INSTALL-INFO
Section: User Commands (1)Updated: September 2008
Index Return to Main Contents
NAME
install-info - update info/dir entriesSYNOPSIS
install-info [OPTION]... [INFO-FILE [DIR-FILE]]DESCRIPTION
Add or remove entries in INFO-FILE from the Info directory DIR-FILE.OPTIONS
- --debug
- report what is being done.
- --delete
- delete existing entries for INFO-FILE from DIR-FILE; don't insert any new entries.
- --description=TEXT
- the description of the entry is TEXT; used with the --name option to become synonymous with the --entry option.
- --dir-file=NAME
- specify file name of Info directory file; equivalent to using the DIR-FILE argument.
- --dry-run
- same as --test.
- --entry=TEXT
- insert TEXT as an Info directory entry. TEXT is written as an Info menu item line followed
- by zero or more extra lines starting with whitespace.
- If you specify more than one entry, all are added. If you don't specify any entries, they are determined
- from information in the Info file itself.
- When removing, TEXT specifies the entry to remove. TEXT is only removed as a last resort, if the entry as determined from the Info file is not present, and the basename of the Info file isn't found either.
- --help
- display this help and exit.
- --info-dir=DIR
- same as --dir-file=DIR/dir.
- --info-file=FILE
- specify Info file to install in the directory; equivalent to using the INFO-FILE argument.
- --item=TEXT
- same as --entry=TEXT.
- --keep-old
- do not replace entries, or remove empty sections.
- --menuentry=TEXT
- same as --name=TEXT.
- --name=TEXT
- the name of the entry is TEXT; used with --description to become synonymous with the --entry option.
- --no-indent
- do not format new entries in the DIR file.
- --quiet
- suppress warnings.
- --regex=R
- put this file's entries in all sections that match the regular expression R (ignoring case).
- --remove
- same as --delete.
- --remove-exactly
- only remove if the info file name matches exactly; suffixes such as .info and .gz are not ignored.
- --section=SEC
- put entries in section SEC of the directory. If you specify more than one section, all the entries
- are added in each of the sections.
- If you don't specify any sections, they are determined
- from information in the Info file itself.
- --section R SEC
- equivalent to --regex=R --section=SEC --add-once.
- --silent
- suppress warnings.
- --test
- suppress updating of DIR-FILE.
- --version
- display version information and exit.
REPORTING BUGS
Email bug reports to<EMAIL_ADDRESS>general questions and discussion to<EMAIL_ADDRESS>Texinfo home page: http://www.gnu.org/software/texinfo/COPYRIGHT
Copyright © 2008 Free Software Foundation, Inc. License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law.
SEE ALSO
The full documentation for install-info is maintained as a Texinfo manual. If the info and install-info programs are properly installed at your site, the command- info install-info
should give you access to the complete manual.
Index
This document was created by man2html, using the manual pages.
Time: 05:29:04 GMT, December 24, 2015


Disney Plus: Here's The Australian Launch List [Updated]
by Chris JagerWhat movies and TV shows are coming to Disney Plus in Australia? It's a question we've all been pondering for weeks. As Netflix has shown, just because a title is available in the US doesn't mean it will be appearing Down Under.
Will we be getting the full Star Wars trilogy? What about The Simpsons? And how many Disney Classics will be making the cut? Today, Disney Australia finally let the mouse out of the bag and the news is good. Here are the chief titles that will be available in Australia on day one.
The list below isn't exhaustive. Rather, it represents the cream that Disney Plus has to offer. There are plenty of additional movies and TV shows on the service too. (Indeed, according to Disney, Australia will be receiving over 600 films and 7000 TV show episodes at launch!)
Without further ado, here are the highlights for the Australian version of Disney+:
Disney+ Originals
- The Mandalorian
- Lady and the Tramp
- High School Musical: The Musical: The Series
- Encore!
- Noelle
Marvel movies on Disney Plus
- The Avengers
- Avengers: Age of Ultron
- Avengers: Infinity War
- Avengers: Endgame
- Ant-Man
- Ant-Man and the Wasp
- Black Panther
- Captain America: The First Avenger
- Captain America: The Winter Soldier
- Captain America: Civil War
- Captain Marvel
- Doctor Strange
- Guardians of the Galaxy
- Guardians of the Galaxy Vol. 2
- Iron Man
- Iron Man 2
- Iron Man 3
- Thor
- Thor: The Dark World
- Thor: Ragnarok
- Marvel animated series (assorted)
Star Wars movies / shows on Disney Plus
- Episode 1: The Phantom Menace
- Episode II: Attack of the Clones
- Epsiode III: Revenge of the Sith
- Episode IV: Star Wars: A New Hope
- Episode V: The Empire Strikes Back
- Episode VI: Return of the Jedi
- Episode VII: The Force Awakens
- Rogue One
- Solo
- The Clone Wars
- The Yoda Chronicles
- Star Wars Rebels
- LEGO Droid Tales
Disney movies on Disney Plus
- Alice In Wonderland
- Alice Through the Looking Glass
- 101 Dalmatians (live action)
- 101 Dalmatians (animated)
- Aladdin (animated)
- Beauty and the Beast (live action)
- Beauty and the Beast (animated)
- Big Hero 6
- Christopher Robin
- Dumbo (live action)
- Dumbo (animated)
- Frozen
- Maleficent
- Mary Poppins
- Mary Poppins Returns
- Moana
- Mulan
- Ralph Breaks The Internet
- Tangled
- The Jungle Book (live action)
- The Lion King (original)
- The Little Mermaid
Pixar movies on Disney Plus
- Brave
- Cars
- Coco
- Finding Dory and Finding Nemo
- Inside Out
- Monsters, Inc.
- Monsters University
- The Incredibles
- Toy Story
- Toy Story 2
- Toy Story 3
- Ratatouille
- Up!
- Wall-E
Other movies on Disney Plus
- Home Alone
- Home Alone 2
- Home Alone 3
- Remember The Titans
- Sister Act
- The Chronicles of Narnia
- The Mighty Ducks
- The Parent Trap
- The Princess Diaries
- Three Men And A Baby
- Tron
Disney Channel / Disney Junior content on Disney Plus
- Camp Rock
- Descendants
- Hannah Montana
- High School Musical 1, 2 and 3
- Jake and the Never Land Pirates
- Mickey and the Roadster Racers
- Mickey Mouse Clubhouse
- Puppy Dog Pals
- Teen Beach Movie
- Vampirina
Other highlights
- Avatar
- Free Solo (National Geographic's Academy Award winning documentary)
- Pirates of the Caribbean
- The Simpsons (seasons 1-29)
And there you have it! Quite a solid lineup, I'm sure you'll agree. We're particularly excited about on-demand access to The Simpsons seasons 1 through 29. (This is something that will surely irk Foxtel, which adopted Bart as its unofficial mascot for years.)
Disney Plus launches in Australia today (November 19) for $8.99 per month. The app will be available om iOS, Android, Apple TV, Chromecast, Amazon Fire TV, PlayStation 4, Xbox One and select smart TVs. Click here to subscribe!
[Via Disney Australia]


The art of leveraging time
Oh dear, if I had a dime, every time(oh just made a rhime, twice :D) someone complained to me about the lack of time. Even if that guy is…myself. Life is a bi..h, according to folk wisdom, but if it is “fair and square” with one thing it is time.
We all have 24h per day to achieve our goals, complete our personal and professional tasks, spend quality time with our family and friends, take care of our bodies and so much more.
Thing is, that some people make it everyday, and achieve in a year things that others need 5, 10 or even more, years to achieve.
I was always fascinated by this “superhuman skill”, so for the past few years I was following the below process:
- Reading about the subject
- Analysing and applying in real life what I have learned
- Evaluating my results
- Decide to keep the practice or throw it
Below are some of the best tips I have come, during all this time.
The Pareto principle
Also known as the 80/20 law. This means that 20% of your tasks will yield 80% of your results. And the rest 80% of the tasks, will give you the rest 20% of your results.
So starting from today, make sure that everything you do during the day, is giving back the maximum results, amongst the remaining tasks. Maximum according to what, you ask?
Well, that depends on you. Each one of us have different goals.
For a career-(wo)man, it might be to acquire a skill that will give her/him the best possible career prospects or a big raise or one step closes to a big career dream.
For a single mother of 5(!!), it might be to make sure, the well being of her kids, is not even remotely threatened.
Read lots of books
If time is money, then reading a book, which contains compressed knowledge, will yield great knowledge results, which in turn will help you improve, in a relavant to the book, sector.
Additionally, books can help you enter a wind-down-before-night-sleep phase(so you both learn and relax) and power on the brain engine again in the morning.
This is becoming even better, if it is done during the so-called “dead hours”, like waiting at a doctor’s waiting room or during commuting.
Hmm, how about squeezing a few pages when waiting for your significant other, but they are late again? :)
Audiobooks
For more advanced anti-dead time users. Audiobooks books have all the pros of physical books and they can also be used during walking. A popular alternative is listening to audiobooks, during the cardio training in the gym(eg treadmill or bike).
I do not reccommend it (and try this on your own risk, as it dangerous and potentially illegal, depending on where you live), but I know people who do it while driving.
Exercise during your commute
As you have already figured it out, I think daily commute is a big time liability, but you can be compensated quite easily. So, another idea, would be to cycle till your office.
You will reach the office fast enough (well, maybe not as fast as with your car), the initial cost of buying a bike is quickly amortised, and by the end of the day, you would have done more cardio than most people around you, do in a week. Good deal, huh?
Use great hardware to eliminate unnecessary delays.
Do you remember when you had to send a couple of emails, and it took you 5 minutes each, without taking into account, the time spent on typing?
Have you ever done the maths, to see how much time you waste on such things? Quality hardware is a great investment, and having a Scrooge McDuck mentality is not a wise choice, if you care about your time.
I am not saying to buy the most expensive gadget or computer you can find, but to be willing to spend a few more bucks to achieve higher quality levels.
No snooze
This alone, have made a significant positive change in my life and especially to my productivity. Why you ask, why is so bad staying in bed for 20 more minutes?
Well apart for not taking advantage of your time(which is why this article is about), here are a couple of articles(and they are only the tip of the iceberg) about the issue:
If you need to relax, do it now
We are not robots and we are not living to just cross-out tasks from our checklist. Thus, you need to listen to your body and take care of it, so that is powerful and productive.
 :)).
If you have forgotten what was the last time you laughed extremely hard, close the f@cking IDE and go watch a comedy with your significant other.
If you feel the pace of your life is unsustainable, create a ‘No-electronics-Saturday’.  And if you employ the Pareto principle, you might realise that you are overcommiting without any actual returns.
The end
Thank you for reading this article. I hope those tips, will help you ‘generate’ more time for yourselves and what you think is important. And if so, please give me some ‘love’ with the reaction buttons below, to show me your appreciation. :)
As always, feel free to contribute to the comments section with your own special tips.


