|
CookMem
A C++ memory context / allocator
|
#include <cookmemcontext.h>
Public Types | |
| typedef MemContext< Arena, Logger, T > | MemCtx |
| typedef MemPool< Arena, Logger, T > | Pool |
| typedef Pool::size_type | size_type |
| typedef Pool::difference_type | difference_type |
| typedef Pool::value_type | value_type |
| typedef Pool::pointer | pointer |
| typedef Pool::const_pointer | const_pointer |
Public Member Functions | |
| MemContext (Arena &arena, Logger &logger, bool padding=false) | |
| MemContext (MemCtx &other) | |
| T * | allocate (std::size_t size) |
| T * | reallocate (T *ptr, std::size_t size) |
| T * | callocate (std::size_t num, std::size_t size) |
| void | deallocate (T *ptr, std::size_t size=0) |
| bool | contains (T *ptr, bool checkUsed=false) |
| size_type | getSize (T *ptr) |
| void | releaseAll () |
| std::size_t | getFootPrintLimit () const |
| void | setFootprintLimit (std::size_t footprintLimit) |
| std::size_t | getFootprint () const |
| std::size_t | getMaxFootprint () const |
| bool | isStoringExactSize () const |
| void | setStoringExactSize (bool b) |
| bool | isPadding () const |
| std::uint8_t | getPaddingByte () const |
| void | setPaddingByte (std::uint8_t paddingByte) |
| Arena & | getArena () |
| Logger & | getLogger () |
| Pool & | getPool () |
A basic memory context that creates a MemPool based on the arena and logger references user passed in.
This class is mostly used in cases when arena or logger that do not have default constructors.
| typedef Pool::const_pointer cookmem::MemContext< Arena, Logger, T >::const_pointer |
const pointer type
| typedef Pool::difference_type cookmem::MemContext< Arena, Logger, T >::difference_type |
pointer difference type
| typedef Pool::pointer cookmem::MemContext< Arena, Logger, T >::pointer |
pointer type
| typedef Pool::size_type cookmem::MemContext< Arena, Logger, T >::size_type |
size type
| typedef Pool::value_type cookmem::MemContext< Arena, Logger, T >::value_type |
value type
|
inline |
Allocate memory from the memory context.
| size | memory request size. |
|
inline |
Allocate n items of certain size. The memory allocated is zeroed.
| num | number of elements |
| size | element size |
|
inline |
Check if a pointer is within the address space of segments owned by this MemPool.
| ptr | memory pointer |
| checkUsed | check the pointer if it is used. |
|
inline |
Free a piece of memory previously allocated by this memory pool.
| ptr | a piece of memory to be freed. |
| size | mostly ignored. It is only used by the memory logger. |
|
inline |
Get the underlying memory arena.
|
inline |
Get the current memory footprint.
|
inline |
Get the memory footprint limit.
|
inline |
Get the underlying logger.
|
inline |
Get the maximum memory footprint.
|
inline |
Get the padding byte.
|
inline |
Get the underlying memory pool.
|
inline |
Gets the allocated size for a pointer.
If we are storing the exact size of the pointer, then the result can match the exact size of user's allocation.
Otherwise, the result is the aligned size.
| ptr | A pointer. |
|
inline |
Check whether or not we are padding bytes at the end of the allocated memory.
|
inline |
Check whether or not we are storing the exact user size in the memory.
|
inline |
This function reallocate a memory.
| ptr | the current memory pointer. |
| size | the new size |
|
inline |
Release all the memory segments held by this MemPool.
|
inline |
Set the maximum memory footprint limit.
| footprintLimit |
|
inline |
Set the padding byte.
| paddingByte | The padding byte. |
|
inline |
Set whether or not we should be storing the exact user size in memory.
| b | the boolean choice |


