Dumbarton couple organising free festive show for local kids
David and Jennifer Clarke have created the fun panto-style performance, which gets underway this month.
by Linzi Watson, https://www.dailyrecord.co.uk/authors/linzi-watson/A Dumbarton couple are staging a free children’s festive show in true Christmas spirit for youngsters across the area.
David and Jennifer Clarke have created a fun panto-style performance called Santa and the Heebie Jeebies, which kicks off later this month.
They have launched business Lost Entertainment and have decided to offer their first show for free for disadvantaged children.
David said: “We are putting on a series of Christmas shows at the Denny Civic Theatre in Dumbarton.
“We are a local family and have struggled to make ends meet for long enough and have really pushed ourselves to the limits to start our own business and put this show together.
“We have decided to invite the children and their families from Robin House to join us free of charge.
“We’ve also reached out to the women’s refuge in the hope that any young families their who would like to join us can.
“We have a seven-year-old daughter and it’s heartbreaking as a parent to hear about other children going through difficulties.
“We really wanted to give something back and offer them a nice afternoon out.”
The Christmas show is based around a story David wrote for his daughter Emily, who is pictured above and will also star in the show, called The Heebie Jeebie.
He has given a whole new take on the phrase ‘heebie jeebies’, typically used to describe a particular type of nervousness.
David explained: “The original story is based on the Scottish saying, when something gives you the ‘heebie jeebies’.
“The story is that heebie jeebies are actually little creatures who come and fix broken things. The idea is that they are not scary at all and are doing good things so it has a moral undertone to it.
“Actor Ian Sexon, who is known for doing voiceovers, will be playing a big role and he has adapted the story for the show.
“It will be very much a panto-style show with slapstick humour. Kids will really enjoy it.”
David and Jennifer are planning to bring more children’s shows to the Dumbarton area.
He added: “I used to work at the fair when I was a kid and I’ve always enjoyed that kind of thing. I worked long hours as a crane operator for 15 years and missed out on time with my family so I decided to start up my own business and thought this would be the perfect opportunity.
“It’s important to us that the first show we stage is free to children and their families.”
The free show will take place on Sunday, November 24 and two more will take place that day too. More shows will take place on December 8, December 15 and December 22.
For more local news from West Dunbartonshire click here .


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--- title: Designing Data-Driven Interfaces date: '2015-07-12T19:00:03+05:30' tags: - ml tumblr_url: https://sureshshanmugam.com/post/123887769772/designing-data-driven-interfaces --- Designing Data-Driven Interfaces


One-to-one English literature and language tuition
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I provide one-to-one English literature and language tuition from my home in Laxey to pupils across the Island. I teach adult learners, GCSE students and primary pupils. My hours are flexible – get in touch to find a slot that suits you.
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I provide exam-specific guidance and practise; I also help with basic skills: sentence structure, punctuation and reading comprehension.
I like to tailor my lessons to the interests of my pupils, and I encourage my pupils, as they progress, to become increasingly involved in the planning – this way they learn to take ownership of the process.
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I write blog articles and content on the subjects of design, technology and the environment.
About me
I am an English graduate of Edinburgh University with a PGCE from Brunel. I have been providing private tuition to pupils from Year 5 to Year 11 for over 5 years.
What I love about this work is that it gives me the opportunity to watch my pupils grow in confidence and ability over the years.
There is nothing more satisfying than the expression on someone’s face when they produce a piece of work that they are proud of – or when they overcome an obstacle that they thought they would never conquer.
This is why I do it.


This software is made as a project for Chitkara University and none of their resources are used
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No one holds any right on this software other than its original creators.
No one is able to modify or reverse engineer it in any form without permission from its original creators.
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libraries and is not liable to pay anything
to them until this product is commercial and is finally released in the market.


A listbox button is intended for use as a custom implementation of the native HTML select element's single-select use case.
Because a button element does not store form data, its value will not be submitted along with other form data without the assistance of JavaScript.
NOTE: only limited JavaScript behaviour has been implemented in these examples. For detailed behaviour requirements, please visit the eBay MIND Pattern for Listbox Button.
By default, the first option should be selected if the user does not specify a selection. This matches the behaviour of a native HTML select element.
An initial selection can be specified by applying the aria-selected state to a single option.
A listbox button attempts to mimic the behaviour of an HTML select by displaying the currently selected value in its collapsed state. In HTML however, a button is labelled by its inner text, whereas a select is labelled by an external label tag. For any button, its label must at all times reflect its function.
All of this leaves us with a conundrum, with three potential solutions outlined below.
We can use aria-labelledby to stitch together an external text element with the internal value.
The button's inner text can be written as a key/value pair, where key denotes purpose and value repesents the current selection.
We can write our option values such that the context can be derived from the option value text alone (this isn't always easy or recommended).
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{% extends "books/base.html" %} {% block title %}My Profile{% endblock %} {% block content %}
{{ favorite_book.book.author.name }}
List is empty. How about to start reading something?
{{ review.created_at|date:"D d M Y" }} {{ review.created_at|time:"H:i" }}
{{ review.content | truncatechars:150 }}
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Software Engineer/Developer, Computer Science undergraduate.
A passionate learner who aspires to daily self-improvement. Currently in my final year of Computer Science studies at National University of Singapore.
Computer Science (Artificial Intelligence)
BCME
Rank Points: 87.5
I was stationed at Schneider Electric's HubAsia Distribution Centre
to help with improving the efficiency of warehouse processes. As the only software engineer onsite, I was given a number of projects which allowed me to learn
fullstack software development skills (through developing apps from scratch). This involved UI design/programming and SQL database deployment/integration,
both of which I had no prior experience with. However, I became comfortable with them after a month of simultaneous research and diving head first into the
projects.
Listed below are some of the projects and responsiblities I picked up at Schneider Electric.
Background info
One outbound process is called "Releasing", and it is simply when goods are released from the warehouse into the ownership of their respective customers (usually
through delivery). In some cases with smaller packages, customers can choose to personally visit Hub Asia and collect their items. These packages need to be prepared
(picked from storage and packed properly by Releasing personnel) before they can be released to customers for collection. Sometimes, customers may arrive before their
packages are ready for collection and therefore will need to wait. Once the package is ready, the customer can then collect it and leave Hub Asia.
Problem
Issues in customer dissatisfaction and time wastage rise during these instances where customers need to wait for their packages to be prepared. Customers can verbally
enquire the Releasing personnel present at the reception area about the status and time of completion for their packages. Sometimes, the information given may be slightly
inaccurate, leading customers to enquire repeatedly when their packages take longer than expected. This is where customer dissatisfaction rises as well as time wastage due
to the Releasing personnel needing to repeatedly check and verbally update customers.
My solution
I developed a multi-component application to provide a data-driven connection from the start to the end of the self-collection process. The app is deployed on 3 computers:
(1) guardhouse check-in/check-out computer, (2) Releasing team computer, (3) reception area computer hooked up to a large television screen. Each area serves a different
purpose, and thus can only access its relevant components in the app. Customers arriving at Hub Asia will first check in at the guardhouse by submitting their information
through the check-in component of the app. Releasing personnel can then see the customer information on the app on their computer, and provide updates on package status and estimated completion time through
the info-update component interface. When Releasing personnel provide these updates, the information is immediately displayed on the reception area television through the dashboard page component
of the app. Customers waiting at the reception area can therefore refer to the live dashboard for instant updates on their package information. Once a customer's package is ready, they will be prompted
through the dashboard. Then, the customer can collect their package, check-out at the guardhouse, and leav Hub Asia.
Ever since the implementation of the dashboard, surveys have found that customer satisfaction had increased. Releasing team personnel also found that they had more time to focus on other responsibilities
since they no longer needed to verbally update customers every now and then.
Background info
One of the warehouse processes is "Put Away", where inbound delivered goods are received and need to be recorded before being kept away. Part
of this process involves scanning the barcodes on labels pasted on the packaging. This crosschecks with information in a database
to see if the labels are correct. There are 5 such stations, each with its own computer and barcode scanner.
Problem
The problem with this was that the interface being used by operators for barcode scanning was a makeshift app using excel and VBA,
where one sheet acts as the main scanning page, another sheet contains the data for crosschecking, and another sheet records the
scans conducted by operators. This means each of the 5 station computers needs its own copy of the excel file, resulting in 5 duplicates
of the same crosschecking data, all of which need to be simultaneously updated whenever new data is available. Also, all the recorded data
of barcode scans conducted had to be pooled from the 5 separate excel files and compiled in order to display statistics in Tableau. Lastly,
excel files have a hard limit on the number of rows per sheet which limits the lifespan of this implementation.
My solution
I extracted and migrated the reference data for label cross-checking into a proper MySQL database.
This way, all the scanning station computers could access the same centralized database for information.
All the data for each scan conducted is also stored into the same database, and pooling is no longer necessary as Tableau can connect directly
to the centralized database to generate live reports and statistics. Users' login credentials are also stored on the database instead of the previous
excel sheets, improving security.
In order to interface with the database, I developed a desktop application to replace the Excel files. This app provided the same barcode scanning
functionality in addition to a few other requested features such as automatic updating of the crosscheck database, exporting scan records into an
excel file for CS&Q (Customer Satisfaction & Quality) personnel to check, as well as generic user management (adding & deleting of users) for admins.
Ever since the implementation of the database and application, CS&Q personnel could save 1 hour every day since they no longer needed to manually collate
data from multiple Excel files. This translates to roughly $284 being saved monthly.
Background info
The thousands of barcode scans done during the put away process can result in varying statuses (CORRECT, INCORRECT, VISUAL CHECK). "INCORRECT" and
"VISUAL CHECK" statuses imply that some issue is present which needs to be fixed, with various possible root causes for these issues.
Problem
In order to stay updated as well as investigate these issues effectively, CSQ and management personnel require daily reports on the scans conducted.
This includes a summary of the statuses encountered along with their root causes.
My solution
I wrote a Python script which runs automatically every morning to send an e-mail to a list of mailees. This script connects to the database I created for the
Put Away Scanning app to retrieve the relevant data, generate graphics (pareto charts, pivot tables, simple HTML tables) to display the information, and
send the formatted e-mail to personnel.
As a member of the WRC Sports Hub, I worked with colleagues to plan and organise activities which encourage and provide opportunities for employees to have fun while exercising. We wanted to introduce a culture of fun and fitness, especially at our HubAsia office which had a warehouse environment where operators rarely have the chance to leave and participate in recreational activities.
We have an E-Idea programme where employees can submit any innovative ideas for improving any aspects of the workplace, such as safety, environmental-friendliness, productivity, etc. Every month and quarter, the best idea will be announced and the submitter will be rewarded. As part of the E-Idea committee, My fellow members and I would have monthly meetings to run through the submitted ideas together, analyze and award points to each idea, and decide on the winner for the month.
We hosted a Chinese New Year celebration for all employees at HubAsia which included a buffet as well as team games. I took up the role of emcee-ing to facilitate the flow of activities.
Working at Schneider Electric has been absolutely enriching and undoubtedly valuable to me. The amount of things I've learnt from this internship is astounding. Whether it be the subtle details or large subjects, technical or soft skills, I was exposed to so much new knowledge and experience that I know I've improved tremendously both as a programmer and as a person. Their well-structured internship programme, inviting culture, and willingness to place large responsibilities and meaningful projects on the shoulders of us interns made for an incredibly memorable and educative experience. Especially with regards to the projects, seeing my work grow and be actually deployed live into their daily production was extremely satisfying and gave me a great sense of achievement.
LinkedIn PostI crafted a lesson plan for teaching beginners to play the ukulele, allowing students without to play recognizable songs within an hour. I used self-designed, visually-appealing slides and wrote intuitive song tabs to facilitate the lesson.
As a homework teacher, I guided students ranging from Primary 1 to Primary 6 in all their school subjects.
I taught students ranging from lower primary to upper secondary in their subjects of English, Mathematics and Science (including pure physics, chemistry and biology for the secondary school students).
I teamed up with friends in this hackathon to create a top-down view multiplayer desktop maze game concept.
My team and I built a web-based drumming application using p5.js, targeted towards helping drummers gain a better sense of rhythm.
We designed and created a mock trip planner/advisor feature with the idea for it to be integrated with Grab.
During my internship at Schneider Electric, I was in charge of designing and developing functional desktop applications to be used daily in live production environments.
Since I was the only software engineer, I became intimately familiar with every facet of development from beginning till end. Some things I learnt include: UI design/programming
(to create user-friendly interfaces), MySQL database creation and integration (for all the apps running on computers throughout the warehouse to simultaneously store and retrieve
data), and defensive programming so as to minimize bugs in the app (which would be costly if encountered in production).
Of course, I have worked with teams as well on software engineering projects in NUS and am able to communicate/work effectively with other developers to achieve our goals.
Apart from programming, I am a self-taught ukulele fingerstylist. My interest in the instrument resulted in me coming across ukulele virtuosos such as Jake Shimabukuro and Kalei Gamiao, whose performances ignited my passion and inspired me to learn to play like them. To date, I have performed multiple times (sometimes with a band but mostly solos) for events such as concerts and competitions, and I have also conducted and introductory workshop for people wishing to pick up the ukulele.
I seek to continue performing in public as well as teaching more lessons. I have also uploaded some song covers to YouTube and strive to upload more in future.
Worked together with other undergraduates to construct safe water gardens to provide hygienic sanitation for local families. In addition to giving safe sanitation, the waste water is channeled to fertilize a garden where residents can grow their own produce for sale or consumption.
On the last day, we also organized games and interaction sessions for children from a nearby orphanage.
We worked together to help construct an outdoor shelter for a nursery, as well as paint a wall mural for an orphanage. We also conducted lessons and cultural exchange activities to engage with the locals.
Thanks for visiting and reading this page! If you made it this far and are finally bored, I hope I can entertain you with some stuff I've coded :)
Built mainly using p5js (a Javascript library geared towards creating graphic and interactive experiences, and based on the principles of processing), I've attempted to clone the internationally sensational and ridiculously addictive game known as Flappy Bird.


