Search Results for "convert"

* Introduction: - This Module is a Library based Perl code. - The library provide: - Convert INFIX expressions to POSTFIX; - Convert POSTFIX expressions to INFIX and; - Perform POSTFIX context validations. - Context validation can be implemented in item selection routines or data context validation, when it is possible to identify data to be selected or ignored in some data analysis process. * NOTE: - Before any implementation, we recommend details in WIKI (https://sourceforge.net/p/csi-math-notation-postfixinfix/wiki/) or the CPAN Perl Modules, see: https://metacpan.org/pod/Math::Notation::PostfixInfix * Support: The Support Service is FREE. ...

Fastjson is a Java library that can be used to quickly convert Java Objects into their JSON representation or convert JSON strings to their equivalent Java object. Based on benchmarks it provides the best performance in both server side and Android client, and can work with arbitrary Java objects, even those pre-existing objects of which you do not have the source code. Fastjson provides a simple conversion method to turn Java objects to JSON and vice versa, with extensive support of Java Generics and arbitrarily complex objects.

Type-safe time calculations in Swift. Time is not just a bunch of Double conversion functions. The main advantage of it is that all time units are strongly-typed. Starting with Xcode 11, Time is officially available only via Swift Package Manager. Of course, you always have an option of just copying-and-pasting the code - Time is just two files, so feel free.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. ...

...For non-disposable tokens (tokens that can be reused), after obtaining the token, save it for repeated use, and use retryWhen() to automatically re-acquire the token when it expires, making the process of token acquisition completely transparent and simplified. Convert the returned data into a more convenient format and give it to the Observer.