{% extends 'common/base_login.html' %} {% import 'bootstrap/wtf.html' as wtf %} {% import 'common/_formshelpers.html' as termsbox %} {% block cardtitle %} CONP Portal: Register New User {% endblock %} {% block appcontent %}


SCENE IV. The Shepherd's cottage.
 This your sheep-shearing
- Is as a meeting of the petty gods,
- And you the queen on't.
- PERDITA
- Sir, my gracious lord,
- To chide at your extremes it not becomes me:
- O, pardon, that I name them! Your high self,
- The gracious mark o' the land, you have obscured
- With a swain's wearing, and me, poor lowly maid,
- Most goddess-like prank'd up: but that our feasts
- In every mess have folly and the feeders
- Digest it with a custom, I should blush
- To see you so attired, sworn, I think,
- To show myself a glass.
- FLORIZEL
- I bless the time
- When my good falcon made her flight across
- Thy father's ground.
- PERDITA
- Now Jove afford you cause!
- To me the difference forges dread; your greatness
- Hath not been used to fear. Even now I tremble
- To think your father, by some accident,
- Should pass this way as you did: O, the Fates!
- How would he look, to see his work so noble
- Vilely bound up? What would he say? Or how
- Should I, in these my borrow'd flaunts, behold
- The sternness of his presence?
- FLORIZEL
- Apprehend
- Nothing but jollity. The gods themselves,
- Humbling their deities to love, have taken
- The shapes of beasts upon them: Jupiter
- Became a bull, and bellow'd; the green Neptune
- A ram, and bleated; and the fire-robed god,
- Golden Apollo, a poor humble swain,
- As I seem now. Their transformations
- Were never for a piece of beauty rarer,
- Nor in a way so chaste, since my desires
- Run not before mine honour, nor my lusts
- Burn hotter than my faith.
- PERDITA
- O, but, sir,
- Your resolution cannot hold, when 'tis
- Opposed, as it must be, by the power of the king:
- One of these two must be necessities,
- Which then will speak, that you must
- change this purpose,
- Or I my life.
- FLORIZEL
- Thou dearest Perdita,
- With these forced thoughts, I prithee, darken not
- The mirth o' the feast. Or I'll be thine, my fair,
- Or not my father's. For I cannot be
- Mine own, nor any thing to any, if
- I be not thine. To this I am most constant,
- Though destiny say no. Be merry, gentle;
- Strangle such thoughts as these with any thing
- That you behold the while. Your guests are coming:
- Lift up your countenance, as it were the day
- Of celebration of that nuptial which
- We two have sworn shall come.
- PERDITA
- O lady Fortune,
- Stand you auspicious!
- FLORIZEL
- See, your guests approach:
- Address yourself to entertain them sprightly,
- And let's be red with mirth.
Enter Shepherd, Clown, MOPSA, DORCAS, and
others, with POLIXENES and CAMILLO disguised
- Shepherd
- Fie, daughter! when my old wife lived, upon
- This day she was both pantler, butler, cook,
- Both dame and servant; welcomed all, served all;
- Would sing her song and dance her turn; now here,
- At upper end o' the table, now i' the middle;
- On his shoulder, and his; her face o' fire
- With labour and the thing she took to quench it,
- She would to each one sip. You are retired,
- As if you were a feasted one and not
- The hostess of the meeting: pray you, bid
- These unknown friends to's welcome; for it is
- A way to make us better friends, more known.
- Come, quench your blushes and present yourself
- That which you are, mistress o' the feast: come on,
- And bid us welcome to your sheep-shearing,
- As your good flock shall prosper.
- PERDITA
- To POLIXENES Sir, welcome:
- It is my father's will I should take on me
- The hostess-ship o' the day.
- To CAMILLO
- You're welcome, sir.
- Give me those flowers there, Dorcas. Reverend sirs,
- For you there's rosemary and rue; these keep
- Seeming and savour all the winter long:
- Grace and remembrance be to you both,
- And welcome to our shearing!
- POLIXENES
- Shepherdess,
- A fair one are you — well you fit our ages
- With flowers of winter.
- PERDITA
- Sir, the year growing ancient,
- Not yet on summer's death, nor on the birth
- Of trembling winter, the fairest
- flowers o' the season
- Are our carnations and streak'd gillyvors,
- Which some call nature's bastards: of that kind
- Our rustic garden's barren; and I care not
- To get slips of them.
- POLIXENES
- Wherefore, gentle maiden,
- Do you neglect them?
- PERDITA
- For I have heard it said
- There is an art which in their piedness shares
- With great creating nature.
- POLIXENES
- Say there be;
- Yet nature is made better by no mean
- But nature makes that mean: so, over that art
- Which you say adds to nature, is an art
- That nature makes. You see, sweet maid, we marry
- A gentler scion to the wildest stock,
- And make conceive a bark of baser kind
- By bud of nobler race: this is an art
- Which does mend nature, change it rather, but
- The art itself is nature.
- PERDITA
- So it is.
- POLIXENES
- Then make your garden rich in gillyvors,
- And do not call them bastards.
- PERDITA
- I'll not put
- The dibble in earth to set one slip of them;
- No more than were I painted I would wish
- This youth should say 'twere well and only therefore
- Desire to breed by me. Here's flowers for you;
- Hot lavender, mints, savoury, marjoram;
- The marigold, that goes to bed wi' the sun
- And with him rises weeping: these are flowers
- Of middle summer, and I think they are given
- To men of middle age. You're very welcome.
- CAMILLO
- I should leave grazing, were I of your flock,
- And only live by gazing.
- PERDITA
- Out, alas!
- You'd be so lean, that blasts of January
- Would blow you through and through.
- Now, my fair'st friend,
- I would I had some flowers o' the spring that might
- Become your time of day; and yours, and yours,
- That wear upon your virgin branches yet
- Your maidenheads growing: O Proserpina,
- For the flowers now, that frighted thou let'st fall
- From Dis's waggon! daffodils,
- That come before the swallow dares, and take
- The winds of March with beauty; violets dim,
- But sweeter than the lids of Juno's eyes
- Or Cytherea's breath; pale primroses
- That die unmarried, ere they can behold
- Bight Phoebus in his strength — a malady
- Most incident to maids; bold oxlips and
- The crown imperial; lilies of all kinds,
- The flower-de-luce being one! O, these I lack,
- To make you garlands of, and my sweet friend,
- To strew him o'er and o'er!
- FLORIZEL
- What, like a corse?
- PERDITA
- No, like a bank for love to lie and play on;
- Not like a corse; or if, not to be buried,
- But quick and in mine arms. Come, take your flowers:
- Methinks I play as I have seen them do
- In Whitsun pastorals: sure this robe of mine
- Does change my disposition.
- FLORIZEL
- What you do
- Still betters what is done. When you speak, sweet.
- I'ld have you do it ever: when you sing,
- I'ld have you buy and sell so, so give alms,
- Pray so; and, for the ordering your affairs,
- To sing them too: when you do dance, I wish you
- A wave o' the sea, that you might ever do
- Nothing but that; move still, still so,
- And own no other function: each your doing,
- So singular in each particular,
- Crowns what you are doing in the present deed,
- That all your acts are queens.

- FLORIZEL
- I think you have
- As little skill to fear as I have purpose
- To put you to't. But come; our dance, I pray:
- Your hand, my Perdita: so turtles pair,
- That never mean to part.
- PERDITA
- I'll swear for 'em.
- POLIXENES
- This is the prettiest low-born lass that ever
- Ran on the green-sward: nothing she does or seems
- But smacks of something greater than herself,
- Too noble for this place.
- CAMILLO
- He tells her something
- That makes her blood look out: good sooth, she is
- The queen of curds and cream.

