- Compatibility With The Correct Version
- Compatibility With The Correct Version Of Titanic Film
- Compatibility With The Correct Version Of The Bible
Find Your Incompatibilities Excel's Compatibility Checker runs automatically when you attempt to save a workbook in the 'Excel 97-2003 (.xls)' format. The Compatibility Checker window warns you that your document may lose some features or those features may be degraded if you save it in the older format. The window also displays a list of those features for you to review. For example, a list item may inform you that your document contains table references that Excel 97-2003 versions don’t support. Review Your Incompatibilities You have two options when the Compatibility Checker discovers issues: save your document in a newer Excel format, or click 'Continue' to save it in the older version and lose some functionality.
While the Compatibility Checker window is open, click the 'Find' link next to an item to jump to the location in the document that has the compatibility problem. To learn more about a compatibility issue or discover a possible workaround, click the 'Help' link next to an item. Excel Compatibility Mode After you save a workbook in an older format, Excel runs in Compatibility Mode.
In this mode, you can’t use any newer functionality that your newer Excel version provides. This ensures that you do not add enhanced features to the document that people using an older Excel version cannot view. Exit Compatibility Mode by clicking 'File,' selecting 'Info,' and then clicking 'Convert.' Excel converts the document into the current Excel format that matches the application version you’re using. For example, if you’re working in Excel 2013, it converts the document into that format to give you the ability to take advantage of Excel 2013’s latest features.
Compatibility With The Correct Version
Check Compatibility Manually While working with an Excel version higher than 2010, you can run the Compatibility Checker manually to create documents that are compatible with Excel 2010 and 2007. Launch the Compatibility Checker by clicking 'File,' selecting 'Info,' and then clicking 'Check for Issues.' The Compatibility Checker window opens and displays compatibility issues it finds.
Put a check mark in the 'Check Compatibility When Saving This Workbook' check box if you want Excel to check the document's compatibility in the future automatically.
This document is for users who need backwards compatibility across different versions of TensorFlow (either for code or data), and for developers who want to modify TensorFlow while preserving compatibility. Semantic Versioning 2.0 TensorFlow follows Semantic Versioning 2.0 for its public API.
Each release version of TensorFlow has the form MAJOR.MINOR.PATCH. For example, TensorFlow version 1.2.3 has MAJOR version 1, MINOR version 2, and PATCH version 3. Changes to each number have the following meaning:. MAJOR: Potentially backwards incompatible changes.
Code and data that worked with a previous major release will not necessarily work with the new release. However, in some cases existing TensorFlow graphs and checkpoints may be migratable to the newer release; see for details on data compatibility. MINOR: Backwards compatible features, speed improvements, etc. Code and data that worked with a previous minor release and which depends only on the public API will continue to work unchanged.
For details on what is and is not the public API, see. PATCH: Backwards compatible bug fixes. For example, release 1.0.0 introduced backwards incompatible changes from release 0.12.1. However, release 1.1.1 was backwards compatible with release 1.0.0. What is covered Only the public APIs of TensorFlow are backwards compatible across minor and patch versions. The public APIs consist of.
All the documented functions and classes in the tensorflow module and its submodules, except for. functions and classes in. functions and classes whose names start with (as these are private). functions, arguments, properties and classes whose name starts with experimental, or whose fully qualified name includes a module called experimental Note that the code in the examples/ and tools/ directories is not reachable through the tensorflow Python module and is thus not covered by the compatibility guarantee. If a symbol is available through the tensorflow Python module or its submodules, but is not documented, then it is not considered part of the public API.
Compatibility With The Correct Version Of Titanic Film
The following protocol buffer files:. What is not covered Some API functions are explicitly marked as 'experimental' and can change in backward incompatible ways between minor releases. These include:. Experimental APIs: The module and its submodules in Python and any functions in the C API or fields in protocol buffers that are explicitly commented as being experimental. In particular, any field in a protocol buffer which is called 'experimental' and all its fields and submessages can change at any time.