--- layout: post esbma_id: 1731 title: From C# to Java: Part 4 date: 2007-11-28 17:58:33 ---
As a member of Microsoft's VSIP program, we have been creating source control plugins for the Visual Studio line of products for eight years. As I started my recent foray into the Eclipse world, I was eager to explore the area of plugins over on this side of the fence. So far, I'm impressed.
The first plugin I installed was our own. SourceGear Fortress includes an Eclipse plugin, but I had never even tried it.
My first reaction is that I really like the way Eclipse handles installation of plugins. The whole process is managed from within Eclipse itself. Under the Help menu is a submenu called Software Updates. All I have to do is provide the URL of our Eclipse update site:
http://download.sourcegear.com/Fortress/latest/update
The rest of the job is very simple, essentially automatic.
Once installed, I have several additional views:
And some new stuff under the Team menu:
And some new items under Preferences:
All in all, I have found using source control under Eclipse to be very pleasant and straightforward. If this seems like I am bragging about my own product, I suppose it is, except for two mitigating factors:
Truth be told, I did find [what I think is] a bug in our Eclipse plugin. Fortunately, because Fortress supports integrated bug-tracking, I was able to use the plugin to report the bug right from within Eclipse. :-)
In part 2 I complained about the way Java handles object comparison (== is for identity, .equals() is for state). I received quite a few comments and emails for that one. I don't mind the contrasting points of view, but I'll confess I deleted all the remarks from condescending nitwits who assumed I simply don't understand pointers. :-)
Anyway, several people suggested I try an Eclipse plugin called CheckStyle.
Once again, installation was extremely simple. After that, I immediately ran CheckStyle on my code, just to see what happens with no further configuration.
CheckStyle complained about the 301 places where I put the curly brace on the next line. :-)
Overall, CheckStyle looks very cool. I'll be experimenting with it more and configuring it to match my coding style. I don't expect I'll make the == mistake again now that I remember how Java does things, but it's nice to know that if I do, CheckStyle can pester me about it.
The VSIP people are always talking about the Visual Studio "ecosystem", the collection of organizations creating every conceivable type of plugin for Visual Studio.
The corresponding ecosystem for Eclipse appears to be enormous. When I Google for the words "eclipse plugin", the top two hits appear to be sites which are trying to help people find plugins. One of those sites is www.eclipseplugincentral.com, which seems to be saying there are 991 plugins in its directory.
I know for a fact that there are at least 992, because the SourceGear Fortress plugin isn't listed yet. :-)
Browsing the directory reveals an incredible diversity of plugins. Many of them are unsurprising, but the fringe cases suggest just how large the surface area of this ecosystem really is:
In fact, as I looked through this list of plugins, I started to realize that Eclipse is basically a modern Emacs. The true die-hard Emacs users want to do everything within Emacs. One of my coworkers likes to edit /etc/passwd and set emacs to be his shell. Eclipse seems to be heading in the same direction.
I've tried 2 Eclipse plugins so far -- 990 to go. This should be fun.


RO purifier ban: Water Quality Association meets Environment Ministry
This move was taken after the Supreme Court had refused to offer a stay on a National Green Tribunal ban on RO purifiers in certain areas
by M SaraswathyThe Water Quality India Association met officials of the Ministry of Environment to discuss the issue of the reverse osmosis (RO) purifiers where total dissolved solids (TDS) in water is less than 500 miligrams per litre.
"We have spoken to the ministry officials and explained to them that RO purifiers are the only way of purifying water for medicines and to use in procedures like dialysis of kidneys," said an official.
This move was taken after the Supreme Court had refused to offer a stay on a National Green Tribunal ban on RO purifiers in certain areas. However, SC has given the industry 10 days to meet MoEF to seek clarity on this matter.
The Water Quality India Association is an industry body of RO purifier manufacturers.
The case
in May, the National Green Tribunal (NGT) asked MoEF to frame rules for the use of RO filters and also banned the use of RO purifiers in locations where TDS was low.
According to NGT, RO purifiers lead to the wastage of almost 70-80 percent water during the purification process. It asked the RO manufacturers to ensure that they are able to recover about 75 percent of the water.
TDS refer to the salt, minerals and other solids present in water. RO purification is used to purify the water.
In early November, NGT found that its order was still not being implemented. Following this, the Water Quality India Association moved the SC to seek a stay on the RO ban. However, the apex court refused to give a stay.
NGT has given time till December 31 to MoEF to frame relevant rules.
What next?
The Water Quality India Association will present a formal written explanation of their stand on the matter.
A source said that RO manufacturers had already ensured that at least 60 percent of the water was recovered. However, he added that the demand for 75 percent water recovery would be tough to adhere to.
"We will submit this information. We are hoping that the MoEF will not go for a summary ban for RO filters," an official added.
If RO purifiers are banned, industries like pharmaceuticals and hospitals which use this technology to clean the water of solids, metals and pesticides will encounter a hurdle.
At present, RO industry consists of Rs 1,000 crore household market and about Rs 1,500 crore of commercial businesses.