- DORCAS
- Mopsa must be your mistress: marry, garlic,
- To mend her kissing with!
- MOPSA
- Now, in good time!
- Clown
- Not a word, a word; we stand upon our manners.
- Come, strike up!
Music. Here a dance of Shepherds and
Shepherdesses
- POLIXENES
- Pray, good shepherd, what fair swain is this
- Which dances with your daughter?
- Shepherd
- They call him Doricles; and boasts himself
- To have a worthy feeding: but I have it
- Upon his own report and I believe it;
- He looks like sooth. He says he loves my daughter:
- I think so too; for never gazed the moon
- Upon the water as he'll stand and read
- As 'twere my daughter's eyes: and, to be plain.
- I think there is not half a kiss to choose
- Who loves another best.
- POLIXENES
- She dances featly.
- Shepherd
- So she does any thing; though I report it,
- That should be silent: if young Doricles
- Do light upon her, she shall bring him that
- Which he not dreams of.
Enter Servant
- Servant
- O master, if you did but hear the pedlar at the
- door, you would never dance again after a tabour and
- pipe; no, the bagpipe could not move you: he sings
- several tunes faster than you'll tell money; he
- utters them as he had eaten ballads and all men's
- ears grew to his tunes.
- Clown
- He could never come better; he shall come in. I
- love a ballad but even too well, if it be doleful
- matter merrily set down, or a very pleasant thing
- indeed and sung lamentably.
- Servant
- He hath songs for man or woman, of all sizes; no
- milliner can so fit his customers with gloves: he
- has the prettiest love-songs for maids; so without
- bawdry, which is strange; with such delicate
- burthens of dildos and fadings, 'jump her and thump
- her;' and where some stretch-mouthed rascal would,
- as it were, mean mischief and break a foul gap into
- the matter, he makes the maid to answer 'Whoop, do me
- no harm, good man;' puts him off, slights him, with
- 'Whoop, do me no harm, good man.'
- POLIXENES
- This is a brave fellow.
- Clown
- Believe me, thou talkest of an admirable conceited
- fellow. Has he any unbraided wares?
- Servant
- He hath ribbons of an the colours i' the rainbow;
- points more than all the lawyers in Bohemia can
- learnedly handle, though they come to him by the
- gross: inkles, caddisses, cambrics, lawns: why, he
- sings 'em over as they were gods or goddesses; you
- would think a smock were a she-angel, he so chants
- to the sleeve-hand and the work about the square on't.
- Clown
- Prithee bring him in; and let him approach singing.
- PERDITA
- Forewarn him that he use no scurrilous words in 's tunes.
Exit Servant
- Clown
- You have of these pedlars, that have more in them
- than you'ld think, sister.
- PERDITA
- Ay, good brother, or go about to think.
Enter AUTOLYCUS, singing
- AUTOLYCUS
- Lawn as white as driven snow;
- Cyprus black as e'er was crow;
- Gloves as sweet as damask roses;
- Masks for faces and for noses;
- Bugle bracelet, necklace amber,
- Perfume for a lady's chamber;
- Golden quoifs and stomachers,
- For my lads to give their dears:
- Pins and poking-sticks of steel,
- What maids lack from head to heel:
- Come buy of me, come; come buy, come buy;
- Buy lads, or else your lasses cry: Come buy.
- Clown
- If I were not in love with Mopsa, thou shouldst take
- no money of me; but being enthralled as I am, it
- will also be the bondage of certain ribbons and gloves.
- MOPSA
- I was promised them against the feast; but they come
- not too late now.
- DORCAS
- He hath promised you more than that, or there be liars.
- MOPSA
- He hath paid you all he promised you; may be, he has
- paid you more, which will shame you to give him again.
- Clown
- Is there no manners left among maids? will they
- wear their plackets where they should bear their
- faces? Is there not milking-time, when you are
- going to bed, or kiln-hole, to whistle off these
- secrets, but you must be tittle-tattling before all
- our guests? 'tis well they are whispering: clamour
- your tongues, and not a word more.
- MOPSA
- I have done. Come, you promised me a tawdry-lace
- and a pair of sweet gloves.
- Clown
- Have I not told thee how I was cozened by the way
- and lost all my money?
- AUTOLYCUS
- And indeed, sir, there are cozeners abroad;
- therefore it behoves men to be wary.
- Clown
- Fear not thou, man, thou shalt lose nothing here.
- AUTOLYCUS
- I hope so, sir; for I have about me many parcels of charge.
- Clown
- What hast here? ballads?
- MOPSA
- Pray now, buy some: I love a ballad in print o'
- life, for then we are sure they are true.
- AUTOLYCUS
- Here's one to a very doleful tune, how a usurer's
- wife was brought to bed of twenty money-bags at a
- burthen and how she longed to eat adders' heads and
- toads carbonadoed.
- MOPSA
- Is it true, think you?
- AUTOLYCUS
- Very true, and but a month old.
- DORCAS
- Bless me from marrying a usurer!
- AUTOLYCUS
- Here's the midwife's name to't, one Mistress
- Tale-porter, and five or six honest wives that were
- present. Why should I carry lies abroad?
- MOPSA
- Pray you now, buy it.
- Clown
- Come on, lay it by: and let's first see moe
- ballads; we'll buy the other things anon.
- AUTOLYCUS
- Here's another ballad of a fish, that appeared upon
- the coast on Wednesday the four-score of April,
- forty thousand fathom above water, and sung this
- ballad against the hard hearts of maids: it was
- thought she was a woman and was turned into a cold
- fish for she would not exchange flesh with one that
- loved her: the ballad is very pitiful and as true.
- DORCAS
- Is it true too, think you?
- AUTOLYCUS
- Five justices' hands at it, and witnesses more than
- my pack will hold.
- Clown
- Lay it by too: another.
- AUTOLYCUS
- This is a merry ballad, but a very pretty one.
- MOPSA
- Let's have some merry ones.
- AUTOLYCUS
- Why, this is a passing merry one and goes to
- the tune of 'Two maids wooing a man:' there's
- scarce a maid westward but she sings it; 'tis in
- request, I can tell you.
- MOPSA
- We can both sing it: if thou'lt bear a part, thou
- shalt hear; 'tis in three parts.
- DORCAS
- We had the tune on't a month ago.
- AUTOLYCUS
- I can bear my part; you must know 'tis my
- occupation; have at it with you.
SONG
- AUTOLYCUS
- Get you hence, for I must go
- Where it fits not you to know.
- DORCAS
- Whither?
- MOPSA
- O, whither?
- DORCAS
- Whither?
- MOPSA
- It becomes thy oath full well,
- Thou to me thy secrets tell.
- DORCAS
- Me too, let me go thither.
- MOPSA
- Or thou goest to the orange or mill.
- DORCAS
- If to either, thou dost ill.
- AUTOLYCUS
- Neither.
- DORCAS
- What, neither?
- AUTOLYCUS
- Neither.
- DORCAS
- Thou hast sworn my love to be.
- MOPSA
- Thou hast sworn it more to me:
- Then whither goest? say, whither?
- Clown
- We'll have this song out anon by ourselves: my
- father and the gentlemen are in sad talk, and we'll
- not trouble them. Come, bring away thy pack after
- me. Wenches, I'll buy for you both. Pedlar, let's
- have the first choice. Follow me, girls.
Exit with DORCAS and MOPSA
- AUTOLYCUS
- And you shall pay well for 'em.
- Follows singing
- Will you buy any tape,
- Or lace for your cape,
- My dainty duck, my dear-a?
- Any silk, any thread,
- Any toys for your head,
- Of the new'st and finest, finest wear-a?
- Come to the pedlar;
- Money's a medler.
- That doth utter all men's ware-a.
Exit
Re-enter Servant
- Servant
- Master, there is three carters, three shepherds,
- three neat-herds, three swine-herds, that have made
- themselves all men of hair, they call themselves
- Saltiers, and they have a dance which the wenches
- say is a gallimaufry of gambols, because they are
- not in't; but they themselves are o' the mind, if it
- be not too rough for some that know little but
- bowling, it will please plentifully.
- Shepherd
- Away! we'll none on 't: here has been too much
- homely foolery already. I know, sir, we weary you.
- POLIXENES
- You weary those that refresh us: pray, let's see
- these four threes of herdsmen.
- Servant
- One three of them, by their own report, sir, hath
- danced before the king; and not the worst of the
- three but jumps twelve foot and a half by the squier.
- Shepherd
- Leave your prating: since these good men are
- pleased, let them come in; but quickly now.
- Servant
- Why, they stay at door, sir.
Exit
Here a dance of twelve Satyrs
- POLIXENES
- O, father, you'll know more of that hereafter.
- To CAMILLO
- Is it not too far gone? 'Tis time to part them.
- He's simple and tells much.
- To FLORIZEL
- How now, fair shepherd!
- Your heart is full of something that does take
- Your mind from feasting. Sooth, when I was young
- And handed love as you do, I was wont
- To load my she with knacks: I would have ransack'd
- The pedlar's silken treasury and have pour'd it
- To her acceptance; you have let him go
- And nothing marted with him. If your lass
- Interpretation should abuse and call this
- Your lack of love or bounty, you were straited
- For a reply, at least if you make a care
- Of happy holding her.
- FLORIZEL
- Old sir, I know
- She prizes not such trifles as these are:
- The gifts she looks from me are pack'd and lock'd
- Up in my heart; which I have given already,
- But not deliver'd. O, hear me breathe my life
- Before this ancient sir, who, it should seem,
- Hath sometime loved! I take thy hand, this hand,
- As soft as dove's down and as white as it,
- Or Ethiopian's tooth, or the fann'd
- snow that's bolted
- By the northern blasts twice o'er.
- POLIXENES
- What follows this?
- How prettily the young swain seems to wash
- The hand was fair before! I have put you out:
- But to your protestation; let me hear
- What you profess.
- FLORIZEL
- Do, and be witness to 't.
- POLIXENES
- And this my neighbour too?
- FLORIZEL
- And he, and more
- Than he, and men, the earth, the heavens, and all:
- That, were I crown'd the most imperial monarch,
- Thereof most worthy, were I the fairest youth
- That ever made eye swerve, had force and knowledge
- More than was ever man's, I would not prize them
- Without her love; for her employ them all;
- Commend them and condemn them to her service
- Or to their own perdition.
- POLIXENES
- Fairly offer'd.
- CAMILLO
- This shows a sound affection.
- Shepherd
- But, my daughter,
- Say you the like to him?
- PERDITA
- I cannot speak
- So well, nothing so well; no, nor mean better:
- By the pattern of mine own thoughts I cut out
- The purity of his.
- Shepherd
- Take hands, a bargain!
- And, friends unknown, you shall bear witness to 't:
- I give my daughter to him, and will make
- Her portion equal his.
- FLORIZEL
- O, that must be
- I' the virtue of your daughter: one being dead,
- I shall have more than you can dream of yet;
- Enough then for your wonder. But, come on,
- Contract us 'fore these witnesses.
- Shepherd
- Come, your hand;
- And, daughter, yours.
- POLIXENES
- Soft, swain, awhile, beseech you;
- Have you a father?
- FLORIZEL
- I have: but what of him?
- POLIXENES
- Knows he of this?
- FLORIZEL
- He neither does nor shall.
- POLIXENES
- Methinks a father
- Is at the nuptial of his son a guest
- That best becomes the table.
- Know man from man? dispute his own estate?
- Lies he not bed-rid? and again does nothing
- But what he did being childish?
- FLORIZEL
- No, good sir;
- He has his health and ampler strength indeed
- Than most have of his age.
- POLIXENES
- By my white beard,
- You offer him, if this be so, a wrong
- Something unfilial: reason my son
- Should choose himself a wife, but as good reason
- The father, all whose joy is nothing else
- But fair posterity, should hold some counsel
- In such a business.
- FLORIZEL
- I yield all this;
- But for some other reasons, my grave sir,
- Which 'tis not fit you know, I not acquaint
- My father of this business.
- POLIXENES
- Let him know't.
- FLORIZEL
- He shall not.
- POLIXENES
- Prithee, let him.
- FLORIZEL
- No, he must not.
- Shepherd
- Let him, my son: he shall not need to grieve
- At knowing of thy choice.
- FLORIZEL
- Come, come, he must not.
- Mark our contract.
- POLIXENES
- Mark your divorce, young sir,
- Discovering himself
- Whom son I dare not call; thou art too base
- To be acknowledged: thou a sceptre's heir,
- That thus affect'st a sheep-hook! Thou old traitor,
- I am sorry that by hanging thee I can
- But shorten thy life one week. And thou, fresh piece
- Of excellent witchcraft, who of force must know
- The royal fool thou copest with —
- Shepherd
- O, my heart!
- POLIXENES
- I'll have thy beauty scratch'd with briers, and made
- More homely than thy state. For thee, fond boy,
- If I may ever know thou dost but sigh
- That thou no more shalt see this knack, as never
- I mean thou shalt, we'll bar thee from succession;
- Not hold thee of our blood, no, not our kin,
- Far than Deucalion off: mark thou my words:
- Follow us to the court. Thou churl, for this time,
- Though full of our displeasure, yet we free thee
- From the dead blow of it. And you, enchantment. —
- Worthy enough a herdsman: yea, him too,
- That makes himself, but for our honour therein,
- Unworthy thee — if ever henceforth thou
- These rural latches to his entrance open,
- Or hoop his body more with thy embraces,
- I will devise a death as cruel for thee
- As thou art tender to't.
Exit
- PERDITA
- Even here undone!
- I was not much afeard; for once or twice
- I was about to speak and tell him plainly,
- The selfsame sun that shines upon his court
- Hides not his visage from our cottage but
- Looks on alike. Will't please you, sir, be gone?
- I told you what would come of this: beseech you,
- Of your own state take care: this dream of mine —
- Being now awake, I'll queen it no inch farther,
- But milk my ewes and weep.
- CAMILLO
- Why, how now, father!
- Speak ere thou diest.
- Shepherd
- I cannot speak, nor think
- Nor dare to know that which I know. O sir!
- You have undone a man of fourscore three,
- That thought to fill his grave in quiet, yea,
- To die upon the bed my father died,
- To lie close by his honest bones: but now
- Some hangman must put on my shroud and lay me
- Where no priest shovels in dust. O cursed wretch,
- That knew'st this was the prince,
- and wouldst adventure
- To mingle faith with him! Undone! undone!