OPD Cash Receipt
Cancer Unit Cash Counter
Mr first name
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Mr first name
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01:58:27 PM
| Service Rendered | Total Charges |
|---|---|
| CD CHARGES-C.T.SCAN | 550.00 |
| Total | 550.00 |
| [Rs Five hundered and Fifty only] |
|---|
| Mrs. Kirti Holkar |
| Subject to INDORE Jurisdiction |


Uber to let users record audio of rides in Brazil, Mexico
by CATHY BUSSEWITZ AP Business WriterNEW YORK (AP) — Uber will let passengers and drivers record audio of their rides in an attempt to improve its safety record.
The ride-hailing company plans to pilot the feature in cities in Brazil and Mexico in December. It eventually hopes to launch it in other markets including the United States, although it has no timeline for possible expansion.
The feature will allow customers to opt into recording all or select trips.
Uber has struggled with safety issues and faced accusations that some of its drivers have assaulted and raped passengers. Some of its drivers also have been attacked by passengers.
Uber says the recordings will be encrypted to protect privacy. Users can later share the recording with Uber if they want to report a problem.


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Safe Numerics |
The library is under development. There are a number of issues still pending.
The library is currently limited to integers.
Conversions to safe integer types from floating point types is not explicitly addressed.
Note that standard library stream conversion functions such as
strtoi
etc. DO check for valid input and throw the
exception std::out_of_range
if the string cannot be
converted to the specified integer type. In other words,
strtoi
already contains some of the functionality that
safe<int>
provides.
Although care has been taken to make the library portable, it's
likely that at least some parts of the implementation - particularly
checked
arithmetic - depend upon two's complement
representation of integers. Hence the library is probably not
currently portable to all other possible C++ architectures.
Currently the library permits a safe<int>
value to be uninitialized. This supports the goal of "drop-in"
replacement of C++/C built-in types with safe counter parts. On the
other hand, this breaks the "always valid" guarantee.


This time, our transaction fails because Emery has already been added to the database. The addition of Fiona has been rolled back, and we’ll need to get to the bottom of this discrepancy (or make some schematic changes to our database to allow for multiple managers per user).
& More…
We’ve only explored the surface to SQLite.swift. Dive into the code to discover more!


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An arrangement of eight identical squares in a plane, attached to one another edge to edge.
There are 369 possible octominoes, 6 of which surround a hole.


On April 28 1945, at the Piazzale Loreto in Milan, Benito Mussolini and his mistress Clara Petacci were shot and hanged in a spectacle that was photographed repeatedly. His last words were, "Shoot me in the chest!"


{% extends "layout.html" %} {% block title %}TryIIIF (Mirador Viewer){% endblock %} {% block head %}
The Mirador Viewer below allows you to zoom, rotate, view full screen, annotate, and compare several images side by side. It also displays metatdata about the image and allows you to bookmark an image. (Not all feateures work in this instance.)
The image viewer configuration file
{% endblock %}


Important
Your prefix cannot have any spaces or the bot will exit with an error message.
--- layout: 0.0.6-largePage column: 0.0.6/headers/documentation.html color: is-link title: Getting started subtitle: Getting started with the Spark framework menu: 0.0.6/menus/documentation/documentation.html ---
const Spark = require("sparkbots") Spark.start({ prefix: "prefix-here", token: "token-here" })
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or ?
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MIT Engineer
Unicorn!
Pro Athlete
Globetrotter
Born and raised in the San Francisco Bay Area, Kevin revises Computer Science at MIT — Class of 2019
—
with a concentration in Theater Arts. This past summer, he interned at Microsoft as a Software
Engineer
in Azure Compute.
He spent the first half of the year abroad, first, teaching high school students in Germany, and
then,
revising Informatik at ETH Zürich, Switzerland, for the inaugural MIT EECS x ETH exchange program.
Kevin is the two-time reigning Collegiate Champion in figure skating, and has traveled the nation and
world as a member of Team USA.


| with.mitml.list {mitml} | R Documentation |
The with
and within
methods evaluate R expressions in a list of multiply imputed data sets.
## S3 method for class 'mitml.list' with(data, expr, include.data = FALSE, ...) ## S3 method for class 'mitml.list' within(data, expr, ignore = NULL, ...)
data |
A list of imputed data sets with class |
expr |
An R expression to be evaluated for each data set. |
include.data |
Either a logical flag or a character string denoting how the data are included when |
ignore |
A character vector naming objects that are created but should not be saved (see 'Details'). |
... |
Not used. |
The two functions provide with
and within
methods for objects of class mitml.list
.
They evaluate an R expression repeatedly for each of the imputed data sets but return different values: with
returns the result of the evaluated expression; within
returns the resulting data sets.
The within
function is useful for transforming and computing variables in the imputed data (e.g., centering, calculating cluster means, etc.).
The with
function is useful, for example, for fitting statistical models.
The list of fitted models can be analyzed using testEstimates
, testModels
, testConstraints
, or anova
.
The include.data
argument can be used to include the imputed data sets in the call to fit statistical models (expr
) using with
.
This is useful for fitting models that require that the fitting function be called with a proper data
argument (e.g., lavaan
or nlme
; see 'Examples').
Setting include.data = TRUE
will fit the model with the imputed data sets used as the data
argument.
Setting include.data = "df"
(or similar) will fit the model with the imputed data sets as the df
argument (useful if the function refers to the data by a nonstandard name, such as "df"
).
The ignore
argument can be used to declare objects that are not to be saved in the data sets created by within
.
with
: A list of class mitml.results
containing the evaluated expression for each data set.
within
: A list of class mitml.list
containing the imputed data modified by the evaluated expression.
Simon Grund
mitmlComplete
, anova.mitml.result
, testEstimates
, testModels
, testConstraints
data(studentratings) fml <- ReadDis + SES ~ ReadAchiev + (1|ID) imp <- panImpute(studentratings, formula = fml, n.burn = 1000, n.iter = 100, m = 5) implist <- mitmlComplete(imp) # * Example 1: data transformation # calculate and save cluster means new1.implist <- within(implist, Means.ReadAchiev <- clusterMeans(ReadAchiev, ID)) # center variables, calculate interaction terms, ignore byproducts new2.implist <- within(implist, { M.SES <- mean(SES) M.CognAbility <- mean(CognAbility) C.SES <- SES - M.SES C.CognAbility <- CognAbility - M.CognAbility SES.CognAbility <- C.SES * C.CognAbility }, ignore = c("M.SES", "M.CognAbility")) # * Example 2: fitting statistical models # fit regression model fit.lm <- with(implist, lm(ReadAchiev ~ ReadDis)) # fit multilevel model with lme4 require(lme4) fit.lmer <- with(implist, lmer(ReadAchiev ~ ReadDis + (1|ID))) ## Not run: # fit structural equation model with lavaan (with include.data = TRUE) require(lavaan) mod <- "ReadAchiev ~ ReadDis" fit.sem <- with(implist, sem(model = mod, cluster = "ID", estimator = "MLR"), include.data = TRUE) ## End(Not run)