Class XmlDocumentProvider
An implementation of IReadsAndWritesDocument which uses the XML/Linq API built-into .NET Framework and dotnet core. See also: System.Xml.Linq.XDocument
Implements
Inherited Members
Namespace: ZptSharp.Dom
Assembly: ZptSharp.Xml.dll
Syntax
public class XmlDocumentProvider : DocumentReaderWriterBase<XmlDocument>, IReadsAndWritesDocument
Constructors
| Improve this Doc View SourceXmlDocumentProvider(IGetsXmlReaderSettings)
Initializes a new instance of the XmlDocumentProvider class.
Declaration
public XmlDocumentProvider(IGetsXmlReaderSettings readerSettingsProvider)
Parameters
| Type | Name | Description |
|---|---|---|
| IGetsXmlReaderSettings | readerSettingsProvider | Reader settings provider. |
Properties
| Improve this Doc View SourceResolvableType
Gets the type of the current instance, for the purpose of future dependency resolution.
Declaration
public override Type ResolvableType { get; }
Property Value
| Type | Description |
|---|---|
| System.Type |
Overrides
Remarks
Gets the type of the current instance, for the purpose of dependency resolution. This is not/should not be quite the same as System.Object.GetType(), because a derived implementation might not always be resolved as its precise type, it might be resolved as a less-derived base type.
Methods
| Improve this Doc View SourceCanReadWriteForFilename(String)
Gets whether or not the current instance may be used to read & write documents which have the specified filename.
Declaration
public override bool CanReadWriteForFilename(string filenameOrPath)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | filenameOrPath | The filename of a ZPT document. |
Returns
| Type | Description |
|---|---|
| System.Boolean |
|
Overrides
GetDocumentProtectedAsync(Stream, RenderingConfig, IDocumentSourceInfo, CancellationToken)
Implement this method in a derived class to perform custom logic related to reading a document from a stream.
Derived types should assume that the stream
& config
parameters have
been null-checked. Also, they should assume that the token
parameter does not request cancellation.
Declaration
protected override Task<IDocument> GetDocumentProtectedAsync(Stream stream, RenderingConfig config, IDocumentSourceInfo sourceInfo, CancellationToken token)
Parameters
| Type | Name | Description |
|---|---|---|
| System.IO.Stream | stream | A stream containing the source of the document. |
| RenderingConfig | config | Rendering configuration. |
| IDocumentSourceInfo | sourceInfo | Information which identifies the source of the document. |
| System.Threading.CancellationToken | token | An object used to cancel the operation if required. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<IDocument> | A task which provides the document which has been read. |
Overrides
Remarks
This implementation is not truly asynchronous, as the APIs involved do not provide async functionality.
WriteDocumentProtectedAsync(XmlDocument, RenderingConfig, CancellationToken)
Implement this method in a derived class to perform custom logic related to writing a document to a stream.
Derived types should assume that the document
& config
parameters
have been null-checked. The document will also already be converted to XmlDocument.
Finally, the token
parameter will have already been checked that it does not request cancellation.
Declaration
protected override Task<Stream> WriteDocumentProtectedAsync(XmlDocument document, RenderingConfig config, CancellationToken token)
Parameters
| Type | Name | Description |
|---|---|---|
| XmlDocument | document | The document to write. |
| RenderingConfig | config | Rendering configuration. |
| System.Threading.CancellationToken | token | An object used to cancel the operation if required. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<System.IO.Stream> | A task which provides the output stream. |


Working
Not working
Here are a few image urls. Highlight this url then press the "image from selection" button http://rubyonrails.com/images/rails.png Copy this one and click the "image from prompt" button http://www.google.com/intl/en_ALL/images/logo.gif This image is here for testing purposes. You should be able to begin removing text from this without adding any first. Try to delete the image below.


About
(current)
Portfolio
(current)
Adrienne Osorio
Quarantine Recipe Machine
Link to Deployed App
Link to Github repo
JavaScript Quiz
Link to Deployed App
Link to Github repo
Nine to Five Scheduler
Link to Deployed App
Link to Github repo


Posted on January 26, 2019 at 1:31 AM
A lot of people do not compost in their home at all. Millions and millions of unused components for compost go to waste in our landfills, adding a unnecessary amount of trash.
Composting is turning food scraps and trash into usuable soil. Composting has been proven to reduce methane and other green house gases in out air. It also leaves you with nutrient rich soil to use or donate.
Now that you know the importance it is time to learn what to compost and how to compost it. You can compost often "random" things in your house. Things like egg shells, cut flowers, coffee grounds, tea, teabags, and toothpicks!
The options are vast! Here is a full list to more items.
Now there are two ways to compost. There is cold composting and hot composting. Cold composting is collecting the items listed above and letting them sit in a bin and decompose for a year. Hot composting requires four ingrediants. It uses nitrogen, carbon, air, and water. All of these feed the bacteria and speed up the process.
Now that you know how to compost, it is time to use your new skill.
By: John Wilhem
By: Jane Good
By: Aaron woolfolk


My name is Billy, I'm 24 years old french front-end developer based in Paris. I am passionate about new technologies and learning new things about any topics. I love watching and playing sports,listen to every kind of music, travelling and aesthetic things, especially clean and minimalist design.


