Enterprise beans are Java EE components that implement Enterprise JavaBeans (EJB) technology. Enterprise beans run in the EJB container, a runtime environment within GlassFish Server (see Container Types). Although transparent to the application developer, the EJB container provides system-level services, such as transactions and security, to its enterprise beans. These services enable you to quickly build and deploy enterprise beans, which form the core of transactional Java EE applications.
The following topics are addressed here:
Written in the Java programming language, an enterprise bean is a server-side component that encapsulates the business logic of an application. The business logic is the code that fulfills the purpose of the application. In an inventory control application, for example, the enterprise beans might implement the business logic in methods called checkInventoryLevel
and orderProduct
. By invoking these methods, clients can access the inventory services provided by the application.
For several reasons, enterprise beans simplify the development of large, distributed applications. First, because the EJB container provides system-level services to enterprise beans, the bean developer can concentrate on solving business problems. The EJB container, rather than the bean developer, is responsible for system-level services, such as transaction management and security authorization.
Second, because the beans rather than the clients contain the application's business logic, the client developer can focus on the presentation of the client. The client developer does not have to code the routines that implement business rules or access databases. As a result, the clients are thinner, a benefit that is particularly important for clients that run on small devices.
Third, because enterprise beans are portable components, the application assembler can build new applications from existing beans. Provided that they use the standard APIs, these applications can run on any compliant Java EE server.
You should consider using enterprise beans if your application has any of the following requirements.
The application must be scalable. To accommodate a growing number of users, you may need to distribute an application's components across multiple machines. Not only can the enterprise beans of an application run on different machines, but also their location will remain transparent to the clients.
Transactions must ensure data integrity. Enterprise beans support transactions, the mechanisms that manage the concurrent access of shared objects.
The application will have a variety of clients. With only a few lines of code, remote clients can easily locate enterprise beans. These clients can be thin, various, and numerous.
Table 32-1 summarizes the two types of enterprise beans. The following sections discuss each type in more detail.
A session bean encapsulates business logic that can be invoked programmatically by a client over local, remote, or web service client views. To access an application that is deployed on the server, the client invokes the session bean's methods. The session bean performs work for its client, shielding it from complexity by executing business tasks inside the server.
A session bean is not persistent. (That is, its data is not saved to a database.)
For code samples, see Chapter 34, "Running the Enterprise Bean Examples".
Session beans are of three types: stateful, stateless, and singleton.
The state of an object consists of the values of its instance variables. In a stateful session bean, the instance variables represent the state of a unique client/bean session. Because the client interacts ("talks") with its bean, this state is often called the conversational state.
As its name suggests, a session bean is similar to an interactive session. A session bean is not shared; it can have only one client, in the same way that an interactive session can have only one user. When the client terminates, its session bean appears to terminate and is no longer associated with the client.
The state is retained for the duration of the client/bean session. If the client removes the bean, the session ends and the state disappears. This transient nature of the state is not a problem, however, because when the conversation between the client and the bean ends, there is no need to retain the state.
A stateless session bean does not maintain a conversational state with the client. When a client invokes the methods of a stateless bean, the bean's instance variables may contain a state specific to that client but only for the duration of the invocation. When the method is finished, the client-specific state should not be retained. Clients may, however, change the state of instance variables in pooled stateless beans, and this state is held over to the next invocation of the pooled stateless bean. Except during method invocation, all instances of a stateless bean are equivalent, allowing the EJB container to assign an instance to any client. That is, the state of a stateless session bean should apply across all clients.
Because they can support multiple clients, stateless session beans can offer better scalability for applications that require large numbers of clients. Typically, an application requires fewer stateless session beans than stateful session beans to support the same number of clients.
A stateless session bean can implement a web service, but a stateful session bean cannot.
A singleton session bean is instantiated once per application and exists for the lifecycle of the application. Singleton session beans are designed for circumstances in which a single enterprise bean instance is shared across and concurrently accessed by clients.
Singleton session beans offer similar functionality to stateless session beans but differ from them in that there is only one singleton session bean per application, as opposed to a pool of stateless session beans, any of which may respond to a client request. Like stateless session beans, singleton session beans can implement web service endpoints.
Singleton session beans maintain their state between client invocations but are not required to maintain their state across server crashes or shutdowns.
Applications that use a singleton session bean may specify that the singleton should be instantiated upon application startup, which allows the singleton to perform initialization tasks for the application. The singleton may perform cleanup tasks on application shutdown as well, because the singleton will operate throughout the lifecycle of the application.
Stateful session beans are appropriate if any of the following conditions are true.
The bean's state represents the interaction between the bean and a specific client.
The bean needs to hold information about the client across method invocations.
The bean mediates between the client and the other components of the application, presenting a simplified view to the client.
Behind the scenes, the bean manages the work flow of several enterprise beans.
To improve performance, you might choose a stateless session bean if it has any of these traits.
The bean's state has no data for a specific client.
In a single method invocation, the bean performs a generic task for all clients. For example, you might use a stateless session bean to send an email that confirms an online order.
The bean implements a web service.
Singleton session beans are appropriate in the following circumstances.
State needs to be shared across the application.
A single enterprise bean needs to be accessed by multiple threads concurrently.
The application needs an enterprise bean to perform tasks upon application startup and shutdown.
The bean implements a web service.
A message-driven bean is an enterprise bean that allows Java EE applications to process messages asynchronously. This type of bean normally acts as a JMS message listener, which is similar to an event listener but receives JMS messages instead of events. The messages can be sent by any Java EE component (an application client, another enterprise bean, or a web component) or by a JMS application or system that does not use Java EE technology. Message-driven beans can process JMS messages or other kinds of messages.
The most visible difference between message-driven beans and session beans is that clients do not access message-driven beans through interfaces. Interfaces are described in the section Accessing Enterprise Beans. Unlike a session bean, a message-driven bean has only a bean class.
In several respects, a message-driven bean resembles a stateless session bean.
A message-driven bean's instances retain no data or conversational state for a specific client.
All instances of a message-driven bean are equivalent, allowing the EJB container to assign a message to any message-driven bean instance. The container can pool these instances to allow streams of messages to be processed concurrently.
A single message-driven bean can process messages from multiple clients.
The instance variables of the message-driven bean instance can contain some state across the handling of client messages, such as a JMS API connection, an open database connection, or an object reference to an enterprise bean object.
Client components do not locate message-driven beans and invoke methods directly on them. Instead, a client accesses a message-driven bean through, for example, JMS by sending messages to the message destination for which the message-driven bean class is the MessageListener
. You assign a message-driven bean's destination during deployment by using GlassFish Server resources.
Message-driven beans have the following characteristics.
They execute upon receipt of a single client message.
They are invoked asynchronously.
They are relatively short-lived.
They do not represent directly shared data in the database, but they can access and update this data.
They can be transaction-aware.
They are stateless.
When a message arrives, the container calls the message-driven bean's onMessage
method to process the message. The onMessage
method normally casts the message to one of the five JMS message types and handles it in accordance with the application's business logic. The onMessage
method can call helper methods or can invoke a session bean to process the information in the message or to store it in a database.
A message can be delivered to a message-driven bean within a transaction context, so all operations within the onMessage
method are part of a single transaction. If message processing is rolled back, the message will be redelivered. For more information, see Receiving Messages Asynchronously Using a Message-Driven Bean and Chapter 51, "Transactions".
Session beans allow you to send JMS messages and to receive them synchronously but not asynchronously. To avoid tying up server resources, do not to use blocking synchronous receives in a server-side component; in general, JMS messages should not be sent or received synchronously. To receive messages asynchronously, use a message-driven bean.
|
Note: The material in this section applies only to session beans and not to message-driven beans. Because they have a different programming model, message-driven beans do not have interfaces or no-interface views that define client access. |
Clients access enterprise beans either through a no-interface view or through a business interface. A no-interface view of an enterprise bean exposes the public methods of the enterprise bean implementation class to clients. Clients using the no-interface view of an enterprise bean may invoke any public methods in the enterprise bean implementation class or any superclasses of the implementation class. A business interface is a standard Java programming language interface that contains the business methods of the enterprise bean.
