KDD99

by Observerspy Python Version: Current License: No License

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

kandi X-RAY | KDD99 Summary

KDD99 is a Python library. KDD99 has no bugs, it has no vulnerabilities and it has low support. However KDD99 build file is not available. You can download it from GitHub.

KDD99

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Quality

Security

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Support

KDD99 has a low active ecosystem.

It has 5 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

KDD99 has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of KDD99 is current.

Quality

KDD99 has no bugs reported.

Security

KDD99 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

KDD99 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

KDD99 releases are not available. You will need to build from source code and install.

KDD99 has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed KDD99 and discovered the below as its top functions. This is intended to give you an instant insight into KDD99 implemented functionality, and help decide if they suit your requirements.

Build the graph
Adds a training op
Compute the loss op
Add placeholder tensors
Adds the accuracy op
Adds a prediction op
Adds the argmax op
Load kdd file
Counting function
Load the label3 file
Replace the kdd in_string with the given label
Calculate accuracy
Compute the classification accuracy
Creates a dictionary for the feed
Train the model on the given dataset
Train the model on a given batch
Load the label for the project
Load data from file
Train the model

Get all kandi verified functions for this library.

KDD99 Key Features

No Key Features are available at this moment for KDD99.

KDD99 Examples and Code Snippets

No Code Snippets are available at this moment for KDD99.

Community Discussions

Trending Discussions on KDD99

SupervisedDBNClassification' object has no attribute 'classes_'

Keras : Shuffling dataset while using LSTM

QUESTION

SupervisedDBNClassification' object has no attribute 'classes_'

Asked 2019-Oct-31 at 10:21

I am using supervisedDBN learning code which is deep learning architecture, i customize the below code and got the following error... I am working on KDD99 Network security dataset to analyze multiple attacks. but have the following error in code. how to fix it I don't know

...

ANSWER

Answered 2019-Oct-31 at 10:21

Unfortunately SupervisedDBNClassification doesn't have the attribute classes_ like most sklearn models. But you can make use of the attribute idx_to_label_map which will return a dictionary of index to label maps. So instead of classifier.classes_ you can use classifier.idx_to_label_map and to get only the labels as a list you can do the following list(classifier.idx_to_label_map.values()). So replace

Source https://stackoverflow.com/questions/58638406

QUESTION

Keras : Shuffling dataset while using LSTM

Asked 2019-Jul-15 at 14:29

Correct me if I am wrong but according to the official Keras documentation, by default, the fit function has the argument 'shuffle=True', hence it shuffles the whole training dataset on each epoch.

However, the point of using recurrent neural networks such as LSTM or GRU is to use the precise order of each data so that the state of the previous data influence the current one.

If we shuffle all the data, all the logical sequences are broken. Thus I don't understand why there are so much examples of LSTM where the argument is not set to False. What is the point of using RNN without sequences ?

Also, when I set the shuffle option to False, my LSTM model is less performant eventhought there are dependencies between the data: I use the KDD99 dataset where the connections are linked.

...

ANSWER

Answered 2019-Jul-15 at 14:29

If we shuffle all the data, all the logical sequences are broken.

No, the shuffling happens on the batches axis, not on the time axis. Usually, your data for an RNN has a shape like this: (batch_size, timesteps, features)

Usually, you give your network not only one sequence to learn from, but many sequences. Only the order in which these many sequences are being trained on gets shuffled. The sequences themselves stay intact. Shuffling is usually always a good idea because your network shall only learn the training examples themselves, not their order.

This being said, there are cases where you have indeed only one huge sequence to learn from. In that case you have the option to still divide your sequence into several batches. If this is the case, you are absolutely right with your concern that shuffling would have a huge negative impact, so don't do that in this case!

Note: RNNs have a stateful parameter that you can set to True. In that case the last state of the previous batch will be passed to the following one which effectively makes your RNN see all batches as one huge sequence. So, absolutely do this, if you have a huge sequence over multiple batches.

Source https://stackoverflow.com/questions/57041305

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

Vulnerabilities

No vulnerabilities reported

Install KDD99

You can download it from GitHub.
You can use KDD99 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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