--- title: Multi-Point Regression keywords: fastai sidebar: home_sidebar nb_path: "nbs/course2020/vision/06_Multi_Point_Regression.ipynb" ---
Here we deal with a single leaf image and we have to predict wether the leaf is healthy, has multiple diseases, has rust, has scab.
So one input image and 4 columns to predict.
In the evaluation we have For each image_id in the test set, you must predict a probability for each target variable.
so we'll set it up as a regression problem.
The data is available here.
from fastai.vision.all import *
import pandas as pd
Download and unzip your data to a folder called plant
path= 'plant/'
Let us see what is there in train.csv
train = pd.read_csv(path+'train.csv')
train.head()
We need to create a tuple is (x,y)
for our model to train. So we'll create like this (image_id, [healthy multiple_diseases rust scab])
Let's create a new column combined
which is a list of the dependent variables
train['combined'] = train[['healthy','multiple_diseases','rust','scab']].values.tolist()
train.head()
For show_batch
to work we need to add the ability for a list to have show_title
class TitledList(list, ShowTitle):
_show_args = {'label': 'text'}
def show(self, ctx=None, **kwargs):
"Show self"
return show_title(self, ctx=ctx, **merge(self._show_args, kwargs))
class ToListTensor(DisplayedTransform):
"Transform to int tensor"
# order = 10 #Need to run after PIL transforms on the GPU
_show_args = {'label': 'text'}
def __init__(self, split_idx=None,):
super().__init__(split_idx=split_idx)
def encodes(self, o): return o
def decodes(self, o): return TitledList(o)
Independent variable is the image we'll use a ImageBlock.
Dependent varaible we'll use a RegressionBlock, here we need to set c_out
.
And we add ToListTensor
to the get_y
blocks = [ImageBlock, RegressionBlock(c_out=4)]
item_tfms = [Resize(150)];# size should be bigger
batch_tfms = [*aug_transforms(flip_vert=True,size=(128)), Normalize.from_stats(*imagenet_stats)]
splitter = RandomSplitter()
plant = DataBlock(blocks =blocks,
get_x = ColReader('image_id', pref=f'gdrive/My Drive/kaggle/plant/images/',suff='.jpg'),
get_y = Pipeline([ColReader('combined'),ToListTensor]),
splitter =splitter,
item_tfms=item_tfms,
batch_tfms = batch_tfms,
n_inp = 1
)
dls = plant.dataloaders(train)
dls.show_batch(nrows=2,ncols=2,figsize=(10,10))
plant.summary(train)
key things to notice:
[0, 0, 0, 1] becomes tensor([0., 0., 0., 1.])
dls.c = 4
model = resnet18
Choose an appropriate Loss function and accuracy for a regression problem
learn = cnn_learner(dls, model, metrics=[MSELossFlat()], loss_func=L1LossFlat(),y_range=(0,1),)
learn.fine_tune(2)
test_img = pd.read_csv(path+'test.csv')
dl = learn.dls.test_dl(test_img)
probs,_ = learn.get_preds(dl=dl)
p1 = pd.DataFrame(probs,columns=[['healthy','multiple_diseases','rust','scab']])
p1['image_id'] = test_img.image_id
cols = ['image_id','healthy','multiple_diseases','rust','scab']
p1[cols].head()


QPolygonF Class
The QPolygonF class provides a vector of points using floating point precision. More...
| Header: | #include <QPolygonF> |
| qmake: | QT += gui |
| Inherits: | QVector |
Note: All functions in this class are reentrant.
Public Functions
| QPolygonF() | |
| QPolygonF(int size) | |
| QPolygonF(const QVector<QPointF> &points) | |
| QPolygonF(QVector<QPointF> &&v) | |
| QPolygonF(const QRectF &rectangle) | |
| QPolygonF(const QPolygon &polygon) | |
| QPolygonF(const QPolygonF &polygon) | |
| QPolygonF(QPolygonF &&other) | |
| ~QPolygonF() | |
| QRectF | boundingRect() const |
| bool | containsPoint(const QPointF &point, Qt::FillRule fillRule) const |
| QPolygonF | intersected(const QPolygonF &r) const |
| bool | isClosed() const |
| QPolygonF | subtracted(const QPolygonF &r) const |
| void | swap(QPolygonF &other) |
| QPolygon | toPolygon() const |
| void | translate(const QPointF &offset) |
| void | translate(qreal dx, qreal dy) |
| QPolygonF | translated(const QPointF &offset) const |
| QPolygonF | translated(qreal dx, qreal dy) const |
| QPolygonF | united(const QPolygonF &r) const |
| operator QVariant() const | |
| QPolygonF & | operator=(QPolygonF &&other) |
| QPolygonF & | operator=(const QPolygonF &other) |
- 89 public functions inherited from QVector
Related Non-Members
| QDataStream & | operator<<(QDataStream &stream, const QPolygonF &polygon) |
| QDataStream & | operator>>(QDataStream &stream, QPolygonF &polygon) |
Additional Inherited Members
- 2 static public members inherited from QVector
Detailed Description
The QPolygonF class provides a vector of points using floating point precision.
A QPolygonF is a QVector<QPointF>. The easiest way to add points to a QPolygonF is to use its streaming operator, as illustrated below:
QPolygonF polygon; polygon << QPointF(10.4, 20.5) << QPointF(20.2, 30.2);
In addition to the functions provided by QVector, QPolygonF provides the boundingRect() and translate() functions for geometry operations. Use the QMatrix::map() function for more general transformations of QPolygonFs.
QPolygonF also provides the isClosed() function to determine whether a polygon's start and end points are the same, and the toPolygon() function returning an integer precision copy of this polygon.
The QPolygonF class is implicitly shared.
See also QVector, QPolygon, and QLineF.
Member Function Documentation
QPolygonF::QPolygonF()
Constructs a polygon with no points.
See also QVector::isEmpty().
QPolygonF::QPolygonF(int size)
Constructs a polygon of the given size. Creates an empty polygon if size == 0.
See also QVector::isEmpty().
QPolygonF::QPolygonF(const QVector<QPointF> &points)
Constructs a polygon containing the specified points.
QPolygonF::QPolygonF(QVector<QPointF> &&v)
Default constructs an instance of QPolygonF.
QPolygonF::QPolygonF(const QRectF &rectangle)
Constructs a closed polygon from the specified rectangle.
The polygon contains the four vertices of the rectangle in clockwise order starting and ending with the top-left vertex.
See also isClosed().
QPolygonF::QPolygonF(const QPolygon &polygon)
Constructs a float based polygon from the specified integer based polygon.
See also toPolygon().
QPolygonF::QPolygonF(const QPolygonF &polygon)
Constructs a copy of the given polygon.
QPolygonF::QPolygonF(QPolygonF &&other)
Move-copy constructor.
QPolygonF::~QPolygonF()
Destroys the polygon.
QRectF QPolygonF::boundingRect() const
Returns the bounding rectangle of the polygon, or QRectF(0,0,0,0) if the polygon is empty.
See also QVector::isEmpty().
bool QPolygonF::containsPoint(const QPointF &point, Qt::FillRule fillRule) const
Returns true
if the given point is inside the polygon according to the specified fillRule; otherwise returns false
.
This function was introduced in Qt 4.3.
QPolygonF QPolygonF::intersected(const QPolygonF &r) const
Returns a polygon which is the intersection of this polygon and r.
Set operations on polygons will treat the polygons as areas. Non-closed polygons will be treated as implicitly closed.
This function was introduced in Qt 4.3.
bool QPolygonF::isClosed() const
Returns true
if the polygon is closed; otherwise returns false
.
A polygon is said to be closed if its start point and end point are equal.
See also QVector::first() and QVector::last().
QPolygonF QPolygonF::subtracted(const QPolygonF &r) const
Returns a polygon which is r subtracted from this polygon.
Set operations on polygons will treat the polygons as areas. Non-closed polygons will be treated as implicitly closed.
This function was introduced in Qt 4.3.
void QPolygonF::swap(QPolygonF &other)
Swaps polygon other with this polygon. This operation is very fast and never fails.
This function was introduced in Qt 4.8.
QPolygon QPolygonF::toPolygon() const
Creates and returns a QPolygon by converting each QPointF to a QPoint.
See also QPointF::toPoint().
void QPolygonF::translate(const QPointF &offset)
Translate all points in the polygon by the given offset.
See also translated().
void QPolygonF::translate(qreal dx, qreal dy)
This is an overloaded function.
Translates all points in the polygon by (dx, dy).
See also translated().
QPolygonF QPolygonF::translated(const QPointF &offset) const
Returns a copy of the polygon that is translated by the given offset.
This function was introduced in Qt 4.6.
See also translate().
QPolygonF QPolygonF::translated(qreal dx, qreal dy) const
This is an overloaded function.
Returns a copy of the polygon that is translated by (dx, dy).
This function was introduced in Qt 4.6.
See also translate().
QPolygonF QPolygonF::united(const QPolygonF &r) const
Returns a polygon which is the union of this polygon and r.
Set operations on polygons will treat the polygons as areas. Non-closed polygons will be treated as implicitly closed.
This function was introduced in Qt 4.3.
See also intersected() and subtracted().
QPolygonF::operator QVariant() const
Returns the polygon as a QVariant.
QPolygonF &QPolygonF::operator=(QPolygonF &&other)
Move-assignment operator.
QPolygonF &QPolygonF::operator=(const QPolygonF &other)
Copy-assignment operator.
Related Non-Members
QDataStream &operator<<(QDataStream &stream, const QPolygonF &polygon)
Writes the given polygon to the given stream, and returns a reference to the stream.
See also Serializing Qt Data Types.
QDataStream &operator>>(QDataStream &stream, QPolygonF &polygon)
Reads a polygon from the given stream into the given polygon, and returns a reference to the stream.
See also Serializing Qt Data Types.