Exit
- FLORIZEL
- Why look you so upon me?
- I am but sorry, not afeard; delay'd,
- But nothing alter'd: what I was, I am;
- More straining on for plucking back, not following
- My leash unwillingly.
- CAMILLO
- Gracious my lord,
- You know your father's temper: at this time
- He will allow no speech, which I do guess
- You do not purpose to him; and as hardly
- Will he endure your sight as yet, I fear:
- Then, till the fury of his highness settle,
- Come not before him.
- FLORIZEL
- I not purpose it.
- I think, Camillo?
- CAMILLO
- Even he, my lord.
- PERDITA
- How often have I told you 'twould be thus!
- How often said, my dignity would last
- But till 'twere known!
- FLORIZEL
- It cannot fail but by
- The violation of my faith; and then
- Let nature crush the sides o' the earth together
- And mar the seeds within! Lift up thy looks:
- From my succession wipe me, father; I
- Am heir to my affection.
- CAMILLO
- Be advised.
- FLORIZEL
- I am, and by my fancy: if my reason
- Will thereto be obedient, I have reason;
- If not, my senses, better pleased with madness,
- Do bid it welcome.
- CAMILLO
- This is desperate, sir.
- FLORIZEL
- So call it: but it does fulfil my vow;
- I needs must think it honesty. Camillo,
- Not for Bohemia, nor the pomp that may
- Be thereat glean'd, for all the sun sees or
- The close earth wombs or the profound sea hides
- In unknown fathoms, will I break my oath
- To this my fair beloved: therefore, I pray you,
- As you have ever been my father's honour'd friend,
- When he shall miss me — as, in faith, I mean not
- To see him any more — cast your good counsels
- Upon his passion; let myself and fortune
- Tug for the time to come. This you may know
- And so deliver, I am put to sea
- With her whom here I cannot hold on shore;
- And most opportune to our need I have
- A vessel rides fast by, but not prepared
- For this design. What course I mean to hold
- Shall nothing benefit your knowledge, nor
- Concern me the reporting.
- CAMILLO
- O my lord!
- I would your spirit were easier for advice,
- Or stronger for your need.
- FLORIZEL
- Hark, Perdita
- Drawing her aside
- I'll hear you by and by.
- CAMILLO
- He's irremoveable,
- Resolved for flight. Now were I happy, if
- His going I could frame to serve my turn,
- Save him from danger, do him love and honour,
- Purchase the sight again of dear Sicilia
- And that unhappy king, my master, whom
- I so much thirst to see.
- FLORIZEL
- Now, good Camillo;
- I am so fraught with curious business that
- I leave out ceremony.
- CAMILLO
- Sir, I think
- You have heard of my poor services, i' the love
- That I have borne your father?
- FLORIZEL
- Very nobly
- Have you deserved: it is my father's music
- To speak your deeds, not little of his care
- To have them recompensed as thought on.
- CAMILLO
- Well, my lord,
- If you may please to think I love the king
- And through him what is nearest to him, which is
- Your gracious self, embrace but my direction:
- If your more ponderous and settled project
- May suffer alteration, on mine honour,
- I'll point you where you shall have such receiving
- As shall become your highness; where you may
- Enjoy your mistress, from the whom, I see,
- There's no disjunction to be made, but by —
- As heavens forefend! — your ruin; marry her,
- And, with my best endeavours in your absence,
- Your discontenting father strive to qualify
- And bring him up to liking.
- FLORIZEL
- How, Camillo,
- May this, almost a miracle, be done?
- That I may call thee something more than man
- And after that trust to thee.
- CAMILLO
- Have you thought on
- A place whereto you'll go?
- FLORIZEL
- Not any yet:
- But as the unthought-on accident is guilty
- To what we wildly do, so we profess
- Ourselves to be the slaves of chance and flies
- Of every wind that blows.
- CAMILLO
- Then list to me:
- This follows, if you will not change your purpose
- But undergo this flight, make for Sicilia,
- And there present yourself and your fair princess,
- For so I see she must be, 'fore Leontes:
- She shall be habited as it becomes
- The partner of your bed. Methinks I see
- Leontes opening his free arms and weeping
- His welcomes forth; asks thee the son forgiveness,
- As 'twere i' the father's person; kisses the hands
- Of your fresh princess; o'er and o'er divides him
- 'Twixt his unkindness and his kindness; the one
- He chides to hell and bids the other grow
- Faster than thought or time.
- FLORIZEL
- Worthy Camillo,
- What colour for my visitation shall I
- Hold up before him?
- CAMILLO
- Sent by the king your father
- To greet him and to give him comforts. Sir,
- The manner of your bearing towards him, with
- What you as from your father shall deliver,
- Things known betwixt us three, I'll write you down:
- The which shall point you forth at every sitting
- What you must say; that he shall not perceive
- But that you have your father's bosom there
- And speak his very heart.
- FLORIZEL
- I am bound to you:
- There is some sap in this.
- CAMILLO
- A cause more promising
- Than a wild dedication of yourselves
- To unpath'd waters, undream'd shores, most certain
- To miseries enough; no hope to help you,
- But as you shake off one to take another;
- Nothing so certain as your anchors, who
- Do their best office, if they can but stay you
- Where you'll be loath to be: besides you know
- Prosperity's the very bond of love,
- Whose fresh complexion and whose heart together
- Affliction alters.
- PERDITA
- One of these is true:
- I think affliction may subdue the cheek,
- But not take in the mind.
- CAMILLO
- Yea, say you so?
- There shall not at your father's house these
- seven years
- Be born another such.
- FLORIZEL
- My good Camillo,
- She is as forward of her breeding as
- She is i' the rear our birth.
- CAMILLO
- I cannot say 'tis pity
- She lacks instructions, for she seems a mistress
- To most that teach.
- PERDITA
- Your pardon, sir; for this
- I'll blush you thanks.
- FLORIZEL
- My prettiest Perdita!
- But O, the thorns we stand upon! Camillo,
- Preserver of my father, now of me,
- The medicine of our house, how shall we do?
- We are not furnish'd like Bohemia's son,
- Nor shall appear in Sicilia.
- CAMILLO
- My lord,
- Fear none of this: I think you know my fortunes
- Do all lie there: it shall be so my care
- To have you royally appointed as if
- The scene you play were mine. For instance, sir,
- That you may know you shall not want, one word.
They talk aside
Re-enter AUTOLYCUS
- AUTOLYCUS
- Ha, ha! what a fool Honesty is! and Trust, his
- sworn brother, a very simple gentleman! I have sold
- all my trumpery; not a counterfeit stone, not a
- ribbon, glass, pomander, brooch, table-book, ballad,
- knife, tape, glove, shoe-tie, bracelet, horn-ring,
- to keep my pack from fasting: they throng who
- should buy first, as if my trinkets had been
- hallowed and brought a benediction to the buyer:
- by which means I saw whose purse was best in
- picture; and what I saw, to my good use I
- remembered. My clown, who wants but something to
- be a reasonable man, grew so in love with the
- wenches' song, that he would not stir his pettitoes
- till he had both tune and words; which so drew the
- rest of the herd to me that all their other senses
- stuck in ears: you might have pinched a placket, it
- was senseless; 'twas nothing to geld a codpiece of a
- purse; I could have filed keys off that hung in
- chains: no hearing, no feeling, but my sir's song,
- and admiring the nothing of it. So that in this
- time of lethargy I picked and cut most of their
- festival purses; and had not the old man come in
- with a whoo-bub against his daughter and the king's
- son and scared my choughs from the chaff, I had not
- left a purse alive in the whole army.
CAMILLO, FLORIZEL, and PERDITA come forward
- CAMILLO
- Nay, but my letters, by this means being there
- So soon as you arrive, shall clear that doubt.
- FLORIZEL
- And those that you'll procure from King Leontes —
- CAMILLO
- Shall satisfy your father.
- PERDITA
- Happy be you!
- All that you speak shows fair.
- CAMILLO
- Who have we here?
- Seeing AUTOLYCUS
- We'll make an instrument of this, omit
- Nothing may give us aid.
- AUTOLYCUS
- If they have overheard me now, why, hanging.
- CAMILLO
- How now, good fellow! why shakest thou so? Fear
- not, man; here's no harm intended to thee.
- AUTOLYCUS
- I am a poor fellow, sir.
- CAMILLO
- Why, be so still; here's nobody will steal that from
- thee: yet for the outside of thy poverty we must
- make an exchange; therefore discase thee instantly,
- — thou must think there's a necessity in't — and
- change garments with this gentleman: though the
- pennyworth on his side be the worst, yet hold thee,
- there's some boot.
- AUTOLYCUS
- I am a poor fellow, sir.
- Aside
- I know ye well enough.
- CAMILLO
- Nay, prithee, dispatch: the gentleman is half
- flayed already.
- AUTOLYCUS
- Are you in earnest, sir?
- Aside
- I smell the trick on't.
- FLORIZEL
- Dispatch, I prithee.
- AUTOLYCUS
- Indeed, I have had earnest: but I cannot with
- conscience take it.
- CAMILLO
- Unbuckle, unbuckle.
- FLORIZEL and AUTOLYCUS exchange garments
- Fortunate mistress — let my prophecy
- Come home to ye! — you must retire yourself
- Into some covert: take your sweetheart's hat
- And pluck it o'er your brows, muffle your face,
- Dismantle you, and, as you can, disliken
- The truth of your own seeming; that you may —
- For I do fear eyes over — to shipboard
- Get undescried.
- PERDITA
- I see the play so lies
- That I must bear a part.
- CAMILLO
- No remedy.
- Have you done there?
- FLORIZEL
- Should I now meet my father,
- He would not call me son.
- CAMILLO
- Nay, you shall have no hat.
- Giving it to PERDITA
- Come, lady, come. Farewell, my friend.
- AUTOLYCUS
- Adieu, sir.
- FLORIZEL
- O Perdita, what have we twain forgot!
- Pray you, a word.
- CAMILLO
- Aside What I do next, shall be to tell the king
- Of this escape and whither they are bound;
- Wherein my hope is I shall so prevail
- To force him after: in whose company
- I shall review Sicilia, for whose sight
- I have a woman's longing.
- FLORIZEL
- Fortune speed us!
- Thus we set on, Camillo, to the sea-side.
- CAMILLO
- The swifter speed the better.
Exeunt FLORIZEL, PERDITA, and CAMILLO
- AUTOLYCUS
- I understand the business, I hear it: to have an
- open ear, a quick eye, and a nimble hand, is
- necessary for a cut-purse; a good nose is requisite
- also, to smell out work for the other senses. I see
- this is the time that the unjust man doth thrive.
- What an exchange had this been without boot! What
- a boot is here with this exchange! Sure the gods do
- this year connive at us, and we may do any thing
- extempore. The prince himself is about a piece of
- iniquity, stealing away from his father with his
- clog at his heels: if I thought it were a piece of
- honesty to acquaint the king withal, I would not
- do't: I hold it the more knavery to conceal it;
- and therein am I constant to my profession.
- Re-enter Clown and Shepherd
- Aside, aside; here is more matter for a hot brain:
- every lane's end, every shop, church, session,
- hanging, yields a careful man work.
- Clown
- See, see; what a man you are now!
- There is no other way but to tell the king
- she's a changeling and none of your flesh and blood.
- Shepherd
- Nay, but hear me.
- Clown
- Nay, but hear me.
- Shepherd
- Go to, then.
- Clown
- She being none of your flesh and blood, your flesh
- and blood has not offended the king; and so your
- flesh and blood is not to be punished by him. Show
- those things you found about her, those secret
- things, all but what she has with her: this being
- done, let the law go whistle: I warrant you.
- Shepherd
- I will tell the king all, every word, yea, and his
- son's pranks too; who, I may say, is no honest man,
- neither to his father nor to me, to go about to make
- me the king's brother-in-law.