A client can access a session bean only through the methods defined in the bean's business interface or through the public methods of an enterprise bean that has a no-interface view. The business interface or no-interface view defines the client's view of an enterprise bean. All other aspects of the enterprise bean (method implementations and deployment settings) are hidden from the client.
Well-designed interfaces and no-interface views simplify the development and maintenance of Java EE applications. Not only do clean interfaces and no-interface views shield the clients from any complexities in the EJB tier, but they also allow the enterprise beans to change internally without affecting the clients. For example, if you change the implementation of a session bean business method, you won't have to alter the client code. But if you were to change the method definitions in the interfaces, you might have to modify the client code as well. Therefore, it is important that you design the interfaces and no-interface views carefully to isolate your clients from possible changes in the enterprise beans.
Session beans can have more than one business interface. Session beans should, but are not required to, implement their business interface or interfaces.
The client of an enterprise bean obtains a reference to an instance of an enterprise bean through either dependency injection, using Java programming language annotations, or JNDI lookup, using the Java Naming and Directory Interface syntax to find the enterprise bean instance.
Dependency injection is the simplest way of obtaining an enterprise bean reference. Clients that run within a Java EE server-managed environment, JavaServer Faces web applications, JAX-RS web services, other enterprise beans, or Java EE application clients support dependency injection using the javax.ejb.EJB
annotation.
Applications that run outside a Java EE server-managed environment, such as Java SE applications, must perform an explicit lookup. JNDI supports a global syntax for identifying Java EE components to simplify this explicit lookup.
Three JNDI namespaces are used for portable JNDI lookups: java:global
, java:module
, and java:app
.
The java:global
JNDI namespace is the portable way of finding remote enterprise beans using JNDI lookups. JNDI addresses are of the following form:
java:global[/application name]/module name /enterprise bean name[/interface name ]
Application name and module name default to the name of the application and module minus the file extension. Application names are required only if the application is packaged within an EAR. The interface name is required only if the enterprise bean implements more than one business interface.
The java:module
namespace is used to look up local enterprise beans within the same module. JNDI addresses using the java:module
namespace are of the following form:
java:module/enterprise bean name/[interface name]
The interface name is required only if the enterprise bean implements more than one business interface.
The java:app
namespace is used to look up local enterprise beans packaged within the same application. That is, the enterprise bean is packaged within an EAR file containing multiple Java EE modules. JNDI addresses using the java:app
namespace are of the following form:
java:app[/module name]/enterprise bean name [/interface name]
The module name is optional. The interface name is required only if the enterprise bean implements more than one business interface.
For example, if an enterprise bean, MyBean
, is packaged within the web application archive myApp.war
, the module name is myApp
. The portable JNDI name is java:module/MyBean
. An equivalent JNDI name using the java:global
namespace is java:global/myApp/MyBean
.
When you design a Java EE application, one of the first decisions you make is the type of client access allowed by the enterprise beans: remote, local, or web service.
Whether to allow local or remote access depends on the following factors.
Tight or loose coupling of related beans: Tightly coupled beans depend on one another. For example, if a session bean that processes sales orders calls a session bean that emails a confirmation message to the customer, these beans are tightly coupled. Tightly coupled beans are good candidates for local access. Because they fit together as a logical unit, they typically call each other often and would benefit from the increased performance that is possible with local access.
Type of client: If an enterprise bean is accessed by application clients, it should allow remote access. In a production environment, these clients almost always run on machines other than those on which GlassFish Server is running. If an enterprise bean's clients are web components or other enterprise beans, the type of access depends on how you want to distribute your components.
Component distribution: Java EE applications are scalable because their server-side components can be distributed across multiple machines. In a distributed application, for example, the server that the web components run on may not be the one on which the enterprise beans they access are deployed. In this distributed scenario, the enterprise beans should allow remote access.
Performance: Owing to such factors as network latency, remote calls may be slower than local calls. On the other hand, if you distribute components among different servers, you may improve the application's overall performance. Both of these statements are generalizations; performance can vary in different operational environments. Nevertheless, you should keep in mind how your application design might affect performance.
If you aren't sure which type of access an enterprise bean should have, choose remote access. This decision gives you more flexibility. In the future, you can distribute your components to accommodate the growing demands on your application.
Although it is uncommon, it is possible for an enterprise bean to allow both remote and local access. If this is the case, either the business interface of the bean must be explicitly designated as a business interface by being decorated with the @Remote
or @Local
annotations, or the bean class must explicitly designate the business interfaces by using the @Remote
and @Local
annotations. The same business interface cannot be both a local and a remote business interface.
A local client has these characteristics.
It must run in the same application as the enterprise bean it accesses.
It can be a web component or another enterprise bean.
To the local client, the location of the enterprise bean it accesses is not transparent.
The no-interface view of an enterprise bean is a local view. The public methods of the enterprise bean implementation class are exposed to local clients that access the no-interface view of the enterprise bean. Enterprise beans that use the no-interface view do not implement a business interface.
The local business interface defines the bean's business and lifecycle methods. If the bean's business interface is not decorated with @Local
or @Remote
, and if the bean class does not specify the interface using @Local
or @Remote
, the business interface is by default a local interface.
To build an enterprise bean that allows only local access, you may, but are not required to, do one of the following.
Create an enterprise bean implementation class that does not implement a business interface, indicating that the bean exposes a no-interface view to clients. For example:
@Session public class MyBean { ... }
Annotate the business interface of the enterprise bean as a @Local
interface. For example:
@Local public interface InterfaceName { ... }
Specify the interface by decorating the bean class with @Local
and specify the interface name. For example:
@Local(InterfaceName.class) public class BeanName implements InterfaceName { ... }
Client access to an enterprise bean that exposes a local, no-interface view is accomplished through either dependency injection or JNDI lookup.
To obtain a reference to the no-interface view of an enterprise bean through dependency injection, use the javax.ejb.EJB
annotation and specify the enterprise bean's implementation class:
@EJB ExampleBean exampleBean;
To obtain a reference to the no-interface view of an enterprise bean through JNDI lookup, use the javax.naming.InitialContext
interface's lookup
method:
ExampleBean exampleBean = (ExampleBean) InitialContext.lookup("java:module/ExampleBean");
Clients do not use the new
operator to obtain a new instance of an enterprise bean that uses a no-interface view.
Client access to enterprise beans that implement local business interfaces is accomplished through either dependency injection or JNDI lookup.
To obtain a reference to the local business interface of an enterprise bean through dependency injection, use the javax.ejb.EJB
annotation and specify the enterprise bean's local business interface name:
@EJB Example example;
To obtain a reference to a local business interface of an enterprise bean through JNDI lookup, use the javax.naming.InitialContext
interface's lookup
method:
ExampleLocal example = (ExampleLocal) InitialContext.lookup("java:module/ExampleLocal");
A remote client of an enterprise bean has the following traits.
It can run on a different machine and a different JVM from the enterprise bean it accesses. (It is not required to run on a different JVM.)
It can be a web component, an application client, or another enterprise bean.
To a remote client, the location of the enterprise bean is transparent.
The enterprise bean must implement a business interface. That is, remote clients may not access an enterprise bean through a no-interface view.
To create an enterprise bean that allows remote access, you must either
Decorate the business interface of the enterprise bean with the @Remote
annotation:
@Remote public interface InterfaceName { ... }
Or decorate the bean class with @Remote
, specifying the business interface or interfaces:
@Remote(InterfaceName.class) public class BeanName implements InterfaceName { ... }
The remote interface defines the business and lifecycle methods that are specific to the bean. For example, the remote interface of a bean named BankAccountBean
might have business methods named deposit
and credit
. Figure 32-1 shows how the interface controls the client's view of an enterprise bean.
Figure 32-1 Interfaces for an Enterprise Bean with Remote Access
Client access to an enterprise bean that implements a remote business interface is accomplished through either dependency injection or JNDI lookup.