Spell Card Library
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This is where you store your amazing, wonderful, powerful spell cards. How valuable!
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--- layout: post title: More on casual gaming date: 2005-10-20 00:00:00 categories: business interfaces-&-interaction mobile play ---
Incredibly well-researched and incisive article on casual gaming: "But there’s a growing movement of simpler, smaller, much less flashy games that don’t require long periods of deep gameplay, called casual games, that promise to be even bigger."


Filter by Region
Filter by Industry
| Event Name | Date | Region | Industry | Connect | |
|---|---|---|---|---|---|
| eTail East
Boston, MA |
11 - 13 September, 2015 | Americas | eCommerce / Retail | False | |
| eTail France | 5 - 7 February, 2015 | Europe | eCommerce / Retail | False | |
| LogiPharma
Montreux Music & Convention Centre, Montreux, Switzerland |
28 - 30 November, 2015 | Europe | Procurement, Pharma / Biotech | False | |
| ProcureCon Indirect
NH Grand Hotel Krasnapolsky, Amsterdam |
2015/05/21 - 2015/05/23 | Europe | Procurement | False | |
| Digital Travel Summit
Green Valley Ranch Resort, Spa & Casino, Las Vegas, NV |
2015/04/27 - 2015/04/29 | Americas | eCommerce / Retail | False | |
| ProcureCon Asia
Amara, Singapore |
2015/06/23 - 2015/06/25 | Asia | Procurement | False | |
| ProcureCon Indirect West
Wigwam, Phoenix, Arizona |
2015/09/28 - 2015/09/30 | Americas | Procurement | False | |
| BioNetwork Partnering Summit
The Ritz-Carlton, Laguna Niguel, CA |
2015/10/26 - 2015/10/28 | Americas | Pharma / Biotech, Healthcare | False | |
| LogiPharma | 15 - 18 December, 2015 | Americas | Pharma / Biotech, Supply Chain | False | |
| Field Service USA
Palm Springs, CA |
2015/04/20 - 2015/04/20 | Americas | Service / Support | False | |
| Field Service East
Westin Buckhead, Atlanta, GA |
30 - 31 November, 2015 | Americas | Service / Support | False | |
| Field Service Medical
San Diego, CA |
28 - 30 May, 2015 | Americas | Service / Support, Healthcare | False | |
| Field Service Medical Europe
Conrad Dublin, Ireland |
27 - 29 January, 2015 | Europe | Service / Support, Healthcare | False | |
| Field Service Europe
Movenpick Hotel Amsterdam City Center, Amsterdam , The Netherlands |
25 - 27 February, 2015 | Europe | Service / Support | False | |
| Mobile Shopping Connect
The Langham Pasadena Hotel, Pasadena CA |
2015/06/22 - 2015/06/24 | Americas | eCommerce / Retail | True | |
| eTail Connect
London, UK |
2015/09/08 - 2015/09/09 | Europe | eCommerce / Retail | True |


The easiest poll ever
With online poll you can create a poll in seconds, share the link with your friends, and see the result in real time.
With online poll you can create a poll in seconds, share the link with your friends, and see the result in real time.


PS Now Competitor Google Stadia Nearly Doubles Its Launch Line-Up of Old Games
Final Fantasy XV! Farming Simulator 2019!
by Liam CroftLast week, Google announced that its Stadia streaming service would launch tomorrow with just 12 old games -- all but one of which you've already played. It was a rather dire line-up, but overnight the company has gone and nearly doubled the number of titles you'll be able to purchase on launch day.
Now made up of 22 games, you can look forward to streaming the likes of Final Fantasy XV, Metro: Exodus, Trials Rising, and Wolfenstein Youngblood all on day one. With the new additions highlighted, the full list now consists of:
- Assassin’s Creed Odyssey
- Attack on Titan: Final Battle 2
- Destiny 2: The Collection (available in Stadia Pro)
- Farming Simulator 2019
- Final Fantasy XV
- Football Manager 2020
- Grid 2019
- Gylt
- Just Dance 2020
- Kine
- Metro Exodus
- Mortal Kombat 11
- NBA 2K20
- Rage 2
- Rise of the Tomb Raider
- Red Dead Redemption 2
- Samurai Shodown (available in Stadia Pro)
- Shadow of the Tomb Raider
- Thumper
- Tomb Raider 2013
- Trials Rising
- Wolfenstein: Youngblood
A subscription to Stadia Pro will now even net users a copy of Samurai Shodown, how lovely is that!? In all seriousness, this is quite obviously an improvement for anyone looking to dive straight into video game streaming before the year is out. Although, it still can't compare to the list of titles available on PlayStation Now, can it?
Some of these games were slated to arrive on Stadia before 2020, so it looks like a handful have been pushed forward to give users more choice on launch day. Could that create a bit of a drought in releases further down the line? We'll see.
Are you thinking of grabbing a subscription to Google Stadia? Tell us why you're not already signed up to PS Now in the comments below.
[via theverge.com]


jQuery Mobile provides a complete set of finger-friendly form elements that are based on native HTML form elements.
All forms should be wrapped in a form
tag that has an action
and method
that will handle the form data processing on the server.
<form action="form.php" method="post">
...
</form>
When constructing forms to be used in jQuery Mobile, most of the standard guidelines used to create forms that submit via ordinary HTTP POST or GET still apply. Additionally, the id
attributes of form controls need to be not only unique on a given page, but also unique across the pages in a site. This is because jQuery Mobile's single-page navigation model allows many different "pages" to be present in the DOM at the same time. You must be careful to use unique id
attributes so there will be only one of each in the DOM. Be sure to pair them properly with label
elements via the for
attribute.
For a more compact version of all form elements and buttons, add the data-mini="true"
attribute to the element to create a mini version. This is useful in toolbars and tight spaces but is still finger-friendly. It's possible to add this attribute to a fieldcontainer to set this on a number of elements at once.
Text Input:
<input type="text" name="name" id="basic" data-mini="true" />
This will produce an input that is not as tall as the standard version and has a smaller text size.
For the sake of accessibility, jQuery Mobile requires that all form elements be paired with a meaningful label
. To hide labels in a way that leaves them visible to assistive technologies—for example, when letting an element’s placeholder
attribute serve as a label—apply the helper class ui-hidden-accessible
to the label itself:
Username:
<input type="text" name="username" id="username" value="" placeholder="Username"/>
To hide labels within a field container and adjust the layout accordingly, add the class ui-hide-label
to the field container as in the following:

Username:
<input type="text" name="username" id="username" value="" placeholder="Username"/>

Both of the above examples will render as:
While the label will no longer be visible, it will be available to assisitive technologies such as screen readers.
All jQuery Mobile widgets can be disabled in the markup by adding the standard disabled
attribute to the element, just like you would with native controls. Each form widget also has standard disable
and enable
methods that are documented with each form widget. Here are a few examples of disabled widgets:
Note that you can disable buttons created from button
or input
-based markup, but not links with a role of button. Links don't have a parallel disabled feature in HTML, but if you need to disable a link-based button (or any element), it's possible to apply the disabled class ui-disabled
yourself with JavaScript to achieve the same effect.
To improve the styling of labels and form elements on wider screens, wrap a div
or fieldset
with the data-role="fieldcontain"
attribute around each label/form element. This framework aligns the input and associated label side-by-side, and breaks to stacked block-level elements below ~480px. The framework will also add a thin bottom border to act as a field separator.
While all form elements are now tested to work correctly within static toolbars as of jQuery Mobile 1.1, we recommend extensive testing when using form elements within fixed toolbars or within any position: fixed
elements. This can potentially trigger a number of unpredictable issues in various mobile browsers, Android 2.2/2.3 in particular (detailed in Known issues in Android 2.2/2.3).
For example:

Text Input:
<input type="text" name="name" id="name" value="" />

Will render as:
For additional examples, see the form elements gallery
By default, jQuery Mobile will automatically enhance certain native form controls into rich touch-friendly components. This is handled internally by finding form elements by tag name and running a plugin method on them. For instance, a select
element will be found and initialized with the "selectmenu" plugin, while an input
element with a type="checkbox"
will be enhanced with the "checkboxradio" plugin. Once initialized, you can address these enhanced components programmatically through their jQuery UI widget API methods. See options, methods, and events listed on each form plugin's documentation page for details.
If you should generate new markup client-side or load in content via AJAX and inject it into a page, you can trigger the create
event to handle the auto-initialization for all the plugins contained within the new markup. This can be triggered on any element (even the page div itself), saving you the task of manually initializing each plugin (see below).
For example, if a block of HTML markup (say a login form) was loaded in through Ajax, trigger the create event to automatically transform all the widgets it contains (inputs and buttons in this case) into the enhanced versions. The code for this scenario would be:
$( ...new markup that contains widgets... ).appendTo( ".ui-page" ).trigger( "create" );
In jQuery Mobile, some enhanced form controls are simply styled (inputs), but others are custom controls (selects, sliders) built from, and kept in sync with, the native control. To programmatically update a form control with JavaScript, first manipulate the native control, then use the refresh
method to tell the enhanced control to update itself to match the new state. Here are some examples of how to update common form controls, then call the refresh
method:
$("input[type='checkbox']").prop("checked",true).checkboxradio("refresh");
$("input[type='radio']").prop("checked",true).checkboxradio("refresh");
var myselect = $("#selectfoo");
myselect[0].selectedIndex = 3;
myselect.selectmenu("refresh");
$("input[type='range']").val(60).slider("refresh");
var myswitch = $("#selectbar");
myswitch[0].selectedIndex = 1;
myswitch.slider("refresh");
If you'd prefer that a particular form control be left untouched by jQuery Mobile, simply give that element the attribute data-role="none"
. For example:

<select name="foo" id="foo" data-role="none">
<option value="a" >A</option>
<option value="b" >B</option>
<option value="c" >C</option>
</select>
If you'd like to prevent auto-initialization without adding attributes to your markup, you can customize the selector that is used for preventing auto-initialization by setting the page plugin's keepNative
option (which defaults to [data-role="none"]
). Be sure to configure this option inside an event handler bound to the mobileinit
event, so that it applies to the first page as well as subsequent pages that are loaded.
$(document).bind('mobileinit',function(){
$.mobile.page.prototype.options.keepNative = "select, input.foo, textarea.bar";
});
Alternately you can use the data-enhance="false"
data attribute on a parent element with $.mobile.ignoreContentEnabled
set to true. Beware though, this will incur a performance penalty for each and every element in the page that would otherwise be enhanced as jQuery Mobile must traverse the set of parents to look for those elements.
One special case is that of selects. The above sample will prevent any and all augmentation from taking place on select elements in the page if select
is included. If you wish to retain the native performance and appearance of the menu itself and benefit from the visual augmentation of the select button by jQuery Mobile, you can set $.mobile.selectmenu.prototype.options.nativeMenu
to true in a mobileinit
callback as a global setting or use data-native-menu="true"
on a case by case basis.
Using a multipart form with a file input is not supported by ajax. In this case you should decorate the parent form with data-ajax="false"
to ensure the form is submitted properly to the server.


A psychiatrist is a medical professional who focuses entirely on the diagnosis and treatment of mental disorders. This individual uses a variety of diagnostic tools to analyze the patient's condition and symptoms to determine the root cause of the disorder. Depending on the results of the diagnostic tests, treatment options available include prescription medication, psychoanalysis, hospitalization, and psychotherapy.
Psychoanalysis focuses on the unconscious mind and its influence on behavior and thoughts. Patients meet with therapists on a regular basis for one-on-one discussions. Examining childhood experiences and identifying the effects they have on current behavior often plays a key role in psychoanalytic therapy.
Psychotherapy is an interactive process involving regular discussions between the mental health professional and the patient in an individual or group setting. The purpose of the discussions is to uncover the causes or triggers of psychological disorders. This type of therapy is goal oriented and aims to assist patients in developing the skills necessary to solve problems.
Unlike a psychologist, a psychiatrist holds a medical degree and has the authorization to prescribe medications for the treatment of patients when conditions call for it. In some patients, chemical imbalances can be the root cause of their psychological disorders. If this is the case, certain medications are effective in correcting those imbalances and allowing the individual to lead a normal life.
Some mental health professionals will specialize within the field of psychiatry. For example, child, adolescent, adult, and geriatric psychiatrists focus their attention on patients within a certain age group. Addiction psychiatrists work with patients suffering from problems associated with drug or alcohol abuse. Forensic psychiatrists work within the criminal justice system to determine if an accused person is mentally competent to stand trial.
To become a certified psychiatrist, a candidate must have a DO or MD degree issued by an accredited school of osteopathy or medicine. After completing a four-year residency with a minimum of three years specifically in psychiatry, candidates are required to take an exam with written and oral components. Those candidates who can successfully pass the examination are eligible to apply for certification through the American Board of Psychiatry and Neurology, or ABPN. Certification renewal must take place every ten years but board certified professionals are authorized to practice anywhere in the United States.
Many of the professionals in this field maintain a private practice. This gives them full control over choosing their clientele and setting their own work schedule. Some psychiatrists find employment at hospitals, universities, governmental agencies, and mental health clinics. In addition to meeting personally with patients, these individuals may spend time consulting, researching, or teaching.
Individuals who experience overwhelming feelings of sadness, depression, suicidal thoughts, stress, or anxiety can benefit by seeking the help of a qualified psychiatrist. Patients who spend quality time with an experienced therapist forge a trusting relationship that helps them develop the coping skills they need to deal with problems in a constructive way. With the help of a mental health professional, patients can enjoy a better quality of life.


Molecular Weight is
{{molwt}} Da
{{seqtype}} Sequence having Length {{len}} bp treated as {{strand}} standed and {{moltype}} molecules.
Do more...
Return home by clicking here ...
{% extends "base.html" %} {% load static %} {% block title %}
{{seqtype}} Sequence having Length {{len}} bp treated as {{strand}} standed and {{moltype}} molecules.
Return home by clicking here ...


We aggregate blockchain!
- transition best-of-breed e-business
- utilize next-generation e-markets
- mesh clicks-and-mortar users
- implement rich markets
- matrix value-added relationships
- reintermediate dot-com niches
- deploy interactive e-commerce
- evolve plug-and-play functionalities
- reinvent integrated bandwidth
- incentivize clicks-and-mortar paradigms
- target global portals
- transform plug-and-play paradigms
- extend end-to-end e-services
- engineer holistic solutions


Sun Web Application Guidelines - Version 4.1
|
| Search Guidelines | Send Feedback | Developers.sun.com |
How To Use These Guidelines - An Introduction
Appendices > Appendix B
This section of the guidelines contains examples to support various sections in the rest of the guidelines document. This section is intended to provide a more detailed look at the designs outlined in the guidelines and to provide developers and UI designers with a head start on creating guidelines-compliant HTML-based prototypes.
| Component | Section in Guidelines | |
|---|---|---|
| Accordion | Example | <IP_ADDRESS> & 5.2.5 |
| Add-and-Remove Idiom | Specification | Example | 8.2 |
| Breadcrumb/Parentage Path | Specification | Example not yet available | 6.3 |
| Buttons | Specification | Example | 7.1 |
| Common Tasks Page | Specification | Example | 5.5 |
| Date/Time Element (Scheduler) | Specification | Example not yet available | 8.4.2 |
| Editable Scrolling List | Specification | Example | 8.1 |
| File/Folder Chooser | Specification | Example | 8.5.2 |
| Form Elements |
|
7.2 & 7.3 |
| Full-Page Alert Messages | Specification | Example | 14.3 |
| Help Window | Specification | Example not yet available | 11.2 |
| HREFS | Specification | Example | -- |
| Inline Alert Messages | Specification | Example | 14.1 |
| Inline Help | Specification | Example not yet available | 11.1 |
| Label | Specification | Example | 7.3.1 & 14.5 |
| Masthead | Specification | Example | 4 |
| Page Title | Specification | Example | 6.2 |
| Pop-Up Bubble | Example | 11.6 |
| Pop-Up Calendar | Specification | Example | 8.4.1 |
| Pop-Up Menu | Example | <IP_ADDRESS> |
| Progress Indicators | Specification | Example | 15 |
| Properties Page | Specification | Example | 6 |
| Skip Navigation Links | Specification | -- |
| Spacer | Specification | -- |
| Tables | Specification | Example | 9 |
| Tabs | Specification | Example not yet available | 5.2 |
| Tree | Specification | Example | 5.4 |
| Version Page | Specification | Example not yet available | 18 |
| Wizard | Specification | Example not yet available | 13 |
The sample HTML also contains hooks and examples for meeting the section 508 accessibility guidelines, which are discussed in Appendix D, Accessibility Requirements of the guidelines. Additions in this version of the sample HTML include examples for skipping repetitive navigation links, associating labels with form elements, properly marking up data tables for accessibility, and use of alt and title attributes to mark up contents for users of assistive technologies.
Section 508 compliance is not guaranteed or complete, however, as there is still some ambiguity surrounding the best way to handle some of the scripting and other aspects of the HTML. This ambiguity is only going to be resolved via testing with assistive technologies.
| Privacy Policy | Terms of Use | Trademarks | Copyright 2007 Sun Microsystems, Inc. |


FICTION BOOKS
To put yourself in the shoes of others and grow your capacity for empathy, you can hardly do better than reading fiction. Multiple studies have shown that imagining stories helps activate the regions of your brain responsible for better understanding others and seeing the world from a new perspective.
Fiction has the ability to help a person understand another person in a way that even television cannot. Fiction readers not only experience the protagonist's point of view, but his innermost thoughts.
Fiction also allows readers to experience new settings. Not just sights and sounds, but smells, tastes, and touches.
|