- AUTOLYCUS
- Aside Very wisely, puppies!
- Shepherd
- Well, let us to the king: there is that in this
- fardel will make him scratch his beard.
- AUTOLYCUS
- Aside I know not what impediment this complaint
- may be to the flight of my master.
- Clown
- Pray heartily he be at palace.
- AUTOLYCUS
- Aside Though I am not naturally honest, I am so
- sometimes by chance: let me pocket up my pedlar's excrement.

- Shepherd
- To the palace, an it like your worship.
- AUTOLYCUS
- Your affairs there, what, with whom, the condition
- of that fardel, the place of your dwelling, your
- names, your ages, of what having, breeding, and any
- thing that is fitting to be known, discover.
- Clown
- We are but plain fellows, sir.
- AUTOLYCUS
- A lie; you are rough and hairy. Let me have no
- lying: it becomes none but tradesmen, and they
- often give us soldiers the lie: but we pay them for
- it with stamped coin, not stabbing steel; therefore
- they do not give us the lie.
- Clown
- Your worship had like to have given us one, if you
- had not taken yourself with the manner.

- AUTOLYCUS
- Whether it like me or no, I am a courtier. Seest
- thou not the air of the court in these enfoldings?
- hath not my gait in it the measure of the court?
- receives not thy nose court-odor from me? reflect I
- not on thy baseness court-contempt? Thinkest thou,
- for that I insinuate, or toaze from thee thy
- business, I am therefore no courtier? I am courtier
- cap-a-pe; and one that will either push on or pluck
- back thy business there: whereupon I command thee to
- open thy affair.
- Shepherd
- My business, sir, is to the king.
- AUTOLYCUS
- What advocate hast thou to him?
- Shepherd
- I know not, an't like you.
- Clown
- Advocate's the court-word for a pheasant: say you
- have none.

- AUTOLYCUS
- How blessed are we that are not simple men!
- Yet nature might have made me as these are,
- Therefore I will not disdain.
- Clown
- This cannot be but a great courtier.
- Shepherd
- His garments are rich, but he wears
- them not handsomely.
- Clown
- He seems to be the more noble in being fantastical:
- a great man, I'll warrant; I know by the picking
- on's teeth.
- AUTOLYCUS
- The fardel there? what's i' the fardel?
- Wherefore that box?
- Shepherd
- Sir, there lies such secrets in this fardel and box,
- which none must know but the king; and which he
- shall know within this hour, if I may come to the
- speech of him.
- AUTOLYCUS
- Age, thou hast lost thy labour.
- Shepherd
- Why, sir?
- AUTOLYCUS
- The king is not at the palace; he is gone aboard a
- new ship to purge melancholy and air himself: for,
- if thou beest capable of things serious, thou must
- know the king is full of grief.
- Shepard
- So 'tis said, sir; about his son, that should have
- married a shepherd's daughter.
- AUTOLYCUS
- If that shepherd be not in hand-fast, let him fly:
- the curses he shall have, the tortures he shall
- feel, will break the back of man, the heart of monster.
- Clown
- Think you so, sir?
- AUTOLYCUS
- Not he alone shall suffer what wit can make heavy
- and vengeance bitter; but those that are germane to
- him, though removed fifty times, shall all come
- under the hangman: which though it be great pity,
- yet it is necessary. An old sheep-whistling rogue a
- ram-tender, to offer to have his daughter come into
- grace! Some say he shall be stoned; but that death
- is too soft for him, say I draw our throne into a
- sheep-cote! all deaths are too few, the sharpest too easy.
- Clown
- Has the old man e'er a son, sir, do you hear. an't
- like you, sir?
- AUTOLYCUS
- He has a son, who shall be flayed alive; then
- 'nointed over with honey, set on the head of a
- wasp's nest; then stand till he be three quarters
- and a dram dead; then recovered again with
- aqua-vitae or some other hot infusion; then, raw as
- he is, and in the hottest day prognostication
- proclaims, shall be be set against a brick-wall, the
- sun looking with a southward eye upon him, where he
- is to behold him with flies blown to death. But what
- talk we of these traitorly rascals, whose miseries
- are to be smiled at, their offences being so
- capital? Tell me, for you seem to be honest plain
- men, what you have to the king: being something
- gently considered, I'll bring you where he is
- aboard, tender your persons to his presence,
- whisper him in your behalfs; and if it be in man
- besides the king to effect your suits, here is man
- shall do it.
- Clown
- He seems to be of great authority: close with him,
- give him gold; and though authority be a stubborn
- bear, yet he is oft led by the nose with gold: show
- the inside of your purse to the outside of his hand,
- and no more ado. Remember 'stoned,' and 'flayed alive.'
- Shepherd
- An't please you, sir, to undertake the business for
- us, here is that gold I have: I'll make it as much
- more and leave this young man in pawn till I bring it you.
- AUTOLYCUS
- After I have done what I promised?
- Shepherd
- Ay, sir.
- AUTOLYCUS
- Well, give me the moiety. Are you a party in this business?
- Clown
- In some sort, sir: but though my case be a pitiful
- one, I hope I shall not be flayed out of it.
- AUTOLYCUS
- O, that's the case of the shepherd's son: hang him,
- he'll be made an example.
- Clown
- Comfort, good comfort! We must to the king and show
- our strange sights: he must know 'tis none of your
- daughter nor my sister; we are gone else. Sir, I
- will give you as much as this old man does when the
- business is performed, and remain, as he says, your
- pawn till it be brought you.
- AUTOLYCUS
- I will trust you. Walk before toward the sea-side;
- go on the right hand: I will but look upon the
- hedge and follow you.
- Clown
- We are blest in this man, as I may say, even blest.
- Shepherd
- Let's before as he bids us: he was provided to do us good.
Exeunt Shepherd and Clown
- AUTOLYCUS
- If I had a mind to be honest, I see Fortune would
- not suffer me: she drops booties in my mouth. I am
- courted now with a double occasion, gold and a means
- to do the prince my master good; which who knows how
- that may turn back to my advancement? I will bring
- these two moles, these blind ones, aboard him: if he
- think it fit to shore them again and that the
- complaint they have to the king concerns him
- nothing, let him call me rogue for being so far
- officious; for I am proof against that title and
- what shame else belongs to't. To him will I present
- them: there may be matter in it.
Exit


