label_smoothing | Corrupted labels and label smoothing | Data Labeling library

by Kyubyong Jupyter Notebook Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | label_smoothing Summary

label_smoothing is a Jupyter Notebook library typically used in Artificial Intelligence, Data Labeling applications. label_smoothing has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Corrupted labels and label smoothing

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Support

label_smoothing has a low active ecosystem.

It has 121 star(s) with 23 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

label_smoothing has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of label_smoothing is current.

Quality

label_smoothing has no bugs reported.

Security

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

License

label_smoothing is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

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label_smoothing Key Features

No Key Features are available at this moment for label_smoothing.

label_smoothing Examples and Code Snippets

Softmax cross entropy .

python

Lines of Code : 154

License : Non-SPDX (Apache License 2.0)

Copy

def softmax_cross_entropy(
    onehot_labels, logits, weights=1.0, label_smoothing=0, scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
  r"""Creates a cross-entropy loss using tf.nn.softmax_cross_

Sigmoid cross entropy .

python

Lines of Code : 62

License : Non-SPDX (Apache License 2.0)

Copy

def sigmoid_cross_entropy(
    multi_class_labels, logits, weights=1.0, label_smoothing=0, scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
  """Creates a cross-entropy loss using tf.nn.sigmoid_cr

Compute the categorical crossentropy .

python

Lines of Code : 44

License : Non-SPDX (Apache License 2.0)

Copy

def categorical_crossentropy(y_true,
                             y_pred,
                             from_logits=False,
                             label_smoothing=0,
                             axis=-1):
  """Computes the categorical crossentrop

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

...

ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

...

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

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

Vulnerabilities

No vulnerabilities reported

Install label_smoothing

You can download it from GitHub.

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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