Full name:
org.liquibase:liquibase-maven-plugin:3.8.3:diff
Description:
Generates a diff between the specified database and the reference database. The output is either a report or a changelog depending on the value of the diffChangeLogFile parameter.
Attributes:
| Name | Type | Since | Description |
|---|---|---|---|
| liquibase.changeLogDirectory | String | - | Specifies the change log directory into which liquibase can find
the change log file. |
| liquibase.changeLogFile | String | - | Specifies the change log file to use for Liquibase. |
| liquibase.changelogCatalogName | String | - | Schema against which Liquibase changelog tables will be created. |
| liquibase.changelogSchemaName | String | - | Schema against which Liquibase changelog tables will be created. |
| liquibase.clearCheckSums | boolean | - | Flag for forcing the checksums to be cleared from the
DatabaseChangeLog table. Default value is: false. |
| liquibase.contexts | String | - | The Liquibase contexts to execute, which can be "," separated if
multiple contexts are required. If no context is specified then ALL
contexts will be executed. |
| liquibase.databaseChangeLogLockTableName | String | - | Table name to use for the databasechangelog. |
| liquibase.databaseChangeLogTableName | String | - | Table name to use for the databasechangelog. |
| liquibase.databaseClass | String | - | The class to use as the database object. |
| liquibase.defaultCatalogName | String | - | The default catalog name to use the for database connection. |
| liquibase.defaultSchemaName | String | - | The default schema name to use the for database connection. |
| liquibase.diffChangeLogFile | String | - | If this parameter is set, the changelog needed to "fix" differences
between the two databases is output. If the file exists, it is
appended to. If this is null, a comparison report is output to
stdout. |
| liquibase.diffExcludeObjects | String | - | Objects to be excluded from the changelog. Example filters:
"table_name", "table:main_.*", "column:*._lock, table:primary.*". |
| liquibase.diffIncludeCatalog | boolean | - | Include the catalog in the diff output? If this is null then the
catalog will not be included |
| liquibase.diffIncludeObjects | String | - | Objects to be included in the changelog. Example filters:
"table_name", "table:main_.*", "column:*._lock, table:primary.*". |
| liquibase.diffIncludeSchema | boolean | - | Include the schema in the diff output? If this is null then the
schema will not be included |
| liquibase.diffIncludeTablespace | boolean | - | Include the tablespace in the diff output? If this is null then the
tablespace will not be included |
| liquibase.diffTypes | String | - | List of diff types to include in Change Log expressed as a comma
separated list from: tables, views, columns, indexes, foreignkeys,
primarykeys, uniqueconstraints, data. If this is null then the
default types will be: tables, views, columns, indexes,
foreignkeys, primarykeys, uniqueconstraints |
| liquibase.driver | String | - | The fully qualified name of the driver class to use to connect to
the database. |
| driverPropertiesFile | File | - | Location of a properties file containing JDBC connection properties
for use by the driver. |
| liquibase.emptyPassword | boolean | - | Deprecated. Use an empty or null value for the password instead. Default value is: false. |
| expressionVariables | Map | - | Array to put a expression variable to maven plugin. |
| expressionVars | Properties | - | Array to put a expression variable to maven plugin. |
| liquibase.includeArtifact | boolean | - | Allows for the maven project artifact to be included in the class
loader for obtaining the Liquibase property and DatabaseChangeLog
files. Default value is: true. |
| liquibase.includeTestOutputDirectory | boolean | - | Allows for the maven test output directory to be included in the
class loader for obtaining the Liquibase property and
DatabaseChangeLog files. Default value is: true. |
| liquibase.labels | String | - | The Liquibase labels to execute, which can be "," separated if
multiple labels are required or a more complex expression. If no
label is specified then ALL all will be executed. |
| liquibase.liquibaseProLicenseKey | String | - | Liquibase PRO license key value |
| liquibase.logging | String | - | Controls the level of logging from Liquibase when executing. The
value can be "debug", "info", "warning", "severe", or "off". The
value is case insensitive. Default value is: INFO. |
| liquibase.outputDefaultCatalog | boolean | - | Whether to ignore the catalog/database name. |
| liquibase.outputDefaultSchema | boolean | - | Whether to ignore the schema name. |
| liquibase.outputFileEncoding | String | - | Flag to set the character encoding of the output file produced by
Liquibase during the updateSQL phase. |
| liquibase.password | String | - | The database password to use to connect to the specified database. |
| liquibase.promptOnNonLocalDatabase | boolean | - | Controls the prompting of users as to whether or not they really
want to run the changes on a database that is not local to the
machine that the user is current executing the plugin on. Default value is: true. |
| liquibase.propertyFile | String | - | The Liquibase properties file used to configure the Liquibase
Liquibase. |
| liquibase.propertyFileWillOverride | boolean | - | Flag allowing for the Liquibase properties file to override any
settings provided in the Maven plugin configuration. By default if
a property is explicity specified it is not overridden if it also
appears in the properties file. Default value is: false. |
| liquibase.propertyProviderClass | String | - | The class to use as the property provider (must be a
java.util.Properties implementation). |
| liquibase.referenceDefaultCatalogName | String | - | The reference database catalog. |
| liquibase.referenceDefaultSchemaName | String | - | The reference database schema. |
| liquibase.referenceDriver | String | - | The fully qualified name of the driver class to use to connect to
the reference database. If this is not specified, then the
driver will be used instead. |
| liquibase.referencePassword | String | - | The reference database password to use to connect to the specified
database. If this is null then an empty password will be used. |
| liquibase.referenceServer | String | - | The server id in settings.xml to use when authenticating with. |
| liquibase.referenceUrl | String | - | The reference database URL to connect to for executing Liquibase. |
| liquibase.referenceUsername | String | - | The reference database username to use to connect to the specified
database. |
| liquibase.server | String | - | The server id in settings.xml to use when authenticating with. |
| liquibase.skip | boolean | - | Set this to 'true' to skip running liquibase. Its use is NOT
RECOMMENDED, but quite convenient on occasion. Default value is: false. |
| systemProperties | Properties | - | List of system properties to pass to the database. |
| liquibase.url | String | - | The Database URL to connect to for executing Liquibase. |
| liquibase.username | String | - | The database username to use to connect to the specified database. |
| liquibase.verbose | boolean | - | Controls the verbosity of the output from invoking the plugin. Default value is: false. |
liquibase.changelogCatalogName:
liquibase.changelogSchemaName:
liquibase.databaseChangeLogLockTableName:
liquibase.databaseChangeLogTableName:
liquibase.diffIncludeTablespace:
liquibase.includeTestOutputDirectory:
liquibase.liquibaseProLicenseKey:
liquibase.outputDefaultCatalog:
liquibase.outputDefaultSchema:
liquibase.promptOnNonLocalDatabase:
liquibase.propertyFileWillOverride:
liquibase.propertyProviderClass:
liquibase.referenceDefaultCatalogName:
liquibase.referenceDefaultSchemaName:


class RestApiClient
<init> |
RestApiClient(delegate: HttpClient) |
underlyingClient |
val underlyingClient: HttpClient |
get |
suspend fun <T> get(block: HttpRequestBuilder.() -> Unit = {}): T |
post |
suspend fun <T> post(block: HttpRequestBuilder.() -> Unit = {}): T |


Y.PaginatorView Class
Uses a data attribute to track link / image / button / etc... actions ... e.g. data-pglink="first | 7 | next"
Item Index
Methods
Properties
Attributes
Methods
_clickChangePage
-
e
Handler responding to a Page Selector "click" event. The clicked Node is reviewed for its data-pglink="" setting, and processed from that.
Changed page is then sent back to the Model, which reprocesses the paginator settings (i.e. indices) and fires a pageChange event.
Listener set in _bindUI
Parameters:
-
e
Object
_disablePageSelector
-
linkSel
-
visible
Method that toggles the visibility of Page Link selector fields based upon their data-pglink attribute setting.
Called by _processPageChange
Parameters:
-
linkSel
Object -
visible
Object
_inputChangePage
-
e
Handler responding to INPUT[text] box page change.
Listener set in _bindUI
Parameters:
-
e
Object
_modelPageChange
-
e
Handler responds to Model's "pageChange" event
Listener set in _bindUI
Parameters:
-
e
Object
_modelStateChange
-
e
Handler responds to Model's "itemsPerPageChange" event
Listener set in _bindUI
Parameters:
-
e
Object
_myClassName
()
String
private
Helper function, because I was too lazy to figure out how to get widget getClassName working
Returns:
_processPageChange
-
cpage
Main handler that accomodates Page changes, updates visual cues for highlighting the selected page link and the active Page selector link list.
This method also fires the View's "pageChange" event.
Parameters:
-
cpage
Object
_resizePaginator
()
private
Method to sync the container for the paginator View with the underlying DataTable 'table' element.
Unfortunately, there isn't a distinct, definitive 'render' complete event due to DT's complex rendering, so I use a timer function to attempt a resize.
_selectChangeRowOptions
-
e
Handler that responds to SELECT changes for no. of rows per page
Listener set in _bindUI
Parameters:
-
e
Object
_syncPaginatorSize
()
private
Method to adjust the CSS width of the paginator container and set it to the width of the underlying DT.
Returns:
_updateRPPSelect
()
private
Method fired after the Paginator View is rendered, so that the SELECT[rowsPerPage] control can be updated
Listener set in _bindUI
destructor
()
Default destructor method, cleans up the listeners that were created and cleans up the view contents.
initializer
()
Initializer setups up classes and the initial container and HTML templating for this View.
Returns:
render
()
private
Render the current settings of the Paginator, using the supplied HTML content from the original "srcNode", using Y.Lang.sub for replacement of Model attributes.
Properties
... (various)
Unknown
Various "templates" for default rendering of this View.
_pagHTML
Unknown
private
Placeholder property to store the initial container HTML for used later in the render method. This property is populated by the View initializer.
_subscr
Unknown
private
Holder for Event subscribers created by this View, saved so they can be cleaned up later.