| vcovPL {sandwich} | R Documentation |
Estimation of sandwich covariances a la Newey-West (1987) and Driscoll and Kraay (1998) for panel data.
vcovPL(x, cluster = NULL, order.by = NULL, kernel = "Bartlett", sandwich = TRUE, fix = FALSE, ...) meatPL(x, cluster = NULL, order.by = NULL, kernel = "Bartlett", lag = "NW1987", bw = NULL, adjust = TRUE, ...)
x |
a fitted model object. |
cluster |
a variable indicating the clustering of observations,
a |
order.by |
a variable, list/data.frame, or formula indicating the
aggregation within time periods. By default |
kernel |
a character specifying the kernel used. All kernels
described in Andrews (1991) are supported, see |
lag |
character or numeric, indicating the lag length used.
Three rules of thumb ( |
bw |
numeric. The bandwidth of the kernel which by default corresponds
to |
sandwich |
logical. Should the sandwich estimator be computed?
If set to |
fix |
logical. Should the covariance matrix be fixed to be positive semi-definite in case it is not? |
adjust |
logical. Should a finite sample adjustment be made? This amounts to multiplication with n/(n - k) where n is the number of observations and k is the number of estimated parameters. |
... |
arguments passed to the |
vcovPL
is a function for estimating the Newey-West (1987) and
Driscoll and Kraay (1998) covariance matrix.
Driscoll and Kraay (1998) apply a Newey-West type correction to the
sequence of cross-sectional averages of the moment conditions (see
Hoechle (2007)). For large T (and regardless of the length of the
cross-sectional dimension), the Driscoll and Kraay (1998)
standard errors are robust to general forms of cross-sectional and
serial correlation (Hoechle (2007)).
The Newey-West (1978) covariance matrix restricts the Driscoll and
Kraay (1998) covariance matrix to no cross-sectional correlation.
The function meatPL
is the work horse for estimating
the meat of Newey-West (1978) and Driscoll and Kraay (1998)
covariance matrix estimators. vcovPL
is a wrapper calling
sandwich
and bread
(Zeileis 2006).
Default lag length is the "NW1987"
.
For lag = "NW1987"
, the lag length is chosen from the heuristic
floor[T^{(1/4)}]. More details on lag length selection in Hoechle (2007).
For lag = "NW1994"
, the lag length is taken from the first step
of Newey and West's (1994) plug-in procedure.
The cluster
/order.by
specification can be made in a number of ways:
Either both can be a single variable or cluster
can be a
list
/data.frame
of two variables.
If expand.model.frame
works for the model object x
,
the cluster
(and potentially additionally order.by
) can also be
a formula
. By default (cluster = NULL, order.by = NULL
),
attr(x, "cluster")
and attr(x, "order.by")
are checked and
used if available. If not, every observation is assumed to be its own cluster,
and observations within clusters are assumed to be ordered accordingly.
If the number of observations in the model x
is smaller than in the
original data
due to NA
processing, then the same NA
processing
can be applied to cluster
if necessary (and x$na.action
being
available).
A matrix containing the covariance matrix estimate.
Andrews DWK (1991). “Heteroscedasticity and Autocorrelation Consistent Covariance Matrix Estimation”, Econometrica, 817–858.
Driscoll JC & Kraay AC (1998). “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data”, The Review of Economics and Statistics, 80(4), 549–560.
Hoechle D (2007). “Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence”, Stata Journal, 7(3), 281–312.
Newey WK & West KD (1987). “Hypothesis Testing with Efficient Method of Moments Estimation”, International Economic Review, 777-787.
Newey WK & West KD (1994). “Automatic Lag Selection in Covariance Matrix Estimation”, The Review of Economic Studies, 61(4), 631–653.
White H (1980). “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity”, Econometrica, 817–838. doi: 10.2307/1912934
Zeileis A (2004). “Econometric Computing with HC and HAC Covariance Matrix Estimator”, Journal of Statistical Software, 11(10), 1–17. doi: 10.18637/jss.v011.i10
Zeileis A (2006). “Object-Oriented Computation of Sandwich Estimators”, Journal of Statistical Software, 16(9), 1–16. doi: 10.18637/jss.v016.i09
Zeileis A, Köll S, Graham N (2020). “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.” Journal of Statistical Software, 95(1), 1–36. doi: 10.18637/jss.v095.i01
## Petersen's data data("PetersenCL", package = "sandwich") m <- lm(y ~ x, data = PetersenCL) ## Driscoll and Kraay standard errors ## lag length set to: T - 1 (maximum lag length) ## as proposed by Petersen (2009) sqrt(diag(vcovPL(m, cluster = ~ firm + year, lag = "max", adjust = FALSE))) ## lag length set to: floor(4 * (T / 100)^(2/9)) ## rule of thumb proposed by Hoechle (2007) based on Newey & West (1994) sqrt(diag(vcovPL(m, cluster = ~ firm + year, lag = "NW1994"))) ## lag length set to: floor(T^(1/4)) ## rule of thumb based on Newey & West (1987) sqrt(diag(vcovPL(m, cluster = ~ firm + year, lag = "NW1987"))) ## the following specifications of cluster/order.by are equivalent vcovPL(m, cluster = ~ firm + year) vcovPL(m, cluster = PetersenCL[, c("firm", "year")]) vcovPL(m, cluster = ~ firm, order.by = ~ year) vcovPL(m, cluster = PetersenCL$firm, order.by = PetersenCL$year) ## these are also the same when observations within each ## cluster are already ordered vcovPL(m, cluster = ~ firm) vcovPL(m, cluster = PetersenCL$firm)


--- layout: page title: About header-img: "img/about-bg.png" ---
Here I'll write up articles relating to web development.


{{ partial "header" . }} {{ if eq .Title "Updates | Eviction Lab"}}
In March 2020, the Eviction Lab launched a new partnership with the Economic Hardship Reporting Project to commission storytelling on U.S. housing insecurity, eviction, and homelessness. If you're a writer or artist interested in pitching your story, please contact<EMAIL_ADDRESS>For tips on reporting about eviction, please see our Media Guide.


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Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 6-8
Dietary
This is an elegant, moist apple cake to be served in slim slices with a very fine cup of tea. It freezes brilliantly, too.
Preheat the oven to 180C/375F/Gas 4. Lightly grease and line the base of a 25cm/10in springform tin with a little oil.
Place the apples into a pan with half the lemon juice and two tablespoons of the caster sugar. Bring to a simmer, cover and cook on a low heat for about 7-9 minutes or until the apples are soft enough to roughly mash with a fork. Do not over-mash, though.  Leave to cool.
Put the cooled purée, eggs, ground almonds, almond essence, remaining caster sugar and approximately one tablespoon of the remaining lemon juice into a food processor and blend together. Pour into the prepared cake tin and sprinkle with the flaked almonds.
Bake for about 40 minutes or until a skewer inserted into the cake comes out clean. Check the cake after about 30 minutes, and if the cake is getting too brown cover the top with parchment paper or foil.
Leave to cool slightly before you remove the cake from the tin. This is delicious served warm or cold with a generous sprinkling of icing sugar.


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{% extends "layout.html" %} {% block pageTitle %} NRS Diagnostic Prototype {% endblock %} {% block header %} {{ govukHeader() }} {% endblock %} {% block beforeContent %} Back {% endblock %} {% block content %}
Math skills
Diagnostic explanation
Diagnostic Q1
Diagnostic Q20
Diagnostic feedback
Functional skills course overview (with diagnostic)
Functional skills course overview (no diagnostic)


Postscript
Looking back...
- Functional programming
- "declarative" programming (recursion over persistent data
structures)
- higher-order functions
- polymorphism
- Logic, the mathematical basis for software engineering:
logic calculus -------------------- = ---------------------------- software engineering mechanical/civil engineering
- inductively defined sets and relations
- inductive proofs
- proof objects
- Coq, an industrial-strength proof assistant
- functional core language
- core tactics
- automation
- Foundations of programming languages
- notations and definitional techniques for precisely specifying
- abstract syntax
- operational semantics
- big-step style
- small-step style
- type systems
- program equivalence
- Hoare logic
- fundamental metatheory of type systems
- progress and preservation
- progress and preservation
- theory of subtyping
- notations and definitional techniques for precisely specifying
Looking forward...
- Several optional chapters of Software Foundations
- Cutting-edge conferences on programming languages and formal
verification:
- POPL
- PLDI
- OOPSLA
- ICFP
- CAV
- (and many others)
- More on functional programming
- Learn You a Haskell for Great Good, by Miran Lipovaca (ebook)
- and many other texts on Haskell, OCaml, Scheme, Scala, ...
- More on Hoare logic and program verification
- The Formal Semantics of Programming Languages: An Introduction, by Glynn Winskel. MIT Press, 1993.
- Many practical verification tools, e.g. Microsoft's
Boogie system, Java Extended Static Checking, etc.
- More on the foundations of programming languages:
- Types and Programming Languages, by Benjamin C. Pierce. MIT Press, 2002.
- Practical Foundations for Programming Languages, by Robert Harper. Forthcoming from MIT Press. Manuscript available from his web page.
- Foundations for Programming Languages, by John C. Mitchell.
MIT Press, 1996.
- More on Coq:
- Certified Programming with Dependent Types, by Adam Chlipala. A draft textbook on practical proof engineering with Coq, available from his web page.
- Interactive Theorem Proving and Program Development: Coq'Art: The Calculus of Inductive Constructions, by Yves Bertot and Pierre Casteran. Springer-Verlag, 2004.
- Iron Lambda (http://iron.ouroborus.net/) is a collection of Coq formalisations for functional languages of increasing complexity. It fills part of the gap between the end of the Software Foundations course and what appears in current research papers. The collection has at least Progress and Preservation theorems for a number of variants of STLC and the polymorphic lambda-calculus (System F)
(* $Date: 2014-08-23 15:24:59 -0400 (Sat, 23 Aug 2014) $ *)


CNN MNIST
Intro
This model is improved version of simple MNIST model (with just 1 layer).
For example, model will classify above image as 2.
Simple MNIST model's accuracy was about 92% but this model can increase up to 99.2%.
Then how this improved MNIST model can increase accuracy?
What is CNN?
This improved model use CNN to extract feature. When input data of ANN model is image, it's hard to select feature of image manually. To improve accuracy of ANN model when input data type is image, we can use ANN to extract feature of image automatically. CNN is basic technology to extract feature in image. In MNIST problem, input data is image of handwritten number, So we can improve accuracy by using CNN to extract feature
CNN is consist of convolution layer and pooling layer. Convolution layer extract feature of image by matrix multiplication. Pooling layer is used to reduce feature size or emphasize specific feature.
CNN can improve accuracy of tensorflow model which handle image data. The reason of improvement is, Fully connected layer only support 1D data and image is 2D data. We need to flatten 2D image to 1D data to use fully connected layer. In this step, spatial information of 2D image is lost. To avoid lost of spatial informations, we use CNN which extract spatial information as feature. So when we use spatial data as input, we can improve accuracy by using CNN
Architecture
Above picture depict architecture of CNN MNIST model. First layer is input layer which accept user input. Input layer accept user input as [?, 28 * 28] list. Input layer forward this value to CNN layer.
From 2nd layer to 5th layer is CNN layer. One CNN layer is consist of 1 convolution layer and 1 pulling layer. So we can say that there are 2 CNN layers. Second CNN layer forward extracted feature to ANN.
6th and 7th layer is ANN layer. This layer using feature which is extracted from CNN layer and classify number. 6th layer using ReLu as loss function and 7th layer using Softmax as loss function.
Last layer is called output layer, There are 10 neurons and the index of neuron which has the largest value will be the prediction of number from 0 to 9.


