var' according to the adagrad scheme. |
| tensorflow::ops::ApplyAdagradDA | Update '*var' according to the proximal adagrad scheme. |
| tensorflow::ops::ApplyAdam | Update '*var' according to the Adam algorithm. |
| tensorflow::ops::ApplyAddSign | Update '*var' according to the AddSign update. |
| tensorflow::ops::ApplyCenteredRMSProp | Update '*var' according to the centered RMSProp algorithm. |
| tensorflow::ops::ApplyFtrl | Update '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::ApplyFtrlV2 | Update '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::ApplyGradientDescent | Update '*var' by subtracting 'alpha' * 'delta' from it. |
| tensorflow::ops::ApplyMomentum | Update '*var' according to the momentum scheme. |
| tensorflow::ops::ApplyPowerSign | Update '*var' according to the AddSign update. |
| tensorflow::ops::ApplyProximalAdagrad | Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. |
| tensorflow::ops::ApplyProximalGradientDescent | Update '*var' as FOBOS algorithm with fixed learning rate. |
| tensorflow::ops::ApplyRMSProp | Update '*var' according to the RMSProp algorithm. |
| tensorflow::ops::ResourceApplyAdadelta | Update '*var' according to the adadelta scheme. |
| tensorflow::ops::ResourceApplyAdagrad | Update '*var' according to the adagrad scheme. |
| tensorflow::ops::ResourceApplyAdagradDA | Update '*var' according to the proximal adagrad scheme. |
| tensorflow::ops::ResourceApplyAdam | Update '*var' according to the Adam algorithm. |
| tensorflow::ops::ResourceApplyAddSign | Update '*var' according to the AddSign update. |
| tensorflow::ops::ResourceApplyCenteredRMSProp | Update '*var' according to the centered RMSProp algorithm. |
| tensorflow::ops::ResourceApplyFtrl | Update '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::ResourceApplyFtrlV2 | Update '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::ResourceApplyGradientDescent | Update '*var' by subtracting 'alpha' * 'delta' from it. |
| tensorflow::ops::ResourceApplyMomentum | Update '*var' according to the momentum scheme. |
| tensorflow::ops::ResourceApplyPowerSign | Update '*var' according to the AddSign update. |
| tensorflow::ops::ResourceApplyProximalAdagrad | Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. |
| tensorflow::ops::ResourceApplyProximalGradientDescent | Update '*var' as FOBOS algorithm with fixed learning rate. |
| tensorflow::ops::ResourceApplyRMSProp | Update '*var' according to the RMSProp algorithm. |
| tensorflow::ops::ResourceSparseApplyAdadelta | var: Should be from a Variable(). |
| tensorflow::ops::ResourceSparseApplyAdagrad | Update relevant entries in '*var' and '*accum' according to the adagrad scheme. |
| tensorflow::ops::ResourceSparseApplyAdagradDA | Update entries in '*var' and '*accum' according to the proximal adagrad scheme. |
| tensorflow::ops::ResourceSparseApplyCenteredRMSProp | Update '*var' according to the centered RMSProp algorithm. |
| tensorflow::ops::ResourceSparseApplyFtrl | Update relevant entries in '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::ResourceSparseApplyFtrlV2 | Update relevant entries in '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::ResourceSparseApplyMomentum | Update relevant entries in '*var' and '*accum' according to the momentum scheme. |
| tensorflow::ops::ResourceSparseApplyProximalAdagrad | Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. |
| tensorflow::ops::ResourceSparseApplyProximalGradientDescent | Sparse update '*var' as FOBOS algorithm with fixed learning rate. |
| tensorflow::ops::ResourceSparseApplyRMSProp | Update '*var' according to the RMSProp algorithm. |
| tensorflow::ops::SparseApplyAdadelta | var: Should be from a Variable(). |
| tensorflow::ops::SparseApplyAdagrad | Update relevant entries in '*var' and '*accum' according to the adagrad scheme. |
| tensorflow::ops::SparseApplyAdagradDA | Update entries in '*var' and '*accum' according to the proximal adagrad scheme. |
| tensorflow::ops::SparseApplyCenteredRMSProp | Update '*var' according to the centered RMSProp algorithm. |
| tensorflow::ops::SparseApplyFtrl | Update relevant entries in '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::SparseApplyFtrlV2 | Update relevant entries in '*var' according to the Ftrl-proximal scheme. |
| tensorflow::ops::SparseApplyMomentum | Update relevant entries in '*var' and '*accum' according to the momentum scheme. |
| tensorflow::ops::SparseApplyProximalAdagrad | Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. |
| tensorflow::ops::SparseApplyProximalGradientDescent | Sparse update '*var' as FOBOS algorithm with fixed learning rate. |
| tensorflow::ops::SparseApplyRMSProp | Update '*var' according to the RMSProp algorithm. |
Members |
|
|---|---|
| tensorflow::ops::Fact | Output a fact about factorials. |


Input Box with only Prompt
Input Box with a Title
Input Box with a Prompt,Title and Default value
Input Box with a Prompt,Title,Default and only XPos
Input Box with a Prompt,Title and Default and only YPos
Input Box with a Prompt,Title and Defaultvalue and XPos and YPos