My Team
Flood Software Solutions
Manager
1
Email:
<EMAIL_ADDRESS>303-333-3333
,
Logan Flood
Manager
2
Email:
<EMAIL_ADDRESS>303-444-4444
Camryn Flood
Engineer
3
Email:
<EMAIL_ADDRESS>camflood
Shadow Flood
Intern
6
Email:
<EMAIL_ADDRESS>Meow University


@(filename:String)(objId:Long)(thicknessForm:Form[thickness])
@base(title="Your OBJ model"){
You are about to convert the following OBJ file:
@filename
Please click the following button to start converting
@helper.form(action = routes.Application.convertObjFile(objId),'id -> "thicknessForm") {
@helper.inputText(thicknessForm("layerthickness"),
'_label -> "Thickness of Layer",
'placeholder -> "0.5",
'class -> "form-control")
@helper.inputText(thicknessForm("minthickness"),
'_label -> "Minimum Thickness Required By Printer",
'placeholder -> "0.1",
'class -> "form-control")
}


This is my new page!
One row table
New tab
First item
Second item
Third item
Some quick example text to build on the card title and make up the bulk of the card's content.
Let's StartSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhereSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhere


This page contains detailed API reference documentation. It's intended to be a resource for people who already have some familiarity with Svelte.
If that's not you (yet), you may prefer to visit the or the before consulting this reference.

Hello {name}!

Components are the building blocks of Svelte applications. They are written into .svelte files, using a superset of HTML.
All three sections — script, styles and markup — are optional.

Tags
A lowercase tag, like
, denotes a regular HTML element. A capitalised tag, such as <Widget> or <Namespace.Widget>, indicates a component.
Tags
Attributes and props
Text expressions
Comments
{#if ...}
RUN TIME
svelte
onMount
beforeUpdate
afterUpdate
onDestroy
COMPILE TIME
svelte.compile
svelte.parse
svelte.preprocess
svelte.walk
svelte.VERSION


@layout("/common/_container.html"){ Dashboard Home Dashboard 6.674 Download 7.538 Purchased 4.362 Order 1.426 Stock 电站快捷选择 @}


Gecode license and authors
GECODE LICENSE AGREEMENT
This software and its documentation are copyrighted by the individual authors as listed in each file. The following terms apply to all files associated with the software unless explicitly disclaimed in individual files.The authors hereby grant permission to use, copy, modify, distribute, and license this software and its documentation for any purpose, provided that existing copyright notices are retained in all copies and that this notice is included verbatim in any distributions. No written agreement, license, or royalty fee is required for any of the authorized uses. Modifications to this software may be copyrighted by their authors and need not follow the licensing terms described here, provided that the new terms are clearly indicated on the first page of each file where they apply.
IN NO EVENT SHALL THE AUTHORS OR DISTRIBUTORS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS SOFTWARE, ITS DOCUMENTATION, OR ANY DERIVATIVES THEREOF, EVEN IF THE AUTHORS HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
THE AUTHORS AND DISTRIBUTORS SPECIFICALLY DISCLAIM ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT.
Gecode authors
The following authors have contributed to the Gecode library and its documentation (in alphabetical order of firstname):- Christian Schulte<EMAIL_ADDRESS>- Gabor Szokoli<EMAIL_ADDRESS>- Guido Tack<EMAIL_ADDRESS>- Mikael Lagerkvist<EMAIL_ADDRESS>- Patrick Pekczynski<EMAIL_ADDRESS>The above authors are grateful to the following people who have provided bugfixes:
- Alexander Samoilov
- Grégoire Dooms
- Olof Sivertsson
We are also grateful for our users reporting bugs and suggesting improvements.


| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| IRestApiService |
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|
| 类和说明 |
|---|
| RestletRoutable
Register a set of REST resources with the central controller
|


My Favorite Things
Enter your favorite things and press submit to list them below.
Thanks for submitting! Here are your favorite things, listed below:
- Favorite color: (none)
- Favorite animal: (none)
- Favorite band: (none)
- Favorite food: (none)
- Favorite phone: (none)
- Favorite hobby: (none)
- Favorite person: (none)


It was a Wednesday night and I was extremely bored after guitar lesson and school. I decided to go through the medicine cabinet to look for codeine but instead found Amitriptyline. I looked to make sure the coast was clear and I took 2 25 mg tablets. I went into my room and crushed them up because I planned to snort them but had a lot of trouble breaking them. It took about 10 minutes but they were both powder now.
I planned to snort them near bedtime cause I had realized they would make me sleepy. I took a shower (its now 8:45) and got ready for bed. Now I arranged the powder in two lines. I snorted one with one nostril and it burned the worst iv ever had a pill burn before. God it sucked. I decided to snort the other one still with my other nostril and it once again burned. I made sure it was all in my nose and went back in my room and turned on techno.
The drip didnít taste very bad if I can remember right but my throat became very sore. My nose still kind of burned but it was also warm and numb when I breathed through it so I enjoyed that. I was becoming very drowsy now (8:55-9:00) and when I would walk around I would completely forget I was walking.  I then realized it was on the 3rd song and each song is about 5 minutes. I had thought about a minute had passed.
I was even more tired now and I decided to close my eyes and listen to the music. Before I knew it the cd was over in what seemed like about 5 minutes. The cd is 50 minutes long. In this time I think I drifted in and out of consciousness and I remember being very deep in thought. I then immediately fell asleep when I realized the cd was over.
The next day I woke up late for school and was very tired. My nose was stuffed up, my throat hurt, and my whole body felt fatigues like I had the flu. this lasted most of the day.
Overall it was an ok experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