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I found it very hard to stay on task when watching Gordon Zhu's videos on Watch and Code. Although recommended it was very tough to stay awake at times. So this time instead of avoiding more lessons, and as I am now in the habit of doing so, I looked more into the coding of HTML pages that I have created. I am currently 60% of the way through, but after talking to a cohort peer I decided to abandon the lessons and instead opt for direct practical applications that is offered with freeCodeCamp.
[deleted section of whining].... WHY.
Otherwise I am enjoying learning the shortcuts of VSC and trying to get a simplier HTML code.
Some java code. That I have implemented to my Sprints Checklist page. What some of the names are in the JS convention. Keyboard shortcuts, I love these. How to open, start, and end a conversation on Zoom. That if I add a bit of CSS coding to the UL tag, parent of the li tag, that I can neatly line up the checkboxes aligned to the right of the left aligned list elements. And I steal code alot from other peoples html and css code. So I have learnt that I am still a magpie apparently.
Oh and I still throw my notes into a word document so I can visually edit the styles and then bung it into Doc to Html converter. I really dont want to have to muck around in my HTML trying to make decisions on something I cannot yet see.
I am never ever confident. My philosophy is that I am always a learner.
Ranting in my reflects. For this, I do apologise.
Watching 'Watch and Code'.
I enjoy doing practicals and being able to see why would I do a particular function. Which is why I sort of like looking at someone elses HTML and CSS code and picking and trying code for myself. I am getting better at typing. I get super sore shoulders if I type too much. Sometimes yes, I dont know how to ask the right question, however I am very greatful to many resources and forums on the internet (in particular I truely like stack-overflow, finding the answers through others code and being able to get an idea of what code can do is really great).
I have just finished the DOM tools crashcourse and found that the tools talked about were at once facinating, and hopeless as I have no idea how I would implement it. Even at double speed it was hard to keep watch.
I would fall asleep trying to watch 'Watch and Code'. So I instead worked on other projects to do with code. Also learning and revisiting my limitations with my facilitator helped to remind me how to overcome limitations. Some things are just not yet habit if I forget about them.
As if talking to a Non-technical friend, discuss:
I like to think of HTML like the basic bones of a project. For example if you want to you could think of html like it is a bicycle. You can make a bike be very simple - the frame, two wheels, a basic paint job, a system for brakes and for changing gears, and basic handlebars. Lets say this bike is your one for all terrains type bike. So decent wide tyres to grip everything. And thats it. This bike is very basic and will get you from A to B.
Then you can use CSS, which is the way of styling your bike/html. With CSS, you can modify everything to look very, very fancy. You can add lights for safety, reflectors, modify the frame and the tyres for lightness and speed or durability and extra grip.
I would explain what control flow and loops were but I may have died at the point that it came up.
The DOM, document/object/model, is a very fun tool you can use to mess with a website locally to your computer, or help you to modify and fix something with a webpage you are making. Recently I *struggled* through a DOM youtube crashcourse. The way to access it is through the webpage you are currently on, rightclicking and selecting inspect. Or however else you can access a inspector.
Using it you can see which tag in your HTML script is causing a problem, for example: a margin or box that is forcing itself too far to the left, or something that you really dont like on the page. Using the inspector you can find out which line is causing the issue, and then change the code to fix the problem. And it is cool because you can deactivate element attribues or, modify them to see what will happen to the webpage - Live!!!
I cant really explain the difference between arrays and objects because I really was dead when it was being explained... or not.
Functions may to be able to take many very simple steps that belong together and group them as a function. I am not sure, great question.


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*** User 2017-05-05 21:38:49.299 ***
21:38:49.299 [info] Disabling blocking sync...
*** User 2017-05-05 21:38:49.307 ***
21:38:49.306 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-1@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 21:38:50.980 ***
21:38:50.980 [info] Enabling membership...
*** User 2017-05-05 21:38:50.983 ***
21:38:50.983 [info] Disabling blocking sync...
*** User 2017-05-05 21:38:50.994 ***
21:38:50.993 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-2@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 21:38:53.296 ***
21:38:53.296 [info] Enabling membership...
*** User 2017-05-05 21:38:53.298 ***
21:38:53.298 [info] Disabling blocking sync...
*** User 2017-05-05 21:38:53.306 ***
21:38:53.305 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-3@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 21:38:53.765 ***
Joining node: 'node-3@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 49324
*** User 2017-05-05 21:38:53.765 ***
Joining issued: 'node-3@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 49324
*** User 2017-05-05 21:38:53.766 ***
Joining node: 'node-2@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 49302
*** User 2017-05-05 21:38:53.766 ***
Joining issued: 'node-2@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 49302
*** User 2017-05-05 21:38:53.767 ***
Joining node: 'node-1@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 49288
*** User 2017-05-05 21:38:53.768 ***
Joining issued: 'node-1@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 49288
*** User 2017-05-05 21:38:53.829 ***
Waiting for join to complete with manager: partisan_default_peer_service_manager
*** User 2017-05-05 21:38:53.831 ***
Finished.
*** CT 2017-05-05 21:38:53.831 *** COVER INFO
Adding nodes to cover test: ['runner@MacBook-Pro-de-Anne-France']
*** CT 2017-05-05 21:38:53.840 *** COVER INFO
Successfully added nodes to cover test: []
*** User 2017-05-05 21:38:53.840 ***
Case finished: uncontracted_latency_test
*** User 2017-05-05 21:39:00.890 ***
21:39:00.890 [info] Application lasp exited with reason: stopped
*** User 2017-05-05 21:39:00.891 ***
21:39:00.891 [info] Application partisan exited with reason: stopped
=== Ended at 2017-05-05 21:39:00
=== successfully completed test case
=== Returned value: ok
Test run history
| Top level test index