To obtain a reference to the remote business interface of an enterprise bean through dependency injection, use the javax.ejb.EJB
annotation and specify the enterprise bean's remote business interface name:
@EJB Example example;
To obtain a reference to a remote business interface of an enterprise bean through JNDI lookup, use the javax.naming.InitialContext
interface's lookup
method:
ExampleRemote example = (ExampleRemote) InitialContext.lookup("java:global/myApp/ExampleRemote");
A web service client can access a Java EE application in two ways. First, the client can access a web service created with JAX-WS. (For more information on JAX-WS, see Chapter 28, "Building Web Services with JAX-WS".) Second, a web service client can invoke the business methods of a stateless session bean. Message beans cannot be accessed by web service clients.
Provided that it uses the correct protocols (SOAP, HTTP, WSDL), any web service client can access a stateless session bean, whether or not the client is written in the Java programming language. The client doesn't even "know" what technology implements the service: stateless session bean, JAX-WS, or some other technology. In addition, enterprise beans and web components can be clients of web services. This flexibility enables you to integrate Java EE applications with web services.
A web service client accesses a stateless session bean through the bean's web service endpoint implementation class. By default, all public methods in the bean class are accessible to web service clients. The @WebMethod
annotation may be used to customize the behavior of web service methods. If the @WebMethod
annotation is used to decorate the bean class's methods, only those methods decorated with @WebMethod
are exposed to web service clients.
For a code sample, see A Web Service Example: helloservice.
The type of access affects the parameters of the bean methods that are called by clients. The following sections apply not only to method parameters but also to method return values.
The parameters of remote calls are more isolated than those of local calls. With remote calls, the client and the bean operate on different copies of a parameter object. If the client changes the value of the object, the value of the copy in the bean does not change. This layer of isolation can help protect the bean if the client accidentally modifies the data.
In a local call, both the client and the bean can modify the same parameter object. In general, you should not rely on this side effect of local calls. Perhaps someday you will want to distribute your components, replacing the local calls with remote ones.
As with remote clients, web service clients operate on different copies of parameters than does the bean that implements the web service.
Because remote calls are likely to be slower than local calls, the parameters in remote methods should be relatively coarse-grained. A coarse-grained object contains more data than a fine-grained one, so fewer access calls are required. For the same reason, the parameters of the methods called by web service clients should also be coarse-grained.
To develop an enterprise bean, you must provide the following files.
Enterprise bean class: Implements the business methods of the enterprise bean and any lifecycle callback methods.
Business interfaces: Define the business methods implemented by the enterprise bean class. A business interface is not required if the enterprise bean exposes a local, no-interface view.
Helper classes: Other classes needed by the enterprise bean class, such as exception and utility classes.
Package the programming artifacts in the preceding list either into an EJB JAR file (a stand-alone module that stores the enterprise bean) or within a web application archive (WAR) module. See Packaging Enterprise Beans in EJB JAR Modules and Packaging Enterprise Beans in WAR Modules for more information.
Because enterprise beans are composed of multiple parts, it's useful to follow a naming convention for your applications. Table 32-2 summarizes the conventions for the example beans in this tutorial.
An enterprise bean goes through various stages during its lifetime, or lifecycle. Each type of enterprise bean (stateful session, stateless session, singleton session, or message-driven) has a different lifecycle.
The descriptions that follow refer to methods that are explained along with the code examples in the next two chapters. If you are new to enterprise beans, you should skip this section and run the code examples first.
Figure 32-2 illustrates the stages that a stateful session bean passes through during its lifetime. The client initiates the lifecycle by obtaining a reference to a stateful session bean. The container performs any dependency injection and then invokes the method annotated with @PostConstruct
, if any. The bean is now ready to have its business methods invoked by the client.
Figure 32-2 Lifecycle of a Stateful Session Bean
While in the ready stage, the EJB container may decide to deactivate, or passivate, the bean by moving it from memory to secondary storage. (Typically, the EJB container uses a least-recently-used algorithm to select a bean for passivation.) The EJB container invokes the method annotated @PrePassivate
, if any, immediately before passivating it. If a client invokes a business method on the bean while it is in the passive stage, the EJB container activates the bean, calls the method annotated @PostActivate
, if any, and then moves it to the ready stage.
At the end of the lifecycle, the client invokes a method annotated @Remove
, and the EJB container calls the method annotated @PreDestroy
, if any. The bean's instance is then ready for garbage collection.
Your code controls the invocation of only one lifecycle method: the method annotated @Remove
. All other methods in Figure 32-2 are invoked by the EJB container. See Chapter 52, "Resource Adapters and Contracts" for more information.
Because a stateless session bean is never passivated, its lifecycle has only two stages: nonexistent and ready for the invocation of business methods. Figure 32-3 illustrates the stages of a stateless session bean.
Figure 32-3 Lifecycle of a Stateless or Singleton Session Bean
The EJB container typically creates and maintains a pool of stateless session beans, beginning the stateless session bean's lifecycle. The container performs any dependency injection and then invokes the method annotated @PostConstruct
, if it exists. The bean is now ready to have its business methods invoked by a client.
At the end of the lifecycle, the EJB container calls the method annotated @PreDestroy
, if it exists. The bean's instance is then ready for garbage collection.
Like a stateless session bean, a singleton session bean is never passivated and has only two stages, nonexistent and ready for the invocation of business methods, as shown in Figure 32-3.
The EJB container initiates the singleton session bean lifecycle by creating the singleton instance. This occurs upon application deployment if the singleton is annotated with the @Startup
annotation. The container performs any dependency injection and then invokes the method annotated @PostConstruct
, if it exists. The singleton session bean is now ready to have its business methods invoked by the client.
At the end of the lifecycle, the EJB container calls the method annotated @PreDestroy
, if it exists. The singleton session bean is now ready for garbage collection.
Figure 32-4 illustrates the stages in the lifecycle of a message-driven bean.
Figure 32-4 Lifecycle of a Message-Driven Bean
The EJB container usually creates a pool of message-driven bean instances. For each instance, the EJB container performs these tasks.
If the message-driven bean uses dependency injection, the container injects these references before instantiating the instance.
The container calls the method annotated @PostConstruct
, if any.
Like a stateless session bean, a message-driven bean is never passivated and has only two states: nonexistent and ready to receive messages.
At the end of the lifecycle, the container calls the method annotated @PreDestroy
, if any. The bean's instance is then ready for garbage collection.


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PastelDeck is a very simple markdown to html deckbuilder that runs from your CLI.. All you need to do is run
$ yarn global add PastelDeck
or
$ npm install -g PastelDeck
and then run pasteldeck
To continue, press down or right
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Property-Based Testing
Table of Contents
Index
Roadmap
Introduction
Top
A First Taste of Testing
Overview
A Tutorial on Typeclasses in Coq (
Typeclasses
)
Top
Basics
Classes and Instances
Parameterized Instances: New Typeclasses from Old
Class Hierarchies
How It Works
Implicit Generalization
Records are Products
Typeclasses are Records
Inferring Instances
Typeclasses and Proofs
Propositional Typeclass Members
Substructures
Some Useful Typeclasses
Dec
Monad
Others
Controlling Instantiation
"Defaulting"
Manipulating the Hint Database
Debugging
Instantiation Failures
Nontermination
Alternative Structuring Mechanisms
Advice from Experts
Matthieu Sozeau
John Wiegley
Michael Soegtrop
Abhishek Anand
Further Reading
Core QuickChick (
QC
)
Top
Generators
The
G
Monad
Primitive generators
Lists
Custom Generators
Checkers
Shrinking
Exercise: Ternary Trees
Putting it all Together
Sized Generators
Automation
Collecting Statistics
Dealing with Preconditions
Another Precondition: Binary Search Trees
Case Study: a Typed Imperative Language (
TImp
)
Top
Atoms, Types and Contexts
Atoms
Types
List Map with Decidable Equality
Contexts
Expressions
Typed Expressions
Generating Typed Expressions
Values and States
Values
States
Evaluation
Shrinking for Expressions
Back to Soundness
Well-Typed Programs
Well-typed commands
Automation (Revisited)
(More) Typeclasses for Generation
Using "SuchThat" Typeclasses
A Larger Case Study (
Vellvm
)
PostScript
Top
Future Directions
Recommended Reading


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Step-By-Step Guide to Developing Schematron Rules
Octavian Nadolu, Syncro Soft
Schematron is a very simple and powerful language. It is used in various domains (financial, insurance, government, and technical publishing sectors) for quality assurance, validating business rules, constraint checking, or data reporting. During the presentation, I will show you how to create Schematron rules step-by-step, apply the rules over one or multiple documents, and also how to integrate Schematron rules in your development process.
