multi_window | Flutter Multi Window Example on MacOS | iOS library

by rodydavis C++ Version: Current License: No License

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kandi X-RAY | multi_window Summary

multi_window is a C++ library typically used in Mobile, iOS applications. multi_window has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

A simple example on how to have multiple windows on MacOS with Flutter and communicate between them. This is more than just an instance of the app.

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multi_window has a low active ecosystem.

It has 79 star(s) with 4 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of multi_window is current.

Quality

multi_window has 0 bugs and 0 code smells.

Security

multi_window has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

multi_window code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

multi_window does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

multi_window releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

It has 61 lines of code, 1 functions and 7 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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multi_window Key Features

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multi_window Examples and Code Snippets

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Community Discussions

Trending Discussions on multi_window

`tf.keras.model.evaluate()` provides different results when fed the same data in different formats

QUESTION

`tf.keras.model.evaluate()` provides different results when fed the same data in different formats

Asked 2021-Feb-05 at 19:14

Backgound

I am looking at the following Tensorflow time series tutorial: https://www.tensorflow.org/tutorials/structured_data/time_series#single-shot_models

For the discussion here, I am going to consider the ‘multi_linear_model’ in the Multi-step models section.

I add the following line

multi_val_performance_new['Linear'] = multi_linear_model.evaluate(next(iter(multi_window.val))[0], next(iter(multi_window.val))[1])

after this line

multi_val_performance['Linear'] = multi_linear_model.evaluate(multi_window.val).

When I inspect the loss and mean absolute errors, they are different.

Question

Why - when I feed the original tf.Dataset to model.evaluate - do I get one set of loss and mean absolute error, but when I feed its components - (next(iter(tf.Dataset))[0], next(iter(tf.Dataset)[1]) to model.evaluate - do I get a different set of loss and mean absolute errors?

I have set shuffle = false in def make_dataset(self, data):.

...

ANSWER

Answered 2021-Feb-05 at 19:14

next(iter(ds)) will only return one batch/observation. Passing the entire dataset in model.evaluate will return the metrics for the entire dataset.

Source https://stackoverflow.com/questions/66069038

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install multi_window

Modify App Delegate
Enable client network access in Xcode
Add a new swift file inside /macos/Runner/ called WindowManagerPlugin.swift
Add window_manager.dart
Call the plugin. Here is an example:

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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