tensorbuilder.patches.tensorbuilder_patch.builder_custom_ops module
import tensorflow as tf from tensorflow.python.framework import tensor_shape builders_blacklist = [ "sampled_softmax_loss" ] def _polynomial(tensor): size = int(tensor.get_shape()[1]) pows = [ tf.pow(tensor[:, n], n + 1) for n in range(size) ] return tf.transpose(tf.pack(pows)) def polynomial_layer(builder, size, **kwargs): """ Creates a fully connected layer of size `size` and then applies the activation function ``` y(i) = z(i)^(i+1) ``` where `z = w*x + b` """ with tf.variable_scope("polynomial_layer"): return builder.fully_connected(size, activation_fn=_polynomial, **kwargs) def minimize(tensor, optimizer, *args, **kwargs): return optimizer.minimize(tensor, *args, **kwargs) def maximize(tensor, optimizer, *args, **kwargs): return optimizer.maximize(tensor, *args, **kwargs) def drop_layer(x, keep_prob, seed=None, name=None): """Computes dropout. With probability `keep_prob`, outputs the input element scaled up by `1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected sum is unchanged. Args: x: A tensor. keep_prob: A scalar `Tensor` with the same type as x. The probability that each element is kept. noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. seed: A Python integer. Used to create random seeds. See [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed) for behavior. name: A name for this operation (optional). Returns: A Tensor of the same shape of `x`. Raises: ValueError: If `keep_prob` is not in `(0, 1]`. """ with tf.op_scope([x], name, "drop_layer") as name: x = tf.convert_to_tensor(x, name="x") if isinstance(keep_prob, float) and not 0 < keep_prob <= 1: raise ValueError("keep_prob must be a scalar tensor or a float in the " "range (0, 1], got %g" % keep_prob) keep_prob = tf.convert_to_tensor(keep_prob, dtype=x.dtype, name="keep_prob") keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar()) noise_shape = [ tf.shape(x)[0], 1 ] # uniform [keep_prob, 1.0 + keep_prob) random_tensor = keep_prob random_tensor += tf.random_uniform( noise_shape, seed=seed, dtype=x.dtype ) # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = tf.floor(random_tensor) ret = x * tf.inv(keep_prob) * binary_tensor ret.set_shape(x.get_shape()) return ret def ensamble_dropout(tree, keep_prob, seed=None, name=None): with tf.op_scope(tree.tensors(), name, "drop_layer"): return tree.map_each(drop_layer, keep_prob, seed=seed, name=name) def add_regularization_loss(tensor, graph=None, scope='add_regularization_loss'): if not graph: reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) else: reg_losses = graph.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) with tf.variable_scope(scope): reg_loss = tf.reduce_sum([ tf.reduce_mean(reg_loss) for reg_loss in reg_losses ], name='reg_loss_mean_sum') return tf.add(tensor, reg_loss) from tensorflow.contrib.framework.python.ops import add_arg_scope from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import standard_ops from tensorflow.python.ops import variable_scope from tensorflow.python.training import moving_averages @add_arg_scope def fully_connected_from_product(inputs, activation_fn=None, normalizer_fn=None, normalizer_params=None, biases_initializer=init_ops.zeros_initializer, biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a fully connected layer. `fully_connected` creates a variable called `weights`, representing a fully connected weight matrix, which is multiplied by the `inputs` to produce a `Tensor` of hidden units. If a `normalizer_fn` is provided (such as `batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is None and a `biases_initializer` is provided then a `biases` variable would be created and added the hidden units. Finally, if `activation_fn` is not `None`, it is applied to the hidden units as well. Note: that if `inputs` have a rank greater than 2, then `inputs` is flattened prior to the initial matrix multiply by `weights`. Args: inputs: A tensor of with at least rank 2 and value for the last dimension, i.e. `[batch_size, depth]`, `[None, None, None, channels]`. num_outputs: Integer, the number of output units in the layer. activation_fn: activation function. normalizer_fn: normalization function to use instead of `biases`. If `normalize_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. normalizer_params: normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collections per variable. outputs_collections: collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for variable_op_scope. Returns: the tensor variable representing the result of the series of operations. Raises: ValueError: if x has rank less than 2 or if its last dimension is not set. """ with variable_scope.variable_op_scope([inputs], scope, 'fully_connected_from_product', reuse=reuse) as sc: outputs = inputs num_outputs = int(outputs.get_shape()[1]) dtype = outputs.dtype if normalizer_fn: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: if biases_initializer is not None: biases_collections = utils.get_variable_collections( variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_outputs,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, collections=biases_collections, trainable=trainable) outputs = nn.bias_add(outputs, biases) if activation_fn: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.name, outputs) @tf.contrib.framework.add_arg_scope def sampled_softmax_loss( inputs, num_outputs, labels, num_sampled, sampled_softmax_loss_args={}, activation_fn=None, normalizer_fn=None, normalizer_params=None, weights_initializer=tf.contrib.layers.initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=tf.zeros_initializer, biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a fully connected layer. `fully_connected` creates a variable called `weights`, representing a fully connected weight matrix, which is multiplied by the `inputs` to produce a `Tensor` of hidden units. If a `normalizer_fn` is provided (such as `batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is None and a `biases_initializer` is provided then a `biases` variable would be created and added the hidden units. Finally, if `activation_fn` is not `None`, it is applied to the hidden units as well. Note: that if `inputs` have a rank greater than 2, then `inputs` is flattened prior to the initial matrix multiply by `weights`. Args: inputs: A tensor of with at least rank 2 and value for the last dimension, i.e. `[batch_size, depth]`, `[None, None, None, channels]`. num_outputs: Integer, the number of output units in the layer. activation_fn: activation function. normalizer_fn: normalization function to use instead of `biases`. If `normalize_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. normalizer_params: normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collections per variable. outputs_collections: collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for variable_op_scope. Returns: the tensor variable representing the result of the series of operations. Raises: ValueError: if x has rank less than 2 or if its last dimension is not set. """ if not isinstance(num_outputs, int): raise ValueError('num_outputs should be integer, got %s.', num_outputs) with tf.variable_op_scope([inputs], scope, 'sampled_softmax_loss', reuse=reuse) as sc: inputs = tf.convert_to_tensor(inputs) dtype = inputs.dtype.base_dtype inputs_shape = inputs.get_shape() num_input_units = tf.contrib.layers.utils.last_dimension(inputs_shape, min_rank=2) static_shape = inputs_shape.as_list() static_shape[-1] = num_outputs weights_shape = [num_input_units, num_outputs] weights_collections = tf.contrib.layers.utils.get_variable_collections( variables_collections, 'weights') weights = tf.contrib.framework.model_variable('weights', shape=weights_shape, dtype=dtype, initializer=weights_initializer, regularizer=weights_regularizer, collections=weights_collections, trainable=trainable) if len(static_shape) > 2: # Reshape inputs inputs = tf.reshape(inputs, [-1, num_input_units]) biases_collections = tf.contrib.layers.utils.get_variable_collections( variables_collections, 'biases') biases = tf.contrib.framework.model_variable('biases', shape=[num_outputs,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, collections=biases_collections, trainable=trainable) outputs = tf.nn.sampled_softmax_loss( tf.transpose(weights), biases, inputs, labels, num_sampled, num_outputs, **sampled_softmax_loss_args) return tf.contrib.layers.utils.collect_named_outputs(outputs_collections, sc.name, outputs) def patch_classes(Builder, BuilderTree, Applicative): #Builder Builder.register_method(polynomial_layer, "tensorbuilder.Builder") Builder.register_map_method(minimize, "tensorbuilder.Builder") Builder.register_map_method(maximize, "tensorbuilder.Builder") Builder.register_map_method(drop_layer, "tensorbuilder.Builder") Builder.register_map_method(add_regularization_loss, "tensorbuilder") Builder.register_map_method(fully_connected_from_product, "tensorbuilder") Builder.register_map_method(sampled_softmax_loss, "tensorbuilder") Builder.register_map_method(sampled_softmax_loss, "tensorbuilder") #Tree BuilderTree.register_method(ensamble_dropout, "tensorbuilder.BuilderTree")
Module variables
var builders_blacklist
Functions
def add_regularization_loss(
tensor, graph=None, scope='add_regularization_loss')
def add_regularization_loss(tensor, graph=None, scope='add_regularization_loss'): if not graph: reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) else: reg_losses = graph.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) with tf.variable_scope(scope): reg_loss = tf.reduce_sum([ tf.reduce_mean(reg_loss) for reg_loss in reg_losses ], name='reg_loss_mean_sum') return tf.add(tensor, reg_loss)
def drop_layer(
x, keep_prob, seed=None, name=None)
Computes dropout.
With probability keep_prob
, outputs the input element scaled up by
1 / keep_prob
, otherwise outputs 0
. The scaling is so that the expected
sum is unchanged.
Args:
x: A tensor.
keep_prob: A scalar Tensor
with the same type as x. The probability
that each element is kept.
noise_shape: A 1-D Tensor
of type int32
, representing the
shape for randomly generated keep/drop flags.
seed: A Python integer. Used to create random seeds. See
set_random_seed
for behavior.
name: A name for this operation (optional).
Returns:
A Tensor of the same shape of x
.
Raises:
ValueError: If keep_prob
is not in (0, 1]
.
def drop_layer(x, keep_prob, seed=None, name=None): """Computes dropout. With probability `keep_prob`, outputs the input element scaled up by `1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected sum is unchanged. Args: x: A tensor. keep_prob: A scalar `Tensor` with the same type as x. The probability that each element is kept. noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. seed: A Python integer. Used to create random seeds. See [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed) for behavior. name: A name for this operation (optional). Returns: A Tensor of the same shape of `x`. Raises: ValueError: If `keep_prob` is not in `(0, 1]`. """ with tf.op_scope([x], name, "drop_layer") as name: x = tf.convert_to_tensor(x, name="x") if isinstance(keep_prob, float) and not 0 < keep_prob <= 1: raise ValueError("keep_prob must be a scalar tensor or a float in the " "range (0, 1], got %g" % keep_prob) keep_prob = tf.convert_to_tensor(keep_prob, dtype=x.dtype, name="keep_prob") keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar()) noise_shape = [ tf.shape(x)[0], 1 ] # uniform [keep_prob, 1.0 + keep_prob) random_tensor = keep_prob random_tensor += tf.random_uniform( noise_shape, seed=seed, dtype=x.dtype ) # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = tf.floor(random_tensor) ret = x * tf.inv(keep_prob) * binary_tensor ret.set_shape(x.get_shape()) return ret
def ensamble_dropout(
tree, keep_prob, seed=None, name=None)
THIS METHOD IS AUTOMATICALLY GENERATED
@immutable
This method the same as tensorbuilder.BuilderTree.ensamble_dropout
.
Original Documentation for tensorbuilder.BuilderTree.ensamble_dropout
def ensamble_dropout(tree, keep_prob, seed=None, name=None)
None
def ensamble_dropout(tree, keep_prob, seed=None, name=None): with tf.op_scope(tree.tensors(), name, "drop_layer"): return tree.map_each(drop_layer, keep_prob, seed=seed, name=name)
def fully_connected_from_product(
*args, **kwargs)
Adds a fully connected layer.
fully_connected
creates a variable called weights
, representing a fully
connected weight matrix, which is multiplied by the inputs
to produce a
Tensor
of hidden units. If a normalizer_fn
is provided (such as
batch_norm
), it is then applied. Otherwise, if normalizer_fn
is
None and a biases_initializer
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation_fn
is not None
,
it is applied to the hidden units as well.
Note: that if inputs
have a rank greater than 2, then inputs
is flattened
prior to the initial matrix multiply by weights
.
Args:
inputs: A tensor of with at least rank 2 and value for the last dimension,
i.e. [batch_size, depth]
, [None, None, None, channels]
.
num_outputs: Integer, the number of output units in the layer.
activation_fn: activation function.
normalizer_fn: normalization function to use instead of biases
. If
normalize_fn
is provided then biases_initializer
and
biases_regularizer
are ignored and biases
are not created nor added.
normalizer_params: normalization function parameters.
weights_initializer: An initializer for the weights.
weights_regularizer: Optional regularizer for the weights.
biases_initializer: An initializer for the biases. If None skip biases.
biases_regularizer: Optional regularizer for the biases.
reuse: whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
variables_collections: Optional list of collections for all the variables or
a dictionary containing a different list of collections per variable.
outputs_collections: collection to add the outputs.
trainable: If True
also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
scope: Optional scope for variable_op_scope.
Returns:
the tensor variable representing the result of the series of operations.
Raises:
ValueError: if x has rank less than 2 or if its last dimension is not set.
@functools.wraps(func) def func_with_args(*args, **kwargs): current_scope = _current_arg_scope() current_args = kwargs key_func = _key_op(func) if key_func in current_scope: current_args = current_scope[key_func].copy() current_args.update(kwargs) return func(*args, **current_args)
def maximize(
tensor, optimizer, *args, **kwargs)
def maximize(tensor, optimizer, *args, **kwargs): return optimizer.maximize(tensor, *args, **kwargs)
def minimize(
tensor, optimizer, *args, **kwargs)
def minimize(tensor, optimizer, *args, **kwargs): return optimizer.minimize(tensor, *args, **kwargs)
def patch_classes(
Builder, BuilderTree, Applicative)
def patch_classes(Builder, BuilderTree, Applicative): #Builder Builder.register_method(polynomial_layer, "tensorbuilder.Builder") Builder.register_map_method(minimize, "tensorbuilder.Builder") Builder.register_map_method(maximize, "tensorbuilder.Builder") Builder.register_map_method(drop_layer, "tensorbuilder.Builder") Builder.register_map_method(add_regularization_loss, "tensorbuilder") Builder.register_map_method(fully_connected_from_product, "tensorbuilder") Builder.register_map_method(sampled_softmax_loss, "tensorbuilder") Builder.register_map_method(sampled_softmax_loss, "tensorbuilder") #Tree BuilderTree.register_method(ensamble_dropout, "tensorbuilder.BuilderTree")
def polynomial_layer(
builder, size, **kwargs)
THIS METHOD IS AUTOMATICALLY GENERATED
@immutable
This method the same as tensorbuilder.Builder.polynomial_layer
.
Original Documentation for tensorbuilder.Builder.polynomial_layer
def polynomial_layer(builder, size)
Creates a fully connected layer of size size
and then applies the activation function
y(i) = z(i)^(i+1)
where z = w*x + b
def polynomial_layer(builder, size, **kwargs): """ Creates a fully connected layer of size `size` and then applies the activation function ``` y(i) = z(i)^(i+1) ``` where `z = w*x + b` """ with tf.variable_scope("polynomial_layer"): return builder.fully_connected(size, activation_fn=_polynomial, **kwargs)
def sampled_softmax_loss(
*args, **kwargs)
Adds a fully connected layer.
fully_connected
creates a variable called weights
, representing a fully
connected weight matrix, which is multiplied by the inputs
to produce a
Tensor
of hidden units. If a normalizer_fn
is provided (such as
batch_norm
), it is then applied. Otherwise, if normalizer_fn
is
None and a biases_initializer
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation_fn
is not None
,
it is applied to the hidden units as well.
Note: that if inputs
have a rank greater than 2, then inputs
is flattened
prior to the initial matrix multiply by weights
.
Args:
inputs: A tensor of with at least rank 2 and value for the last dimension,
i.e. [batch_size, depth]
, [None, None, None, channels]
.
num_outputs: Integer, the number of output units in the layer.
activation_fn: activation function.
normalizer_fn: normalization function to use instead of biases
. If
normalize_fn
is provided then biases_initializer
and
biases_regularizer
are ignored and biases
are not created nor added.
normalizer_params: normalization function parameters.
weights_initializer: An initializer for the weights.
weights_regularizer: Optional regularizer for the weights.
biases_initializer: An initializer for the biases. If None skip biases.
biases_regularizer: Optional regularizer for the biases.
reuse: whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
variables_collections: Optional list of collections for all the variables or
a dictionary containing a different list of collections per variable.
outputs_collections: collection to add the outputs.
trainable: If True
also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
scope: Optional scope for variable_op_scope.
Returns:
the tensor variable representing the result of the series of operations.
Raises:
ValueError: if x has rank less than 2 or if its last dimension is not set.
@functools.wraps(func) def func_with_args(*args, **kwargs): current_scope = _current_arg_scope() current_args = kwargs key_func = _key_op(func) if key_func in current_scope: current_args = current_scope[key_func].copy() current_args.update(kwargs) return func(*args, **current_args)