Schematron is a rule-based validation language for making assertions about presence or absence of certain patterns in XML documents. With Schematron, you can express constraints in a way that you cannot perform with other schemas (like XSD, RNG, or DTD). XSD, RNG, and DTD schemas define structural aspects and data types of the XML documents and can be used to check big things, such as if an element is allowed in a specific context, or if an attribute is allowed for an element. But with Schematron, you can create your custom rules specific to your project, and check things such as if the text from an element respects a particular constraint, or verify data inter-dependencies such as if a start date from an attribute value is set before the end date attribute value.
In a Schematron document, you can add a collection of rules that contain tests. The Schematron content is written as an XML document using a small number of elements, and this makes it easy to understand and write, even for people that are not programmers.
When a document is validated with a Schematron schema, for each rule from the schema, the following steps are performed:
- First, it is determined the context on which the rule is applied. That can be an element, or an attribute, or maybe a text fragment. The context is expressed using an XPath expression.
- Next, it checks if the assertions from the rule are evaluated to true or false. Depending on the evaluation result, the assertion message is displayed. The assertions are also expressed using XPath expressions that are evaluated to true or false.
Schematron Structure
Basically, the most important components of a Schematron schema are the tests. The tests are defined in the assert and report elements. An assert generates a message when a test statement evaluates to false. A report generates a message when a test statement evaluate to true. The generated messages are defined in the assert and report elements.
Example 1: An assert that tests if the price is greater that 10
<assert test="@price > 10">The price is too small</assert>
Tests that are executed in the same context can be grouped in a rule element. A rule defines a context on which a list of assertions will be tested, and is comprised of one or more assert or report elements.
Example 2: A set of tests for the book element
<rule context="book">
<assert test="@price > 10">The price is too small</assert>
<assert test="@isbn">The book ISBN is missing</assert>
</rule>
A set of rules that are related in some way can be grouped in a pattern element. The pattern elements are added in a schema element, that is the top-level element of a Schematron schema .
Example 3: A sample ISO Schematron schema
<schema xmlns="http://purl.oclc.org/dsdl/schematron">
<pattern>
<rule context="book">
<assert test="@price > 10">The price is too small</assert>
<assert test="@isbn">The book isbn is missing</assert>
</rule>
</pattern>
</schema>
The Schematron is saved in a file that has the suffix ".sch". Also the prefix for the Schematron elements is by convention "sch:".
Validate with Schematron Schema
A Schematron schema can be associated with an XML document using an xml-model processing instruction. You can also use tool-specific association options like: associate a set al XML files that have a specific namespace with a Schematron, or associate all XML files from a project or from a directory.
To validate an XML file with a Schematron schema, you can use an editing framework that will allow you to run the validation on the currently edited XML file and will present the problems directly in the editor. You can also run the validation over multiple XML files and check all the reported problems from a user interface. Another solution is to use Apache Ant, and run the validation from a bat/shell script. You can also use pipelining languages, like W3C's XProc.
The result of the validation can be presented in the editor in case you are using an editing framework, and you will have the content that does not satisfy the assertions highlighted and the messages displayed in the user interface. In case you are using bat/shell script, you can get the validation errors in an XML file, normally the format of the XML file is a Schematron Validation Reporting Language (SVRL).
Schematron assertion messages can be presented in different languages. The Schematron developer can create a message for each language using the diagnostics elements. For each translated message, a diagnostic element need to be created and referenced from the assert or report element. Depending on the application language or on the Schematron file language, a message in a specific language can be presented to the user.
Schematron Quick Fixes
Schematron allows the schema author to define and control the messages that are presented to the user. The messages can also include hints to the user on how to solve the problem, but they will need to manually correct the issue. This wastes valuable time since the user needs to correct the problem manually and additional errors can be created when they fix the reported problem. An action that will automatically correct the reported problem for Schematron validation errors will bridge this gap, saving time and avoiding the potential for causing other issues.
Schematron Quick Fix (SQF) it is a simple language that allows the Schematron developer to define actions that will correct the reported problems. SQF was created as an extension of the Schematron language. It was developed within the W3C community group "Quick-Fix Support for XML Community Group". First draft of the Schematron Quick Fix specification was published in April 2015, second draft in March 2018, and it is now available on the W3C Quick-Fix Support for XML community group page.
Example 4: A Schematron Quick Fix that removes a bold element keeping its text content
<sch:rule context="title">
<sch:report test="b" sqf:fix="resolveBold">Bold element is not allowed in title.</sch:report>
<sqf:fix id="resolveBold">
<sqf:description>
<sqf:title>Change the bold element into text</sqf:title>
</sqf:description>
<sqf:replace match="b" select="node()"/>
</sqf:fix>
</sch:rule>
An SQF fix consists of a set of operations that can be performed on an XML document. These are basic operations (add, delete, replace, and string replace) that need to make precise changes in the document. This means that when a fix is applied, only the affected part of the XML document should be changed and the DOCTYPE declaration, entities, etc. must be preserved.
Conclusion
It is important to have a quality control over the XML documents from your project. To achieve this you can use Schematron schema in combination with an XSD, RNG, or DTD schema. Schematron solves the limitation that other types of schema have when validating XML documents because it allows the schema author to define the tests and control the messages that are presented to the user. The validation problems are more accessible to users and it ensures that they understand the problem.
Contact: octavian_mailto:nadolu@oxygenxml.com


Preparation time
1-2 hours
Cooking time
10 to 30 mins
Serves
Serves 4
Go all out for a Mexican barbecue with spiced cornmeal stuffed in corn husks with lime and honeyed chicken thighs.
Place all the ingredients for the marinade in a small bowl then mix well with a spoon.  Cover with a cling film to marinate in the fridge for a couple of hours.
For the corn and quinoa tamale, gently pull off the husk (leaves) from around the cob, making sure to keep it whole. With a sharp knife, cut off the corn from the cob and keep the leaves to one side.
In a pan, heat up the olive oil and add the onions, garlic, lemon thyme and finely chopped chipotle. Cook by gently stirring until the onions get a light golden colour; at this stage add the corn and the apple juice. Cover with a lid and cook on a low heat to simmer gently.
After 15 minutes, keeping the ingredients in the pan, use a hand-held stick blender to slightly blend the mixture - pulse 3 or 4 times (only enough to start to break some corn and release some of the starch). Add the quinoa, mix well, cover with a lid and cook on a low heat for another 10 minutes. Pour the mixture into a clean bowl, then set aside to cool.
To shape the tamale: Cut four 15cm/6in lengths of kitchen string. On a clean surface overlap the corn leaves to make four crosses. Place equal amounts of the mixture in the middle and then fold in the right side to the left, then the top down toward you, then the left to right and finally, the front up to complete the parcel. Tighten the parcel neatly with a piece of string.
For the salsa, heat a griddled pan or barbecue to hot. Toss the vegetables in very little of the olive oil, season with salt and pepper then sear them on a very hot cast iron griddle or barbecue to slightly char the outside.
Place in a blender, add the rest of the olive oil with some salt, pepper, garlic and lime juice and blend to a slightly chunky sauce. Taste and season again if necessary.
Preheat a griddle pan or barbecue to medium-high and grill the marinated chicken thighs until cooked through, then set aside to rest. (Insert a skewer into the thickest part of the thigh to check that the juices run clear.) Finish in the oven if necessary at 200C/400F/Gas 6.