{% extends "motion/base.html" %} {% load i18n %} {% block head_title %}{{ block.super }} - {% trans "404 File Not Found" %}{% endblock %} {% block navigation %} {% include "motion/bits/navigation.html" %} {% endblock %} {% block content_body %}
{% trans "You have reached a 404 File Not found. Please contact the server administrator to let us know a that file is missing. Thank you!" %}
{% endblock %}


SYNONYM RULE
= (undirected) |
Default configuration. Makes input term match synonym term and vice versa. |
-> (directed) |
Makes only synonym term match for input term. |
$1 placeholder |
Can be used with a directed synonym in combination with * Wildcard. See Input Matching. |
UP/DOWN RULE
Increases or decreases document ranking matching a specific term, e.g.:
UP(10): Altersteilzeit
, or DOWN(500): englische Übersetzung
.
Can be combined with native Solr field syntax. See FILTER RULE.
FILTER RULE
Filter exclusively documents matching a specific term or totally remove them completely.
When term is prefixed with *
, native Solr field syntax can be used, e.g.:
* price:[350 TO 450]
, * microline1:Vertrag
, or -vertrag
.
DELETE RULE
Delete rules allow you to remove keywords from a query.
Input Matching
"personal computer" |
Only match the exact query 'personal computer'. |
"personal computer |
Only match queries starting with 'personal computer' |
personal computer" |
Only match queries ending with 'personal computer' |
urlaubs* |
Wildcard matching of queries starting with 'urlaubs' (e.g. 'urlaubsgeld' or 'urlaubszeit').
Can be combined with $1 placeholder in a directed synonym (e.g. urlaub $1 ).
WARNING: Only works in combination with a directed (not undirected) synonym!
|
For more information refer to detailled documentation of querqy.


Section Nav
Hero
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About
Us
Introduction
WHAT WE STRIVE FOR
We understand that beer profile with different flavors, ranging from crisp, clean, bitter, druity, tart, sour or caramel, is vital. Picking a beer with a balance of everything that suits everyone's palette is what we are here for. Our flavor profile is determined by a particular recipe of carbonation, water, yeast and hops. We tweak each step in our brewing process in our own way to add our own personal touch and final notes, which makes each beer of ours unique with every sip.
About Image
About Section 1
QUALITY
The quality and freshness of our beer is vital to us as it determines the flavor profile and experience consumers have. This is why we formulated several of our products to stay fresh and further enhance the fruity flavor and finish of bittersweet notes as they remain dormant over time.
About 1 Image
About Section 2
About 2 Image PC
TASTE BUD
We consider every aspect of our drinks, ranging from the taste down to its visual profile (color) as color, aroma, carbonation and the bottle are the qualities that determine a lasting impression on every aspect of our consumer's human senses. In the end, we strive form flavor that reminds you of the refreshing crisp and clean nature of the countryside.


Problem 6. Point in Circle Write an expression that checks if given point P(x, y) is within a circle K({0,0}, 5). //{0,0} is the centre and 5 is the radius
See the result in the console -> F12 + F5


