autotune | Platform for reusable news tools | Frontend Framework library

by voxmedia JavaScript Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets(2)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | autotune Summary

autotune is a JavaScript library typically used in User Interface, Frontend Framework, React applications. autotune has no vulnerabilities, it has a Permissive License and it has low support. However autotune has 4 bugs. You can download it from GitHub.

Autotune is no longer in active development by Vox Product.

Support

Quality

Security

License

Reuse

Support

autotune has a low active ecosystem.

It has 412 star(s) with 35 fork(s). There are 144 watchers for this library.

It had no major release in the last 6 months.

There are 45 open issues and 339 have been closed. On average issues are closed in 214 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of autotune is current.

Quality

autotune has 4 bugs (0 blocker, 0 critical, 4 major, 0 minor) and 75 code smells.

Security

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

autotune code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

autotune is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Installation instructions are not available. Examples and code snippets are available.

autotune saves you 4985 person hours of effort in developing the same functionality from scratch.

It has 10493 lines of code, 301 functions and 174 files.

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

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autotune Key Features

No Key Features are available at this moment for autotune.

autotune Examples and Code Snippets

Create a snapshot of a dataset .

python

Lines of Code : 86

License : Non-SPDX (Apache License 2.0)

Copy

def snapshot(path, compression="AUTO", reader_func=None, shard_func=None):
  """API to persist the output of the input dataset.

  The snapshot API allows users to transparently persist the output of their
  preprocessing pipeline to disk, and materi

Load a dataset .

python

Lines of Code : 62

License : Non-SPDX (Apache License 2.0)

Copy

def load(path, element_spec=None, compression=None, reader_func=None):
  """Loads a previously saved dataset.

  Example usage:

  >>> import tempfile
  >>> path = os.path.join(tempfile.gettempdir(), "saved_data")
  >>> # S

Community Discussions

Trending Discussions on autotune

App crashing while working with class and not going to catch

How to extract mlr3 tuned graph step by step?

Get samples rom TF dataset

Tensorflow image crop from bounding box

How to monitor accuracy with CTC loss function and Datasets? (runnable code included)

How to keep the shape of tf.dataset after applying map?

How be sure on_epoch_end triggers

tf.image.decode_jpeg often taking forever to load file

Invalid argument: Dimension -972891 must be >= 0

Why does shuffling sequences of data in tf.keras.dataset affect the order of sequences differently between tf.fit and tf.predict?

QUESTION

App crashing while working with class and not going to catch

Asked 2021-Jun-10 at 11:17

In my AppDelegate.m, I am doing something like this

...

ANSWER

Answered 2021-Jun-10 at 07:22

You're using assign for reference/pointer types: @property retain, assign, copy, nonatomic in Objective-C

They should probably be declared copy, because this is a kind of value object, I think.

No exceptions were caught because no exceptions were thrown. Throwing/catching exceptions for control flow is not common in Objective-C

You don't need to write explicit setter functions for @properties

You should prefer to use BOOL type instead of Boolean, with values of YES/NO instead of true/false.

You should return instancetype not id from init, at least in reasonably modern Objective C

Consider making an initialiser that takes all the properties (initWithRoomName:clientID:) and make them read only once set

You don't need to declare -(id) init in your header since it gets that from NSObject

Source https://stackoverflow.com/questions/67916333

QUESTION

How to extract mlr3 tuned graph step by step?

Asked 2021-Jun-09 at 07:49

My codes in following

...

ANSWER

Answered 2021-Jun-08 at 09:22

To be able to fiddle with the models after resampling its best to call resample with store_models = TRUE

Using your example

Source https://stackoverflow.com/questions/67869401

QUESTION

Get samples rom TF dataset

Asked 2021-Jun-08 at 12:32

I have a TF dataset

...

ANSWER

Answered 2021-Jun-08 at 12:32

You could use tf.data.Dataset.shard.

Creates a Dataset that includes only 1/num_shards of this dataset.

shard is deterministic. The Dataset produced by A.shard(n, i) will contain all elements of A whose index mod n = i.

Source https://stackoverflow.com/questions/67886584

QUESTION

Tensorflow image crop from bounding box

Asked 2021-May-21 at 09:45

I am using the stanford dog dataset to do some image classification, loading with the following command.