Place the tamales on the griddle or barbecue to colour on both sides. Then switch off the heat (or move the tamale off the direct heat if on the barbecue) and leave to warm through.
To serve, place the salsa in a jug. Cut off the top leaves of each tamale with scissors place one on each plate. Place a chicken thigh alongside, sprinkle over the herbs and serve.


Tourism in India is important for the country's economy and is growing rapidly. The World Travel and Tourism Council calculated that tourism generated ₹16.91 lakh crore (US$240 billion) or 9.2% of India's GDP in 2018 and supported 42.673 million jobs, 8.1% of its total employment. The sector is predicted to grow at an annual rate of 6.9% to ₹32.05 lakh crore (US$450 billion) by 2028 (9.9% of GDP). In October 2015, India's medical tourism sector was estimated to be worth US$3 billion, and it is projected to grow to US$7–8 billion by 2020.In 2014, 184,298 foreign patients travelled to India to seek medical treatment.
Reasons to visit India:
India is a vibrant land of startling contrasts where both the traditional and modern worlds meet.
The world's seventh largest nation by area and the second largest in terms of population.
India boasts a rich heritage that's the result of centuries of different cultures and religions leaving their mark.
Things to do for travelers include the opportunity to experience an array of sacred sites and spiritual encounters, while nature lovers will enjoy its sun-washed beaches, lush national parks, and exciting wildlife sanctuaries.
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ANSWER 6 RIGHT QUESTIONS AND WIN AN EXCITING CHANCE TO GET FREE DISCOUNT VOUCHERS FOR YOUR TRIP


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My name is Tshegofatso Mabona!
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--- layout: post status: publish published: true title: "燕子矶，北望长江。" author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 1456 wordpress_url: https://leaskh.com/?p=1456 date: '2010-11-29 00:04:04 +0800' date_gmt: '2010-11-28 16:04:04 +0800' categories: tags: [] comments: [] ---


Good Morning Britain 'shake-up' as Piers Morgan works Friday for General Election coverage
Joked he will be putting in a shift on Friday as a 'national duty'
by Robert LeighPiers Morgan will 'drag' himself back into work on Friday to helm Good Morning Britain's coverage of the General Election.
Many GMB fans were delighted to hear the ex newspaper editor will be working on his usual day off.
But there's no need to fret Piers will be overworked and turn up for five days (or even four days) in a row this week because of the shake up - he'll still be taking Thursday off.
Piers, 54, joked to viewers earlier today he will be putting in a shift on Friday as a 'national duty'.
Read more: Good Morning Britain viewers reduced to tears as lonely widower kisses a photo of his late wife
"There's an election special, so my weekend will not be starting on Wednesday morning as it normally does," Piers said.
"I've got to be dragged back in here… shocking."
Fellow host Susanna Reid pointed out: "It's not a full week is it? You won't be doing Thursday."
Piers replied: "No, but four days is a real stretch.
Read more: Piers Morgan apologises to Ruth Langsford for missing Eamonn Holmes' 60th birthday party
"But I will do it for you lot - I will do it for the nation."
Chuckling, Susanna fired back: "I thought you were going to say you would do it for the money, which might be more honest."
Many viewers gave the change to the usual presenting line up of Kate Garraway, Charlotte Hawkins and Ben Shephard the thumbs up.
"How could you not love Piers. Hilarious as always!" tweeted one viewer.
"GMB is only best when Piers Morgan is on," claimed another.
A third added: "We need you on Friday."
However, one social media user objected: "The nation would rather you didn't."
And another added: "That's one way to ruin my Friday morning @gmb, I'll he watching @BBCBreakfast instead now!"
- Good Morning Britain airs on weekdays from 6am
Leave us a comment on our Facebook page @EntertainmentDailyFix and let us know what you think of this story.


If you see this page you may have exceed the usage limits as a free single end user of the given API, OpenRouteService will return this error message.
If you repeatedly exceed the limits, your access to the API may be temporarily blocked.
Please inform yourself regarding
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Creates an new instance of the connection parameters.
(optional) an object to be converted into key-value pairs to initialize this connection.
Adds 'sectionParams' to this ConfigParams object under the section named 'section'.
name of the section, under which 'sectionParams' is to be added. The keys of 'sectionParams' will be renamed to "(section).<key's name>", when added to this ConfigParams object.
ConfigParams that are to be added under the section named 'section'.
map of items to append to this StringValueMap.
Removes all values from this StringValueMap.
a clone of this object.
key of the item to retrieve from this StringValueMap.
the item stored by the given key.
Gets the AWS access id.
the AWS access id.
Gets the AWS client key.
the AWS client key.
Gets the AWS account id.
the AWS account id.
Gets the AWS resource ARN. If the ARN is not defined it automatically generates it from other properties.
the AWS resource ARN.
key of the item to retrieve.
the item with the given key as an AnyValueArray object.
key of the item to retrieve.
value to use if the item with the given key cannot be converted into an AnyValueArray.
the item with the given key as an AnyValueArray object or 'defaultValue', if conversion is not possible.
key of the item to retrieve.
the item with the given key as a boolean (or false as the default).
key of the item to retrieve.
value to return if conversion is not possible.
the item with the given key as a boolean or the defaultValue (if conversion is not possible).
key of the item to retrieve.
the item with the given key as a Datetime (or null as the default).
key of the item to retrieve.
value to return if conversion is not possible.
the item with the given key as a Datetime or the defaultValue (if conversion is not possible).
key of the item to retrieve.
the item with the given key as a double (or 0 as the default).
key of the item to retrieve.
value to return if conversion is not possible.
the item with the given key as a double or the defaultValue (if conversion is not possible).
key of the item to retrieve.
the item with the given key as a float (or 0 as the default).
key of the item to retrieve.
value to return if conversion is not possible.
the item with the given key as a float or the defaultValue (if conversion is not possible).
key of the item to retrieve.
the item with the given key as an integer (or 0 as the default).
key of the item to retrieve.
value to return if conversion is not possible.
the item with the given key as an integer or the defaultValue (if conversion is not possible).
key of the item to retrieve.
the item with the given key as a long (or 0 as the default).
key of the item to retrieve.
value to return if conversion is not possible.
the item with the given key as a long or the defaultValue (if conversion is not possible).
key of the item to retrieve.
the item with the given key as an AnyValueMap object.
key of the item to retrieve.
value to use if the item with the given key cannot be converted into an AnyValueMap.
the item with the given key as an AnyValueMap object or 'defaultValue', if conversion is not possible.
key of the item to retrieve.
the item with the given key as a nullable AnyValueArray object (returns null if the item with the given key is null).
key of the item to retrieve.
the item with the given key as a nullable boolean.
key of the item to retrieve.
the item with the given key as a nullable Datetime.
key of the item to retrieve.
the item with the given key as a nullable double.
key of the item to retrieve.
the item with the given key as a nullable float.
key of the item to retrieve.
the item with the given key as a nullable integer.
key of the item to retrieve.
the item with the given key as a nullable long.
key of the item to retrieve.
the item with the given key as a nullable AnyValueMap object (returns null if the item with the given key is null).
key of the item to retrieve.
the item with the given key as a nullable string.
Converts the item with the given key into a nullable object of type 'type' using [[TypeConverter.toNullableType]] and returns it.
the TypeCode to be used in TypeConverter.toNullableType
key of the item to retrieve.
the item with the given key as a nullable object of type 'type'.
key of the item to retrieve.
the item with the given key without any conversions or all items (if 'key' is undefined or omitted).
key of the item to retrieve.
the item with the given key as a string (or null as the default).
key of the item to retrieve.
value to return if conversion is not possible.
the item with the given key as a string or the defaultValue (if conversion is not possible).
Converts the item with the given key into an object of type 'type' using [[TypeConverter.toTypeWithDefault]] and returns it.
the TypeCode to be used in TypeConverter.toTypeWithDefault
key of the item to retrieve.
the item with the given key as an object of type 'type' (or null as the default).