tensorlayer.files
¶
load_mnist_dataset ([shape]) |
Automatically download MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 digit images respectively. |
load_cifar10_dataset ([shape, plotable, second]) |
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. |
load_ptb_dataset () |
Penn TreeBank (PTB) dataset is used in many LANGUAGE MODELING papers, including “Empirical Evaluation and Combination of Advanced Language Modeling Techniques”, “Recurrent Neural Network Regularization”. |
load_matt_mahoney_text8_dataset () |
Download a text file from Matt Mahoney’s website if not present, and make sure it’s the right size. |
load_imbd_dataset ([path, nb_words, ...]) |
Load IMDB dataset |
load_nietzsche_dataset () |
Load Nietzsche dataset |
simple_read_words ([filename]) |
Standard way to read context from file. |
read_words (filename[, replace]) |
File to list format context. |
read_analogies_file ([eval_file, word2id]) |
Reads through an analogy question file, return its id format. |
build_vocab (data) |
Build vocabulary. |
build_reverse_dictionary (word_to_id) |
Given a dictionary for converting word to integer id. |
build_words_dataset (words[, ...]) |
Build the words dictionary and replace rare words with ‘UNK’ token. |
words_to_word_ids (data, word_to_id) |
Given a context (words) in list format and the vocabulary, Returns a list of IDs to represent the context. |
word_ids_to_words (data, id_to_word) |
Given a context (ids) in list format and the vocabulary, Returns a list of words to represent the context. |
save_vocab (count[, name]) |
Save the vocabulary to a file so the model can be reloaded. |
save_npz ([save_dict, name]) |
Input parameters and the file name, save parameters into .npz file. |
load_npz ([path, name]) |
Load the parameters of a Model saved by tl.files.save_npz(). |
assign_params (sess, params, network) |
Assign the given parameters to the TensorLayer network. |
npz_to_W_pdf ([path, regx]) |
Convert the first weight matrix of .npz file to .pdf by using tl.visualize.W(). |
save_any_to_npy ([save_dict, name]) |
Save variables to .npy file. |
save_any_to_npy ([save_dict, name]) |
Save variables to .npy file. |
load_file_list ([path, regx]) |
Return a file list in a folder by given a path and regular expression. |
Load dataset functions¶
-
tensorlayer.files.
load_mnist_dataset
(shape=(-1, 784))[source]¶ Automatically download MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 digit images respectively.
- shape : tuple
- The shape of digit images
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784))
-
tensorlayer.files.
load_cifar10_dataset
(shape=(-1, 32, 32, 3), plotable=False, second=3)[source]¶ The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
- shape : tupe
- The shape of digit images: e.g. (-1, 3, 32, 32) , (-1, 32, 32, 3) , (-1, 32*32*3)
- plotable : True, False
- Whether to plot some image examples.
- second : int
- If ‘plotable’ is True, ‘second’ is the display time.
>>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=True)
CIFAR-10 images can only be display without color change under uint8. >>> X_train = np.asarray(X_train, dtype=np.uint8) >>> plt.ion() >>> fig = plt.figure(1232) >>> count = 1 >>> for row in range(10): >>> for col in range(10): >>> a = fig.add_subplot(10, 10, count) >>> plt.imshow(X_train[count-1], interpolation=’nearest’) >>> plt.gca().xaxis.set_major_locator(plt.NullLocator()) # 不显示刻度(tick) >>> plt.gca().yaxis.set_major_locator(plt.NullLocator()) >>> count = count + 1 >>> plt.draw() >>> plt.pause(3)
-
tensorlayer.files.
load_ptb_dataset
()[source]¶ Penn TreeBank (PTB) dataset is used in many LANGUAGE MODELING papers, including “Empirical Evaluation and Combination of Advanced Language Modeling Techniques”, “Recurrent Neural Network Regularization”.
It consists of 929k training words, 73k validation words, and 82k test words. It has 10k words in its vocabulary.
In “Recurrent Neural Network Regularization”, they trained regularized LSTMs of two sizes; these are denoted the medium LSTM and large LSTM. Both LSTMs have two layers and are unrolled for 35 steps. They initialize the hidden states to zero. They then use the final hidden states of the current minibatch as the initial hidden state of the subsequent minibatch (successive minibatches sequentially traverse the training set). The size of each minibatch is 20.
The medium LSTM has 650 units per layer and its parameters are initialized uniformly in [−0.05, 0.05]. They apply 50% dropout on the non-recurrent connections. They train the LSTM for 39 epochs with a learning rate of 1, and after 6 epochs they decrease it by a factor of 1.2 after each epoch. They clip the norm of the gradients (normalized by minibatch size) at 5.
The large LSTM has 1500 units per layer and its parameters are initialized uniformly in [−0.04, 0.04]. We apply 65% dropout on the non-recurrent connections. They train the model for 55 epochs with a learning rate of 1; after 14 epochs they start to reduce the learning rate by a factor of 1.15 after each epoch. They clip the norm of the gradients (normalized by minibatch size) at 10.
tensorflow.models.rnn.ptb import reader
-
tensorlayer.files.
load_matt_mahoney_text8_dataset
()[source]¶ Download a text file from Matt Mahoney’s website if not present, and make sure it’s the right size. Extract the first file enclosed in a zip file as a list of words. This dataset can be used for Word Embedding.
- word_list : a list
- a list of string (word). e.g. [.... ‘their’, ‘families’, ‘who’, ‘were’, ‘expelled’, ‘from’, ‘jerusalem’, ...]
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> print('Data size', len(words))
-
tensorlayer.files.
load_imbd_dataset
(path='imdb.pkl', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2, index_from=3)[source]¶ Load IMDB dataset
>>> X_train, y_train, X_test, y_test = tl.files.load_imbd_dataset( ... nb_words=20000, test_split=0.2) >>> print('X_train.shape', X_train.shape) ... (20000,) [[1, 62, 74, ... 1033, 507, 27],[1, 60, 33, ... 13, 1053, 7]..] >>> print('y_train.shape', y_train.shape) ... (20000,) [1 0 0 ..., 1 0 1]
Vector representations of words¶
-
tensorlayer.files.
simple_read_words
(filename='nietzsche.txt')[source]¶ Standard way to read context from file.
-
tensorlayer.files.
read_words
(filename, replace=['\n', '<eos>'])[source]¶ - File to list format context.
Note that: this script can not handle punctuations. For customized read_words method, see
tutorial_generate_text.py
.- filename : a string
- A file path (like .txt file),
- replace : a list
- [original string, target string], to disable replace use [‘’, ‘’]
The context in a list, split by ‘ ‘ by default, and use ‘<eos>’ to represent ‘
- ‘.
e.g. [... ‘how’, ‘useful’, ‘it’, “‘s” ... ]
-
tensorlayer.files.
read_analogies_file
(eval_file='questions-words.txt', word2id={})[source]¶ Reads through an analogy question file, return its id format.
- eval_data : a string
- The file name.
- word2id : a dictionary
- Mapping words to unique IDs.
Returns: questions: a [n, 4] numpy array containing the analogy question’s
word ids. questions_skipped: questions skipped due to unknown words.>>> eval_file should be in this format : >>> : capital-common-countries >>> Athens Greece Baghdad Iraq >>> Athens Greece Bangkok Thailand >>> Athens Greece Beijing China >>> Athens Greece Berlin Germany >>> Athens Greece Bern Switzerland >>> Athens Greece Cairo Egypt >>> Athens Greece Canberra Australia >>> Athens Greece Hanoi Vietnam >>> Athens Greece Havana Cuba ...