...

ANSWER

Answered 2021-May-21 at 09:45

tf.data.Dataset default to graph mode for performance reasons. In graph mode, you can't do tuple unpacking. You should use tf.unstack instead.

Note that your dataset has a variable number of bounding boxes. You can either consider only one bounding box per image, or write a preprocess function that is agnostic to the number of bounding boxes.

Note also that the bounding boxes of your dataset are normalized between 0 and 1, while tf.image.crop_to_bounding_box expects its coordinates (or offset) to be ints, with the format (top, left, height, width)

I would suggest to skip the tuple unpacking and to use tf.image.crop_and_resize instead, that handle an arbitrary number of boxes per image.

Fixing your approach:

This method will handle just 1 bounding box per image.

Source https://stackoverflow.com/questions/67629889

QUESTION

How to monitor accuracy with CTC loss function and Datasets? (runnable code included)

Asked 2021-May-19 at 20:37

I've been trying to speed up training of my CRNN network for optical character recognition, but I can't get the accuracy metric working when using TFRecords and tf.data.Dataset pipelines. I previously used a Keras Sequence and had it working. Here is a complete runnable toy example showing my problem (tested with Tensorflow 2.4.1):

...

ANSWER

Answered 2021-May-17 at 09:45

There probably some issue with [accuracy] with tf.data, but I'm not super sure if this is the main cause in your case or if the issue still exits. If I try as follows, it runs anyway without Sequence (with tf.data).

Source https://stackoverflow.com/questions/67506106

QUESTION

How to keep the shape of tf.dataset after applying map?

Asked 2021-May-18 at 10:30

I'm creating a tf.dataset object containing 2 images as inputs and a mask as target. All of them are 3D. After applying a custom map, the shape of the object changes from to , , ), types: (tf.float32, tf.float32, tf.int32)>, and when I fit the data, my model throws an error because it only detects one input instead of 2. Here is what I'm doing:

...

ANSWER

Answered 2021-May-18 at 10:30

I have managed to solve this problem by "flattening" (eliminating the parenthesis) the return of the random_crop_flip, and applying another map on top of them, where I specified the shapes and returned my desired structure (x ,y), z:

Source https://stackoverflow.com/questions/67573468

QUESTION

How be sure on_epoch_end triggers

Asked 2021-May-14 at 07:24

I made a simple HDF5 reader class to avoid loading entire dataset in memory. I used sequence class to do so, but I'm not sure if on_epoch_end() function will trigger correctly.

I put a single print inside it, but it never appear! So I think there is something wrong in my code:

...

ANSWER

Answered 2021-May-14 at 07:24

Since seems to be a TF bug, I found a workaround to trigger on_epoch_end() of my generator.

Source https://stackoverflow.com/questions/67182474

QUESTION

tf.image.decode_jpeg often taking forever to load file

Asked 2021-May-12 at 05:20

The following code is part of my code for a tf graph to read images. When I use this code to iterate through the data, the program gets stuck in tf.io.read_file(path) after a few hundred images forever and doesn't do anything. More specifically, the code even can't be paused and I had to restart the session every time.

...

ANSWER

Answered 2021-May-12 at 05:20

As mentioned in the comment, try the following function to decode the image file as it can handle mixed extension file format (jpg, png etc), ref.

Source https://stackoverflow.com/questions/67226923

QUESTION

Invalid argument: Dimension -972891 must be >= 0

Asked 2021-Apr-29 at 20:06

I have created a data pipeline using tf.data for speech recognition using the following code snippets:

...

ANSWER

Answered 2021-Mar-16 at 17:47

I have found that the issue happened in the padding step, I mean

Source https://stackoverflow.com/questions/66637269

QUESTION

Why does shuffling sequences of data in tf.keras.dataset affect the order of sequences differently between tf.fit and tf.predict?

Asked 2021-Apr-27 at 05:27

I am training an LSTM deep learning model with time series sequences and labels.

I generate a tensorflow dataset "train_data" and "test_data"

...

ANSWER

Answered 2021-Mar-25 at 12:18

The dataset gets shuffled everytime you iterate through it. What you get after your list comprehension isn't in the same order as when you write predict. If you don't want that, pass:

Source https://stackoverflow.com/questions/66771775

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