Converts the item with the given key into an object of type 'type' using [[TypeConverter.toTypeWithDefault]] and returns it.
the TypeCode to be used in TypeConverter.toTypeWithDefault
value to return if conversion is not possible.
the item with the given key as an object of type 'type' or the defaultValue, if conversion is not possible.
key of the item to retrieve.
the item with the given key as an AnyValue object.
all of the keys that are contained in this StringValueMap as a list of strings.
Gets the AWS partition name.
the AWS partition name.
Gets the AWS region.
the AWS region.
Gets the AWS resource id.
the AWS resource id.
Gets the AWS resource type.
the AWS resource type.
name of the section to retrieve configuration parameters from.
all configuration parameters that belong to the section named 'section'.
Example key "Section-1.Subsection-1-1.Key-1-1-1" contains the section named "Section-1".
Calling getSection("Section-1")
would return a ConfigParams object containing
the key "Subsection-1-1.Key-1-1-1"
the names of all sections that are present in this object's complex keys.
Example key "Section-1.Subsection-1-1.Key-1-1-1" contains the section named "Section-1".
Gets the AWS service name.
the AWS service name.
the number of key-value pairs stored in this StringValueMap.
Overrides the configuration parameters stored in this object with the ones in 'configParams'. If a configuration is already set in this ConfigParams object, it will be overwritten by the value in 'configParams' with the same key.
configuration parameters to override the parameters of this object with.
ConfigParams object with overridden parameters.
the key by which to insert the given value.
the value to insert into this StringValueMap by the given key.
key of the item to remove.
Sets the AWS access id.
the AWS access id.
Sets the AWS client key.
a new AWS client key.
Sets the AWS account id.
the AWS account id.
Sets the AWS resource ARN. When it sets the value, it automatically parses the ARN and sets individual parameters.
a new AWS resource ARN.
the key by which to set the 'value' passed.
the value to set in this StringValueMap with the given 'key'. If 'key' is omitted, then this StringValueMap will be set (cleared and filled anew) using 'value'. In this case 'value' will be converted to a map using "MapConverter.toMap(value)" and set to this StringValueMap.
Sets the default configurations for this ConfigParams object, based on the default configuration parameters passed in 'defaultConfigParams'. If a configuration is already set in this ConfigParams object, it will not be overwritten by the default value in 'defaultConfigParams' with the same key.
the default configuration parameters to use.
this ConfigParams object with the newly set defaults.
Sets the AWS partition name.
a new AWS partition name.
Sets the AWS region.
a new AWS region.
Sets the AWS resource id.
a new AWS resource id.
Sets the AWS resource type.
a new AWS resource type.
Sets the AWS service name.
a new AWS service name.
this StringValueMap as a semicolon-separated values string.
Example: "key1=value1;key2;key3=value3".
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(optional) transaction id to trace execution through call chain.
a ConfigException or null if validation passed successfully.
Retrieves AwsConnectionParams from configuration parameters. The values are retrieves from "connection" and "credential" sections.
configuration parameters
the generated AwsConnectionParams object.
Static method for creating a StringValueMap using the maps passed as parameters.
the maps passed to this method to create a StringValueMap with.
the StringValueMap created.
Creates a new AwsConnectionParams object filled with key-value pairs serialized as a string.
a string with serialized key-value pairs as "key1=value1;key2=value2;..." Example: "Key1=123;Key2=ABC;Key3=2016-09-16T00:00:00.00Z"
a new AwsConnectionParams object.
Static method that creates a ConfigParams object using the tuples passed to the method.
the tuples to convert to a ConfigParams object.
the generated ConfigParams.
Static method for creating a StringValueMap from an array of tuples.
the key-value tuples array to initialize the new StringValueMap with.
the StringValueMap created and filled by the 'tuples' array provided.
Static method that creates a ConfigParams object based on the values that are stored in the 'value' object's properties.
configuration parameters in the form of an object with properties.
generated ConfigParams.
Retrieves AwsConnectionParams from multiple configuration parameters. The values are retrieves from "connection" and "credential" sections.
a list with configuration parameters
the generated AwsConnectionParams object.
Generated using TypeDoc
Contains connection parameters to authenticate against Amazon Web Services (AWS) and connect to specific AWS resource.
The class is able to compose and parse AWS resource ARNs.
Configuration parameters
In addition to standard parameters CredentialParams may contain any number of custom parameters
AwsConnectionResolver
Example
let connection = AwsConnectionParams.fromTuples( "region", "us-east-1", "access_id", "XXXXXXXXXXXXXXX", "secret_key", "XXXXXXXXXXXXXXX", "service", "s3", "bucket", "mybucket" ); let region = connection.getRegion(); // Result: "us-east-1" let accessId = connection.getAccessId(); // Result: "XXXXXXXXXXXXXXX" let secretKey = connection.getAccessKey(); // Result: "XXXXXXXXXXXXXXX" let pin = connection.getAsNullableString("bucket"); // Result: "mybucket"


Soren Bagley is a photographer based in Toledo, Ohio.
He co-founded the advertising agency Hawkesbury Media Group which produces commercial content for a variety of businesses in the Midwest. HMG has produced content for such high-profile clients as LifeTouch, Buckeye Cable Network, and the Republican National Committee.
Bagley is currently employed as an in-house photographer for Woodard Photographic where he works to capture a wide variety of candids for major events in his home city of Toledo, OH.


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- 这个东西有什么作用?
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- 送个礼物给你
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# Copyright (C) 2011-2012 Yaco Sistemas<EMAIL_ADDRESS>#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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Per xxx policy, this training is an annual requirement and colleagues are required to re-certify annually. Failure to complete this training will result in escalation.
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- click play next to the pipeline in the web ui
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Audit Trail Reporting allows you to examine updates to the setup and configuration of your account so you can understand what’s going on at a larger scale
. Audit records are available for up to one year
, and can be viewed in maximum durations
of thirty-one
days.
审计跟踪报告允许您检查帐户设置和配置的更新，以便您能够更大规模地了解发生的情况。审计记录的有效期最长为一年，最长为三十一天。
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Would you like to take a look and see how complex the problem is?
Meanwhile, please let me know if you have any clue to track down the issue.
We’ll be discussing on the Phase 2 approach, but I doubt there’ll be time for that till next week.
Thanks, nonetheless.
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Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License.
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I was able to clarify that this is to streamline our process of reporting false positives to the vendor. We currently give users the following instructions for filing false positives
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hint: Pulling without specifying how to reconcile divergent branches is hint: discouraged. You can squelch this message by running one of the following hint: commands sometime before your next pull: hint: hint: git config pull.rebase false # merge (the default strategy) hint: git config pull.rebase true # rebase hint: git config pull.ff only # fast-forward only hint: hint: You can replace “git config” with “git config –global” to set a default hint: preference for all repositories. You can also pass –rebase, –no-rebase, hint: or –ff-only on the command line to override the configured default per hint: invocation.
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prior to
other than
on a second turn
I haven’t had the need to clean my browser cache for ages 我很久没有清理浏览器缓存了
lift off
ALLOW CERTIFICATES FROM OTHER CAS TO WORK AT THEIR FULL POTENTIAL
NONETHELESS
NEVERTHELESS
prompt
cipher
applicant
is ready to issue certificates
leaf
Utterly marvellous
off topic
accomplish
comply with
made the format a bit distorted
The following commands inventory the usage for their respective framework
A tool that facilitates
continuous development for Kubernetes applications.
off-the-shelf
revision
confinement
confined
renewal
vzhong@vzhong:~/buildaudit/bdba-lb/helm$ sudo snap install helm [sudo] password for vzhong: error: This revision of snap “helm” was published using classic confinement and thus may perform arbitrary system changes outside of the security sandbox that snaps are usually confined to, which may put your system at risk. If you understand and want to proceed repeat the command including –classic.
mimic
This website is unrelated to Apple Inc All trademarks and copyrights on this website are property of their respective owners.