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> data, count, dictionary, reverse_dictionary = tl.files.build_words_dataset(words, vocabulary_size, True) >>> analogy_questions = tl.files.read_analogies_file( eval_file='questions-words.txt', word2id=dictionary) >>> print(analogy_questions) ... [[ 3068 1248 7161 1581] ... [ 3068 1248 28683 5642] ... [ 3068 1248 3878 486] ... ..., ... [ 1216 4309 19982 25506] ... [ 1216 4309 3194 8650] ... [ 1216 4309 140 312]]
-
tensorlayer.files.
build_vocab
(data)[source]¶ - Build vocabulary.
- Given the context in list format Return the vocabulary, which is a dictionary for word to id. e.g. {‘campbell’: 2587, ‘atlantic’: 2247, ‘aoun’: 6746 .... }
- data : a list of string
- the context in list format
- word_to_id : a dictionary
- mapping words to unique IDs. e.g. {‘campbell’: 2587, ‘atlantic’: 2247, ‘aoun’: 6746 .... }
tensorflow.models.rnn.ptb.reader
>>> data_path = os.getcwd() + '/simple-examples/data' >>> train_path = os.path.join(data_path, "ptb.train.txt") >>> word_to_id = build_vocab(read_txt_words(train_path))
-
tensorlayer.files.
build_reverse_dictionary
(word_to_id)[source]¶ Given a dictionary for converting word to integer id. Returns a reverse dictionary for converting a id to word.
-
tensorlayer.files.
build_words_dataset
(words, vocabulary_size=50000, printable=True)[source]¶ Build the words dictionary and replace rare words with ‘UNK’ token. The most common word has the smallest integer id.
- words : a list of string or byte
- The context in list format. You may need to do preprocessing on the words, such as lower case, remove marks etc.
- vocabulary_size : an int
- The maximum vocabulary size, limiting the vocabulary size. Then the script replaces rare words with ‘UNK’ token.
- printable : boolen
- Whether to print the read vocabulary size of the given words.
- data : a list of integer
- The context in a list of ids
- count : a list of tuple and list
count[0] is a list : the number of rare words count[1:] are tuples : the number of occurrence of each word e.g. [[‘UNK’, 418391], (b’the’, 1061396), (b’of’, 593677),
(b’and’, 416629), (b’one’, 411764)]- dictionary : a dictionary
- word_to_id, mapping words to unique IDs.
- reverse_dictionary : a dictionary
- id_to_word, mapping id to unique word.
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> vocabulary_size = 50000 >>> data, count, dictionary, reverse_dictionary = ... tl.files.build_words_dataset(words, vocabulary_size)
-
tensorlayer.files.
words_to_word_ids
(data, word_to_id)[source]¶ Given a context (words) in list format and the vocabulary, Returns a list of IDs to represent the context.
- data : a list of string or byte
- the context in list format
- word_to_id : a dictionary
- mapping words to unique IDs.
A list of IDs to represent the context.
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> vocabulary_size = 50000 >>> data, count, dictionary, reverse_dictionary = ... tl.files.build_words_dataset(words, vocabulary_size, True) >>> context = [b'hello', b'how', b'are', b'you'] >>> ids = tl.files.words_to_word_ids(words, dictionary) >>> context = tl.files.word_ids_to_words(ids, reverse_dictionary) >>> print(ids) ... [6434, 311, 26, 207] >>> print(context) ... [b'hello', b'how', b'are', b'you']
-
tensorlayer.files.
word_ids_to_words
(data, id_to_word)[source]¶ Given a context (ids) in list format and the vocabulary, Returns a list of words to represent the context.
- data : a list of integer
- the context in list format
- id_to_word : a dictionary
- mapping id to unique word.
A list of string or byte to represent the context.
see words_to_word_ids
-
tensorlayer.files.
save_vocab
(count, name='vocab.txt')[source]¶ Save the vocabulary to a file so the model can be reloaded.
- count : a list of tuple and list
count[0] is a list : the number of rare words count[1:] are tuples : the number of occurrence of each word e.g. [[‘UNK’, 418391], (b’the’, 1061396), (b’of’, 593677),
(b’and’, 416629), (b’one’, 411764)]
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> vocabulary_size = 50000 >>> data, count, dictionary, reverse_dictionary = ... tl.files.build_words_dataset(words, vocabulary_size, True) >>> tl.files.save_vocab(count, name='vocab_text8.txt') >>> vocab_text8.txt ... UNK 418391 ... the 1061396 ... of 593677 ... and 416629 ... one 411764 ... in 372201 ... a 325873 ... to 316376
Load and save network¶
-
tensorlayer.files.
save_npz
(save_dict={}, name='model.npz')[source]¶ Input parameters and the file name, save parameters into .npz file. Use tl.utils.load_npz() to restore.
- save_dict : a dictionary
- Parameters want to be saved.
- name : a string or None
- The name of the .npz file.
>>> tl.files.save_npz(network.all_params, name='model_test.npz') ... File saved to: model_test.npz >>> load_params = tl.files.load_npz(name='model_test.npz') ... Loading param0, (784, 800) ... Loading param1, (800,) ... Loading param2, (800, 800) ... Loading param3, (800,) ... Loading param4, (800, 10) ... Loading param5, (10,) >>> put parameters into a TensorLayer network, please see assign_params()
-
tensorlayer.files.
load_npz
(path='', name='model.npz')[source]¶ Load the parameters of a Model saved by tl.files.save_npz().
- path : a string
- Folder path to .npz file.
- name : a string or None
- The name of the .npz file.
- params : list
- A list of parameters in order.
>>> see save_npz and assign_params
-
tensorlayer.files.
assign_params
(sess, params, network)[source]¶ Assign the given parameters to the TensorLayer network.
sess : TensorFlow Session params : list
A list of parameters in order.- network :
- The network to be assigned
Layer
class... Save your network as follow: >>> tl.files.save_npz(network.all_params, name=’model_test.npz’) >>> network.print_params() ... ... Next time, load and assign your network as follow: >>> sess.run(tf.initialize_all_variables()) # re-initialize, then save and assign >>> load_params = tl.files.load_npz(name=’model_test.npz’) >>> tl.files.assign_params(sess, load_params, network) >>> network.print_params()
Load and save variables¶
-
tensorlayer.files.
save_any_to_npy
(save_dict={}, name='any.npy')[source]¶ Save variables to .npy file.
>>> tl.files.save_any_to_npy(save_dict={'data': ['a','b']}, name='test.npy') >>> data = tl.files.load_npy_to_any(name='test.npy') >>> print(data) ... {'data': ['a','b']}
-
tensorlayer.files.
save_any_to_npy
(save_dict={}, name='any.npy')[source] Save variables to .npy file.
>>> tl.files.save_any_to_npy(save_dict={'data': ['a','b']}, name='test.npy') >>> data = tl.files.load_npy_to_any(name='test.npy') >>> print(data) ... {'data': ['a','b']}
Visualizing npz file¶
-
tensorlayer.files.
npz_to_W_pdf
(path=None, regx='w1pre_[0-9]+\\.(npz)')[source]¶ Convert the first weight matrix of .npz file to .pdf by using tl.visualize.W().
- path : a string or None
- A folder path to npz files.
- regx : a string
- Regx for the file name.
... convert the first weight matrix of w1_pre...npz file to w1_pre...pdf. >>> tl.files.npz_to_W_pdf(path=’/Users/.../npz_file/’, regx=’w1pre_[0-9]+.(npz)’)