Other languages: TensorFlow APIs in languages other than Python and C, such as:. (exposed through header files in ).,. Details of composite ops: Many public functions in Python expand to several primitive ops in the graph, and these details will be part of any graphs saved to disk as GraphDefs. These details may change for minor releases.
In particular, regressions tests that check for exact matching between graphs are likely to break across minor releases, even though the behavior of the graph should be unchanged and existing checkpoints will still work. Floating point numerical details: The specific floating point values computed by ops may change at any time. Users should rely only on approximate accuracy and numerical stability, not on the specific bits computed. Changes to numerical formulas in minor and patch releases should result in comparable or improved accuracy, with the caveat that in machine learning improved accuracy of specific formulas may result in decreased accuracy for the overall system. Random numbers: The specific random numbers computed by the may change at any time. Users should rely only on approximately correct distributions and statistical strength, not the specific bits computed.
However, we will make changes to random bits rarely (or perhaps never) for patch releases. We will, of course, document all such changes. Version skew in distributed Tensorflow: Running two different versions of TensorFlow in a single cluster is unsupported. There are no guarantees about backwards compatibility of the wire protocol.
Bugs: We reserve the right to make backwards incompatible behavior (though not API) changes if the current implementation is clearly broken, that is, if it contradicts the documentation or if a well-known and well-defined intended behavior is not properly implemented due to a bug. For example, if an optimizer claims to implement a well-known optimization algorithm but does not match that algorithm due to a bug, then we will fix the optimizer. Our fix may break code relying on the wrong behavior for convergence. We will note such changes in the release notes.
Compatibility With The Correct Version Of The Bible
Error messages: We reserve the right to change the text of error messages. In addition, the type of an error may change unless the type is specified in the documentation. For example, a function documented to raise an InvalidArgument exception will continue to raise InvalidArgument, but the human-readable message contents can change. Compatibility of graphs and checkpoints You'll sometimes need to preserve graphs and checkpoints. Graphs describe the data flow of ops to be run during training and inference, and checkpoints contain the saved tensor values of variables in a graph. Many TensorFlow users save graphs and trained models to disk for later evaluation or additional training, but end up running their saved graphs or models on a later release.
In compliance with semver, any graph or checkpoint written out with one version of TensorFlow can be loaded and evaluated with a later version of TensorFlow with the same major release. However, we will endeavor to preserve backwards compatibility even across major releases when possible, so that the serialized files are usable over long periods of time. Graphs are serialized via the GraphDef protocol buffer. To facilitate (rare) backwards incompatible changes to graphs, each GraphDef has a version number separate from the TensorFlow version. For example, GraphDef version 17 deprecated the inv op in favor of reciprocal. The semantics are:. Each version of TensorFlow supports an interval of GraphDef versions.
This interval will be constant across patch releases, and will only grow across minor releases. Dropping support for a GraphDef version will only occur for a major release of TensorFlow.
Newly created graphs are assigned the latest GraphDef version number. If a given version of TensorFlow supports the GraphDef version of a graph, it will load and evaluate with the same behavior as the TensorFlow version used to generate it (except for floating point numerical details and random numbers), regardless of the major version of TensorFlow. In particular, all checkpoint files will be compatible. If the GraphDef upper bound is increased to X in a (minor) release, there will be at least six months before the lower bound is increased to X. For example (we're using hypothetical version numbers here):. TensorFlow 1.2 might support GraphDef versions 4 to 7.
TensorFlow 1.3 could add GraphDef version 8 and support versions 4 to 8. At least six months later, TensorFlow 2.0.0 could drop support for versions 4 to 7, leaving version 8 only. Finally, when support for a GraphDef version is dropped, we will attempt to provide tools for automatically converting graphs to a newer supported GraphDef version.
Graph and checkpoint compatibility when extending TensorFlow This section is relevant only when making incompatible changes to the GraphDef format, such as when adding ops, removing ops, or changing the functionality of existing ops. The previous section should suffice for most users.