Reap finished successfully
Runbook
The triage of pcakges come in various forms, including:
retention
get missing 找不到
negotiation
Summit
could be that 可能是这样(不是’可以是这样’)
ongoing
IDK
Intermittent 断断续续的；间歇的
discrepancy n 差异;不符合;不一致
right-most
I’m applying to re-join xxx
Once you get a chance please keep us posted
Once you have inventoried your project
Can you elaborate more on it ?
I will priority this task
take some significant time
redact
in favor of
This is also not entirely trivial
by means of
initiatives contribute to revenue generation
vacation
cryptography
will try to sneak it into Q3
stay for a while
conservatively
advocate
Content-Disposition
revision
inadvertent
trim
Fostering Inclusive Communities
all XXX originated tickets
I got knocked out of Grafana admin account
I’ve managed to reproduce it 我设法复制了它
Well, my take would be something like this 嗯，我的看法是这样的
from scratch 从头开始
estimated time of arrival 估计的到达时间
vice versa 反之亦然
a corrupted installer
lack / leak
Package manipulation
Please be advised 请注意
How is your evaluation going?
I want to make sure you’re aware of our enterprise-tailored offerings, designed to help organizations such as yours get the most out of Data Center. 我想确保您了解我们为企业量身定制的产品，旨在帮助像您这样的组织充分利用数据中心
It turns out that her aunt and my cousin are one and the same. 原来她姑妈就是我表姐
circumstances
Recipe
A Concourse server can authenticate against GitLab to leverage
their permission model.
resorting to a complicated plugin system.
loop in
error-prone
incorporate
Please join me in welcoming xxx
rotate the password
revision
consecutively
homological
along with
rotation
take it into consideration
out of curiosity 出于好奇
across
timely updates
reserve & preserve
should fulfill the needs of Dashboard scan capability
Informative
identical twins 同卵双胞胎
substitution
retention
WARNING: the set_pipeline step is experimental and subject to change!
Dedicated new solution
based on the filtering criteria
preliminary activation
short for & stand for
stretch
By this change
proof of concept
Approximately equal : ~=
Proceed to www.iftty.com (unsafe)
recipient
There’re a lot of potential features around configuring notifications.
resides
A sample configuration pane which can handle the majority of needs is drawn below
send notification
In addition to
one-stop
are based off of e.g. All prices are based off of USD, so there always must be a USD price.
N/A 不适用;不可用;不知道;不适用的;不限
line up
imminently
out-of-the-box
thoughtful
overhead
On Linux systems, idle overhead is reduced most by using a Minikube cluster deployed on Docker. 在Linux系统上，使用部署在Docker上的Minikube集群可以最大程度地减少空闲开销
bring up
facilitates
leave out
yes, not ready yet, but will be till end of the week
preventive measure
maternity leave
I have not context
which is mostly done so that the control of where to place a scan is completely going through this new tool. 这主要是为了通过这个新工具完全控制扫描的位置
spanning over 跨越
consecutively
deposit
right after
Below neither works 下面两个都不起作用
如果都不存在
大于或等于
drawback
reputation
consensus
There are some caveats:
principle engineer
in case the owner shall be changed with svc account, we forbid that
consequent & subsequent
interpolation
partially
would be more indicative
obfuscation
xx elapsed time 3:15:02, completed on Fri Aug 12 21:08:33 2022
sustainability
laid out
recipe
adhere to
WARNING: The file has been changed since reading it!!!
In contrast
on-demand
specifically
we’ll try this out
I was riding in the car.
I ended up using something similar to the accepted answer, with minor modifications
Sundial: An instrument that indicates the time of day by means of
the position
ought to
with no guarantees whatsoever
are subject to
in some other fashion
to ensure the comprehensiveness of this doc
pre-installed
originates from
ahead of time
It’s better to avoid the use of LD_LIBRARY_PATH and use alternatives
configure: error: changes in the environment can compromise the build
Accreditation
foreseeable
distraction
maybe something wrong with the URL concatenation
campaign
passive
on-premise
All preliminary checks passed.
All installed files intact.
Please let me know if you can help me with the same.
Will the missing data from Aug 3-Aug 22 be replenished?
Any new PVCs have been failing to get provisioned but the existing ones will continue to work fine.
Going forward
comprehensive
submission
Uncheck 取消勾选
play around
reservation
Templates are stored on a dedicated CI server
slightly more involved
We would like to discuss our progress and get some clarification on the best way to proceed
I hope you are doing well!
Looks like a sane plan to me
Thank you for all the effort you put into this!
Keep the momentum going!
recap
Could you look able to help on it on priority ?
compliant & complain
Hi all, I am moving to accommodate holiday and schedule conflicts. Please let me know if this works.
could barely talk
comment out
By this change
in favor of
synonym
Miscellaneous
instantiate
Deterministic
Admission
courtesy service
peripheral tools
testbed
cheatsheet
as of today
obscurely
mitigations
Encountered errors while bringing up the project
TypeError: exceptions must derive from BaseException
Do you by any chance have any idea about this method? – by any chance
proceed with
moderator
chargeback
catastrophe 垮塌四周飞
Originating BU
where docker-compose.yaml resides
liability
free of charge
implications
Orchestration
accompanying slides
Looks like this targets xxx functionality
Issue is still ongoing
nonetheless
decommission
dealt, gave, existentialism, apotheosis, adequate, authentic, glittering, alive, supreme, moral, entertain, visceral,
error: This revision of snap "helm" was published using classic confinement and thus may perform
arbitrary system changes outside of the security sandbox that snaps are usually confined to,
which may put your system at risk.
If you understand and want to proceed repeat the command including --classic.
咬文嚼字
- flag & argument


All Classes
-
All Classes Interface Summary Class Summary Enum Summary Exception Summary Class Description AnalysisContext An analysis context is an aggregation of the APIs to check and configuration for the analysis.AnalysisContext.Builder AnalysisResult Holds the result of the analysis.AnalysisResult.ExtensionInstance<I> AnalysisResult.Extensions API Represents a set of archives that define an API.API.Builder ApiAnalyzer<E extends Element<E>> An API analyzer is the main interface one has to implement to support checking some kind of API.Archive A simple abstraction of a file or archive.Archive.Role The role of the archive in the API.Archive.Versioned Extension of the archive interface that can also provide the version of the archive.ArchiveAnalyzer<E extends Element<E>> The instances of implementations of this interface are produced by theApiAnalyzer
s to analyze the API archives and create an element tree that is then used for API comparison.BaseApiAnalyzer<E extends Element<E>> A convenience base class for API analyzers.BaseArchiveAnalyzer<F extends BaseElementForest<E>,E extends Element<E>> A convenience base class for API analyzers.BaseConfigurable A convenience base class for all configurable types in Revapi.BaseDifferenceAnalyzer<E extends Element<E>> A convenience base class for difference analyzers analyzers.BaseDifferenceTransform<E extends Element<E>> A convenience base class for difference transformsBaseEagerLoadingArchiveAnalyzer<F extends BaseElementForest<E>,E extends BaseElement<E>> A base class for archive analyzers that need to load all the elements in an archive eagerly.BaseElement<E extends Element<E>> A base class for API elements.BaseElementForest<E extends Element<E>> A convenience base class for element forest implementations.BaseElementMatcher A convenience base class for element matchers.BaseReporter A convenience base class for reporters.BaseTreeFilter<E extends Element<E>> A convenience base class for tree filters.BaseTreeFilterProvider A convenience base class for tree filter providers.CoIterator<E> Heavily inspired by the equivalently named class in Clirr 0.6.CollectingReporter This is a simple helper class that can be used by the users to collect all the reports from Revapi.CompatibilityType Enumerates possible compatibility types.Configurable A thing that can be configured from a JSON file.ConfigurationException Thrown if an invalid configuration is detected.ConfigurationValidator CorrespondenceComparatorDeducer<E extends Element<E>> A correspondence comparator deducer produces a comparator that is used to compare elements from 2 collections.Criticality Criticality represents the assigned importance of aDifference
.DFSFilteringIterator<E extends Element> Deprecated. This always had very limited utility andElement.stream(Class, boolean)
seems to be a more usable alternative.Difference Represents a single difference between an old and new API element.Difference.Builder Difference.InReportBuilder DifferenceAnalyzer<E extends Element<E>> An element analyzer is basically a visitor over matching elements of some language between the two API versions being compared.DifferenceSeverity Enumerates the possible severities of differences found during the API analysis.DifferenceTransform<E extends Element<?>> A difference transform may elect to transform certain kinds of differences into other kinds.DifferenceTransform.TraversalTracker<E extends Element<E>> Some more sophisticated transforms may need to track the traversal of the element forest.Element<E extends Element<E>> An element in a forest representation of given "language" under API inspection.ElementFilter Deprecated. useTreeFilterProvider