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What does the standard industry term “efficient” really mean?
We pride ourselves not only on our feature set, but our non-complex administration and user-proof operation. Our technology takes the best aspects of SMIL and C++. Our functionality is unmatched, but our 1000/60/60/24/7/365 returns-on-investment and non-complex operation is constantly considered a remarkable achievement. The power to enhance perfectly leads to the aptitude to deploy dynamically. Think super-macro-real-time.
(Text courtesy http://andrewdavidson.com/gibberish/)


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This keyboard is designed for the Dzongkha language of Bhutan. The keyboard follows the current (October 2009) official Dzongkha layout approved by the Department of Information Technology and Dzongkha Development Authority of the Government of Bhutan. This keyboard is fully compliant with the Unicode Standard.
This keyboard has four states: unshifted, Shift, AltGr, and Shift AltGr. The AltGr state can also be called Right Alt. If your hardware keyboard doesn't have an AltGr key, you can simulate it. From the Options tab of Keyman Configuration, tick 'Simulate AltGr with Ctrl+Alt' and click . You can then use Ctrl+Alt to type any keys in the AltGr state and Shift+Ctrl+Alt to type any keys in the Shift AltGr state.
You can also type Latin letters while using this keyboard. To get the standard Latin keystroke for any key instead of the Dzongkha output, type $ first. For example, to get % instead of ༎ type $%.
This keyboard includes an On Screen Keyboard view for easy reference. The On Screen Keyboard works best when associated with a QWERTY US layout.
This is a Unicode keyboard and works with any Unicode font which has support for Tibetan script. The DDC Uchen font fully supports this keyboard and is installed with the keyboard. To find other fonts on your computer which support the keyboard, use the Keyman Font Helper.