참고
Click here to download the full example code
Introduction || Tensors || Autograd || Building Models || TensorBoard Support || Training Models || Model Understanding
Training with PyTorch¶
Follow along with the video below or on youtube.
Introduction¶
In past videos, we’ve discussed and demonstrated:
Building models with the neural network layers and functions of the torch.nn module
The mechanics of automated gradient computation, which is central to gradient-based model training
Using TensorBoard to visualize training progress and other activities
In this video, we’ll be adding some new tools to your inventory:
We’ll get familiar with the dataset and dataloader abstractions, and how they ease the process of feeding data to your model during a training loop
We’ll discuss specific loss functions and when to use them
We’ll look at PyTorch optimizers, which implement algorithms to adjust model weights based on the outcome of a loss function
Finally, we’ll pull all of these together and see a full PyTorch training loop in action.
Dataset and DataLoader¶
The Dataset
and DataLoader
classes encapsulate the process of
pulling your data from storage and exposing it to your training loop in
batches.
The Dataset
is responsible for accessing and processing single
instances of data.
The DataLoader
pulls instances of data from the Dataset
(either
automatically or with a sampler that you define), collects them in
batches, and returns them for consumption by your training loop. The
DataLoader
works with all kinds of datasets, regardless of the type
of data they contain.
For this tutorial, we’ll be using the Fashion-MNIST dataset provided by
TorchVision. We use torchvision.transforms.Normalize()
to
zero-center and normalize the distribution of the image tile content,
and download both training and validation data splits.
import torch
import torchvision
import torchvision.transforms as transforms
# PyTorch TensorBoard support
from torch.utils.tensorboard import SummaryWriter
from datetime import datetime
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
# Create datasets for training & validation, download if necessary
training_set = torchvision.datasets.FashionMNIST('./data', train=True, transform=transform, download=True)
validation_set = torchvision.datasets.FashionMNIST('./data', train=False, transform=transform, download=True)
# Create data loaders for our datasets; shuffle for training, not for validation
training_loader = torch.utils.data.DataLoader(training_set, batch_size=4, shuffle=True)
validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=4, shuffle=False)
# Class labels
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')
# Report split sizes
print('Training set has {} instances'.format(len(training_set)))
print('Validation set has {} instances'.format(len(validation_set)))
Training set has 60000 instances
Validation set has 10000 instances
As always, let’s visualize the data as a sanity check:
import matplotlib.pyplot as plt
import numpy as np
# Helper function for inline image display
def matplotlib_imshow(img, one_channel=False):
if one_channel:
img = img.mean(dim=0)
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
if one_channel:
plt.imshow(npimg, cmap="Greys")
else:
plt.imshow(np.transpose(npimg, (1, 2, 0)))
dataiter = iter(training_loader)
images, labels = next(dataiter)
# Create a grid from the images and show them
img_grid = torchvision.utils.make_grid(images)
matplotlib_imshow(img_grid, one_channel=True)
print(' '.join(classes[labels[j]] for j in range(4)))
Sandal Sneaker Ankle Boot Coat
The Model¶
The model we’ll use in this example is a variant of LeNet-5 - it should be familiar if you’ve watched the previous videos in this series.
import torch.nn as nn
import torch.nn.functional as F
# PyTorch models inherit from torch.nn.Module
class GarmentClassifier(nn.Module):
def __init__(self):
super(GarmentClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
model = GarmentClassifier()
Loss Function¶
For this example, we’ll be using a cross-entropy loss. For demonstration purposes, we’ll create batches of dummy output and label values, run them through the loss function, and examine the result.
loss_fn = torch.nn.CrossEntropyLoss()
# NB: Loss functions expect data in batches, so we're creating batches of 4
# Represents the model's confidence in each of the 10 classes for a given input
dummy_outputs = torch.rand(4, 10)
# Represents the correct class among the 10 being tested
dummy_labels = torch.tensor([1, 5, 3, 7])
print(dummy_outputs)
print(dummy_labels)
loss = loss_fn(dummy_outputs, dummy_labels)
print('Total loss for this batch: {}'.format(loss.item()))
tensor([[0.3557, 0.8645, 0.5204, 0.7741, 0.1139, 0.4145, 0.7348, 0.6162, 0.7117,
0.4355],
[0.1968, 0.2940, 0.9526, 0.3006, 0.2524, 0.0563, 0.8280, 0.6981, 0.1534,
0.7943],
[0.8296, 0.4251, 0.3695, 0.4926, 0.9087, 0.2559, 0.7270, 0.5436, 0.4969,
0.0528],
[0.2731, 0.1459, 0.0121, 0.0764, 0.9622, 0.0705, 0.4019, 0.9218, 0.3255,
0.4819]])
tensor([1, 5, 3, 7])
Total loss for this batch: 2.2294564247131348
Optimizer¶
For this example, we’ll be using simple stochastic gradient descent with momentum.
It can be instructive to try some variations on this optimization scheme:
Learning rate determines the size of the steps the optimizer takes. What does a different learning rate do to the your training results, in terms of accuracy and convergence time?
Momentum nudges the optimizer in the direction of strongest gradient over multiple steps. What does changing this value do to your results?
Try some different optimization algorithms, such as averaged SGD, Adagrad, or Adam. How do your results differ?
# Optimizers specified in the torch.optim package
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
The Training Loop¶
Below, we have a function that performs one training epoch. It enumerates data from the DataLoader, and on each pass of the loop does the following:
Gets a batch of training data from the DataLoader
Zeros the optimizer’s gradients
Performs an inference - that is, gets predictions from the model for an input batch
Calculates the loss for that set of predictions vs. the labels on the dataset
Calculates the backward gradients over the learning weights
Tells the optimizer to perform one learning step - that is, adjust the model’s learning weights based on the observed gradients for this batch, according to the optimization algorithm we chose
It reports on the loss for every 1000 batches.
Finally, it reports the average per-batch loss for the last 1000 batches, for comparison with a validation run
def train_one_epoch(epoch_index, tb_writer):
running_loss = 0.
last_loss = 0.
# Here, we use enumerate(training_loader) instead of
# iter(training_loader) so that we can track the batch
# index and do some intra-epoch reporting
for i, data in enumerate(training_loader):
# Every data instance is an input + label pair
inputs, labels = data
# Zero your gradients for every batch!
optimizer.zero_grad()
# Make predictions for this batch
outputs = model(inputs)
# Compute the loss and its gradients
loss = loss_fn(outputs, labels)
loss.backward()
# Adjust learning weights
optimizer.step()
# Gather data and report
running_loss += loss.item()
if i % 1000 == 999:
last_loss = running_loss / 1000 # loss per batch
print(' batch {} loss: {}'.format(i + 1, last_loss))
tb_x = epoch_index * len(training_loader) + i + 1
tb_writer.add_scalar('Loss/train', last_loss, tb_x)
running_loss = 0.
return last_loss
Per-Epoch Activity¶
There are a couple of things we’ll want to do once per epoch:
Perform validation by checking our relative loss on a set of data that was not used for training, and report this
Save a copy of the model
Here, we’ll do our reporting in TensorBoard. This will require going to the command line to start TensorBoard, and opening it in another browser tab.
# Initializing in a separate cell so we can easily add more epochs to the same run
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
writer = SummaryWriter('runs/fashion_trainer_{}'.format(timestamp))
epoch_number = 0
EPOCHS = 5
best_vloss = 1_000_000.
for epoch in range(EPOCHS):
print('EPOCH {}:'.format(epoch_number + 1))
# Make sure gradient tracking is on, and do a pass over the data
model.train(True)
avg_loss = train_one_epoch(epoch_number, writer)
# We don't need gradients on to do reporting
model.train(False)
running_vloss = 0.0
for i, vdata in enumerate(validation_loader):
vinputs, vlabels = vdata
voutputs = model(vinputs)
vloss = loss_fn(voutputs, vlabels)
running_vloss += vloss
avg_vloss = running_vloss / (i + 1)
print('LOSS train {} valid {}'.format(avg_loss, avg_vloss))
# Log the running loss averaged per batch
# for both training and validation
writer.add_scalars('Training vs. Validation Loss',
{ 'Training' : avg_loss, 'Validation' : avg_vloss },
epoch_number + 1)
writer.flush()
# Track best performance, and save the model's state
if avg_vloss < best_vloss:
best_vloss = avg_vloss
model_path = 'model_{}_{}'.format(timestamp, epoch_number)
torch.save(model.state_dict(), model_path)
epoch_number += 1
EPOCH 1:
batch 1000 loss: 1.7202785758674144
batch 2000 loss: 0.8457988122999668
batch 3000 loss: 0.7344358410686255
batch 4000 loss: 0.6348215425089002
batch 5000 loss: 0.6132729426613077
batch 6000 loss: 0.5689538897902239
batch 7000 loss: 0.535509346962208
batch 8000 loss: 0.5065724759609439
batch 9000 loss: 0.49358063461841084
batch 10000 loss: 0.5068647645610618
batch 11000 loss: 0.4623187284328742
batch 12000 loss: 0.4442366441761842
batch 13000 loss: 0.4400626162974513
batch 14000 loss: 0.45063220444787294
batch 15000 loss: 0.41373204464805896
LOSS train 0.41373204464805896 valid 0.4639061391353607
EPOCH 2:
batch 1000 loss: 0.4049252691374859
batch 2000 loss: 0.4027956905339379
batch 3000 loss: 0.3870164828733832
batch 4000 loss: 0.3765921371775912
batch 5000 loss: 0.3810157497630571
batch 6000 loss: 0.38515496355987855
batch 7000 loss: 0.3881014699799125
batch 8000 loss: 0.38654046844306866
batch 9000 loss: 0.35264253223035485
batch 10000 loss: 0.35956855410384014
batch 11000 loss: 0.3746511521441862
batch 12000 loss: 0.35857216321751184
batch 13000 loss: 0.3264845824319054
batch 14000 loss: 0.33541295046557207
batch 15000 loss: 0.3707398298132175
LOSS train 0.3707398298132175 valid 0.3628268837928772
EPOCH 3:
batch 1000 loss: 0.33687457587916286
batch 2000 loss: 0.33213954120439304
batch 3000 loss: 0.3441374519288947
batch 4000 loss: 0.33658539128087434
batch 5000 loss: 0.31185333648807134
batch 6000 loss: 0.3150397274837887
batch 7000 loss: 0.353223590657406
batch 8000 loss: 0.3398171684925983
batch 9000 loss: 0.32288358722522387
batch 10000 loss: 0.3145468523482559
batch 11000 loss: 0.3245599505953287
batch 12000 loss: 0.321184238298873
batch 13000 loss: 0.31208387652691455
batch 14000 loss: 0.30990570707632287
batch 15000 loss: 0.333060434007697
LOSS train 0.333060434007697 valid 0.3348868787288666
EPOCH 4:
batch 1000 loss: 0.302605270275053
batch 2000 loss: 0.30221588003780564
batch 3000 loss: 0.31341029232504664
batch 4000 loss: 0.30225226803854455
batch 5000 loss: 0.2959274389560742
batch 6000 loss: 0.3017923433062388
batch 7000 loss: 0.3103919526273894
batch 8000 loss: 0.3142617614178744
batch 9000 loss: 0.2908107597097842
batch 10000 loss: 0.30662362715498237
batch 11000 loss: 0.2936881104596905
batch 12000 loss: 0.29864081332642306
batch 13000 loss: 0.2882055617513561
batch 14000 loss: 0.2811639510149398
batch 15000 loss: 0.29043961816684893
LOSS train 0.29043961816684893 valid 0.31826725602149963
EPOCH 5:
batch 1000 loss: 0.2909442294574983
batch 2000 loss: 0.27795928239824935
batch 3000 loss: 0.28197013997076553
batch 4000 loss: 0.27602926642545994
batch 5000 loss: 0.27291372970746125
batch 6000 loss: 0.26500764907761276
batch 7000 loss: 0.2763223950124077
batch 8000 loss: 0.28239086670237523
batch 9000 loss: 0.2731176992859073
batch 10000 loss: 0.2828177599106548
batch 11000 loss: 0.28514709650713665
batch 12000 loss: 0.27786594535020415
batch 13000 loss: 0.27871137002642354
batch 14000 loss: 0.29793933952681256
batch 15000 loss: 0.28550324632457885
LOSS train 0.28550324632457885 valid 0.3166631758213043
To load a saved version of the model:
saved_model = GarmentClassifier()
saved_model.load_state_dict(torch.load(PATH))
Once you’ve loaded the model, it’s ready for whatever you need it for - more training, inference, or analysis.
Note that if your model has constructor parameters that affect model structure, you’ll need to provide them and configure the model identically to the state in which it was saved.
Other Resources¶
Docs on the data utilities, including Dataset and DataLoader, at pytorch.org
A note on the use of pinned memory for GPU training
Documentation on the datasets available in TorchVision, TorchText, and TorchAudio
Documentation on the loss functions available in PyTorch
Documentation on the torch.optim package, which includes optimizers and related tools, such as learning rate scheduling
A detailed tutorial on saving and loading models
The Tutorials section of pytorch.org contains tutorials on a broad variety of training tasks, including classification in different domains, generative adversarial networks, reinforcement learning, and more
Total running time of the script: ( 5 minutes 4.804 seconds)


Upload a photo:
File uploaded at: {{ uploaded_file_url }}
{% endif %}Upload a photo:
File uploaded at: {{ uploaded_file_url }}
{% endif %}Photos:
{% if images%} {% for i in images %} Title: {{ i.title}} {% if not user.is_authenticated %} Author: {{i.author }} {% endif %} {% endfor %} {%else%}You have no photos. If you would like to upload some, please go to Add Photo
{%endif%}


The tabs module contains components to render a tabset and includes options to display buttons in the tab area and to make tabs closeable.
The index of the active tab.
Fires when the active tab changes. Emits the index of the active tab.
Fires when the user clicks the "new tab" button.
Fires when the user clicks the "open tab" button.
The text of the button that selects the tab.
Displays a counter alongside the tab header.
The index of the tab. If not defined, it will be set to the position of the tab on load.
Determines whether the tab is disabled.
Specifying a listener for this event adds a close button to the tab. The event fires when the user clicks the tab's close button.


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