insteadElementForest<E extends Element<E>> A representation of some "unit" understood by an API analyzer.ElementForest.Visitor<E extends Element<E>> A visitor of the element forest.ElementMatcher An element matcher is a helper extension to other extensions that need to figure out if a certain element meets certain criteria.ElementMatcher.CompiledRecipe A "compiled" representation of a textual recipe.ElementPair A simple pair of elements.FileArchive A simple implementation of theArchive
interface providing a file as a Revapi archive.Filter<T> Deprecated. Filtering turned out to be not that usable and will be removed.FilterFinishResult A result of a finished filtering (seeTreeFilter.finish(Element)
.FilteringIterator<E> Deprecated. Filtering turned out to be more complex than this.FilterStartResult The result of the element filtering inTreeFilter
.IncludeExcludeTreeFilter<E extends Element<E>> A tree filter that uses a combination of an include and exclude filters to arrive at the filtering decisions.IncludeExcludeTreeFilter.IncludeExcludeResult IndependentTreeFilter<E extends Element<E>> A simple implementation of theTreeFilter
interface that simply repeats the result provided from theIndependentTreeFilter.start(Element)
method in itsIndependentTreeFilter.finish(Element)
method.InputStreamArchive A simple implementation of theArchive
interface that uses a "factory" supplier that is able to return a new input stream representing the archive every time it is called.JSONUtil A utility class for JSON files.OverridableIncludeExcludeTreeFilter<E extends Element<E>> This is similar to theIncludeExcludeTreeFilter
but adds support for the "include inside exclude", e.g.PipelineConfiguration This class represents the configuration of the Revapi analysis pipeline.PipelineConfiguration.Builder Reference<E extends Element<E>> Represents a reference between two elements determined in some way by the API analyzer.Reference.Type<E extends Element<E>> Represents the type of the relationship between two elements.Report Represents the differences between comparable elements from old and new API.Report.Builder ReportComparator Whileelements
of a single API analyzer are by definition mutually comparable, the same doesn't apply for reports.ReportComparator.Builder ReportComparator.Strategy The comparison strategy defines the way the elements of the same type are compared with each other.Reporter A reporter is the final stage during the API change analysis.Revapi The main entry point to the library.Revapi.Builder Deprecated. favor thePipelineConfiguration.Builder
ServiceTypeLoader<T> Because Revapi manages the lifecycle of instances of its extensions, it cannot unfortunately use theServiceLoader
directly (before Java 9 which enhances it to support the use case Revapi needs).SimpleConfigurable Deprecated. useBaseConfigurable
insteadSimpleDifferenceTransform<T extends Element<T>> Deprecated. useBaseDifferenceTransform
insteadSimpleElement Deprecated. useBaseElement
insteadSimpleElementFilter Deprecated. useIndependentTreeFilter
or some otherTreeFilter
insteadSimpleElementForest Deprecated. useBaseElementForest
insteadSimpleReporter Deprecated. useBaseReporter
insteadStats Quick and dirty execution time statistics collection class.Stats.Collector Ternary Ternary logic using the Kleene algebra.TransformationResult The result of an transformation.TransformationResult.Resolution The resolution of the difference transformationTreeFilter<E extends Element<E>> A tree filter is something that is called repeatedly by the caller as the caller walks a tree of elements in a depth first search manner.TreeFilterProvider Forest filter helps theArchiveAnalyzer
filter the resulting element forest while it is being created.ValidationResult Represents the results of the the configuration validation.ValidationResult.Error XmlToJson<Xml> A tool to convert some XML representation of the Revapi configuration toJsonNode
used by Revapi.XmlToJson.JSONPointer Deprecated. use Jackson's implZipArchiveAnalyzer<F extends BaseElementForest<E>,E extends BaseElement<E>> This is a base class for archive analyzers that need to search for files in a zipped archive.


In the course you will learn scala from scratch which covers classes, trait, function, higher order functions, pattern matching recursion, map, flatmap and much more.
In the course you will learn scala from scratch which covers classes, trait, function, higher order functions, pattern matching recursion, map, flatmap and much more.
This course covers Akka Actors, supervison, parent child creation, routing, data persistence, remoting, clustering, event sourcing, circuit breaker, Akka stream and Akka http
This course covers Akka Actors, supervison, parent child creation, routing, data persistence, remoting, clustering, event sourcing, circuit breaker, Akka stream and Akka http
In the course you will learn scala from scratch which covers classes, trait, function,higher order functions, pattern matching recursion, map, flatmap and much more.
In the course you will learn scala from scratch which covers classes, trait, function,higher order functions, pattern matching recursion, map, flatmap and much more.
In the course you will learn scala from scratch which covers classes, trait, function,higher order functions, pattern matching recursion, map, flatmap and much more.
In the course you will learn scala from scratch which covers classes, trait, function,higher order functions, pattern matching recursion, map, flatmap and much more.
In the course you will learn scala from scratch which covers classes, trait, function,higher order functions, pattern matching recursion, map, flatmap and much more.
In the course you will learn scala from scratch which covers classes, trait, function,higher order functions, pattern matching recursion, map, flatmap and much more.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, (None, image_height, image_width, image_channels), name='input_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return inputs_real, inputs_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse) as discriminator_scope:
alpha = 0.2
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
relu1 = tf.maximum(alpha * x1, x1)
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
flat = tf.reshape(relu3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
reuse = not is_train
alpha = 0.2
with tf.variable_scope('generator', reuse=reuse):
x1 = tf.layers.dense(z, 2*2*512)
x1 = tf.reshape(x1, (-1, 2, 2, 512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
sample_z = np.random.uniform(-1, 1, size=(50, z_dim))
samples, losses = [], []
steps, print_every = 0, 100
# Inputs
input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
# Loss
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
# Optimizers
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
steps += 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
batch_images = batch_images * 2
sess.run(d_opt, feed_dict={input_real:batch_images, input_z: batch_z, lr:learning_rate})
sess.run(g_opt, feed_dict={input_real:batch_images, input_z: batch_z})
sess.run(g_opt, feed_dict={input_real:batch_images, input_z: batch_z})
sess.run(g_opt, feed_dict={input_real:batch_images, input_z: batch_z})
if steps % 10 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
losses.append((train_loss_d, train_loss_g))
if steps % 100 == 0:
gen_samples = sess.run(
generator(input_z, data_shape[3], is_train=False),
feed_dict={input_z: sample_z})
samples.append(gen_samples)
show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.25
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.25
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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This table shows all the recommendations that you have done. You can search using any criteria
{% endblock %}


{% extends 'base.html' %} {% load static %} {% block title%} Pay {% endblock title%} {% block links %} {% endblock links %} {% block content %} {%if request.user == transcript_request.request_by%}
Has Paid: {{transcript_request.has_paid}}
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Before Change
// Now we inverting the masking operation, to go back to a full 3D
// representation
(x, y, z) = mean_img.shape
components = np.zeros((x, y, z, n_components))
components[mask] = components_masked
// Using a masked array is important to have transparency in the figures
After Change
// Now we inverting the masking operation, to go back to a full 3D
// representation
component_img = masker.inverse_transform(components_masked)
components = component_img.get_data()
// Using a masked array is important to have transparency in the figures
components = np.ma.masked_equal(components, 0, copy=False)


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