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If you're creating a project from scratch, type in the name of the project's top-level directory. If it does not exist, Code Co-op will create it for you.
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Japanese Vegetarian
Five week course in London
A five week introduction to traditional Japanese vegetarian meals, teaching you a selection of rice and noodle dishes.
A five week introduction to traditional Japanese vegetarian meals, teaching you a selection of rice and noodle dishes.
An intensive one-day course looking at how to create the most delicious sauces for use in a range of Japanese cookery.


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about me
I am an undergradute student at Indraprastha Institute of Information Technology, Delhi pursuing my Bachelor’s Degree in Technology with Electronics and Communication being my major.
I have always been actively involved in projects which may help in solving real life problems. I like to design and prototype interactive systems that enhance the interaction between computer systems and humans.
My undergraduate thesis labelled “Designing wearable toolkits and trinkets” is being advised by Dr. Aman Parnami and I’m an active member of the Weave Lab.


Annual Report 2014
“The Crop Trust is an international organization working to safeguard crop diversity, forever.” Marie Haga
Executive Director of the Crop Trust
“The Crop Trust is an international organization working to safeguard crop diversity, forever.” Marie Haga
Executive Director of the Crop Trust


Wine Marinade
- prep: 10 mins
- total: 10 mins
- Servings: 6
- Yield: 6 servings
Mouth pleasing red wine marinade flavored with soy sauce, garlic, ginger
and molasses. Use for pork, vegetables, or chicken.
Ingredients
- ½ cup soy sauce
- ¼ cup red wine
- 3 tablespoons white sugar
- 1 teaspoon minced garlic
- 1 teaspoon ground ginger
- 1 teaspoon molasses
Steps
-
Mix together soy sauce, wine, sugar, garlic, ginger and molasses. Pour
over meat and refrigerate.


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