WhitenBlackBox | Towards Reverse-Engineering Black

by coallaoh Python Version: Current License: MIT

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

kandi X-RAY | WhitenBlackBox Summary

WhitenBlackBox is a Python library typically used in Manufacturing, Utilities, Machinery, Process applications. WhitenBlackBox has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However WhitenBlackBox build file is not available. You can download it from GitHub.

Towards Reverse-Engineering Black-Box Neural Networks, ICLR'18

Support

Quality

Security

License

Reuse

Support

WhitenBlackBox has a low active ecosystem.

It has 38 star(s) with 11 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 634 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of WhitenBlackBox is current.

Quality

WhitenBlackBox has 0 bugs and 0 code smells.

Security

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

WhitenBlackBox code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

WhitenBlackBox is licensed under the MIT License. This license is Permissive.

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

Reuse

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

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

Installation instructions, examples and code snippets are available.

WhitenBlackBox saves you 640 person hours of effort in developing the same functionality from scratch.

It has 1486 lines of code, 82 functions and 14 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Get attribute from finding
Compute the loss for the given target
Compute the loss
Construct the cosine distribution
Set random seed
Configures the experiment
Return a dictionary mapping attributes to labels
Set the target
Select attribute from attr_dict
Removes attribute from attr_dict
Forward computation
Helper function to apply layer - softmax
Convert a structured x into a list
Save the model to file
Multiply Mnist data
Trains a control
Train the optimizer
Adds ensembles
Return a list of configs for each experiment
Prepare query output
Compute the number of parameters in the model
Evaluate features
Calculate statistics for an attribute
Prepare meta - training data
Run the test
Load conf model parameters

Get all kandi verified functions for this library.

WhitenBlackBox Key Features

No Key Features are available at this moment for WhitenBlackBox.

WhitenBlackBox Examples and Code Snippets

No Code Snippets are available at this moment for WhitenBlackBox.

Community Discussions

No Community Discussions are available at this moment for WhitenBlackBox.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install WhitenBlackBox

VERY IMPORTANT Clone this repository recursively.
Run the following commands to download and untar the necessary data (6.3MB).
MNIST-NET is a dataset of 11,282 diverse MNIST digit classifiers. The full pipeline for generating MNIST-NET is included in the repository (see below). The generation has taken about 40 GPU days with NVIDIA Tesla K80. Alternatively, the dataset can be downloaded from this link (19GB). Untar the downloaded file in the cache/ folder.